This work compares Autometrics with dual penalization techniques such as minimax concave penalty and smoothly clipped absolute deviation under asymmetric error distributions such as exponential, gamma, and Frechet with varying sample sizes as well as predictors. Comprehensive simulations, based on a wide variety of scenarios, reveal that the methods considered show improved performance for increased sample size. In the case of low multicollinearity, these methods show good performance in terms of potency, but in gauge, shrinkage methods collapse, and higher (...) gauge leads to overspecification of the models. High levels of multicollinearity adversely affect the performance of Autometrics. In contrast, shrinkage methods are robust in presence of high multicollinearity in terms of potency, but they tend to select a massive set of irrelevant variables. Moreover, we find that expanding the data mitigates the adverse impact of high multicollinearity on Autometrics rapidly and gradually corrects the gauge of shrinkage methods. For empirical application, we take the gold prices data spanning from 1981 to 2020. While comparing the forecasting performance of all selected methods, we divide the data into two parts: data over 1981–2010 are taken as training data, and those over 2011–2020 are used as testing data. All methods are trained for the training data and then are assessed for performance through the testing data. Based on a root-mean-square error and mean absolute error, Autometrics remain the best in capturing the gold prices trend and producing better forecasts than MCP and SCAD. (shrink)

The idea that approximate exactness is the most we can and should expect scientific theories to yield underlies the formation and application of the multi-valued logic of approximation discussed in this paper. In this logic, inexactness is controlled and minimized by means of uniquely designed deductions. We show how the notion of equality is handled within this logic and we apply it to certain principles and interpretations of quantum theory.

We present a taxonomy of errors in the scientific literature and an account of how the errors are distributed over the categories. We have developed the taxonomy by studying substantial errors in the scientific literature as described in retraction notices published in the journal Science over the past 35 years. We then examine how the sorts of errors that lead to retracted papers can be prevented and detected, considering the perspective of collaborating scientists, journal editors and referees, and readers of (...) the published articles. (shrink)

Beginning with the premise that the principal function of a criminal trial is to find out the truth about a crime, Larry Laudan examines the rules of evidence and procedure that would be appropriate if the discovery of the truth were, as higher courts routinely claim, the overriding aim of the criminal justice system. Laudan mounts a systematic critique of existing rules and procedures that are obstacles to that quest. He also examines issues of error distribution by offering (...) the first integrated analysis of the various mechanisms - the standard of proof, the benefit of the doubt, the presumption of innocence and the burden of proof - for implementing society's view about the relative importance of the errors that can occur in a trial. (shrink)

Software-intensive systems are ubiquitous in the industrialized world. The reliability of software has implications for how we understand scientific knowledge produced using software-intensive systems and for our understanding of the ethical and political status of technology. The reliability of a software system is largely determined by the distribution of errors and by the consequences of those errors in the usage of that system. We select a taxonomy of software error types from the literature on empirically observed software errors (...) and compare that taxonomy to Giuseppe Primiero’s Minds and Machines 24: 249–273, (2014) taxonomy of error in information systems. Because Primiero’s taxonomy is articulated in terms of a coherent, explicit model of computation and is more fine-grained than the empirical taxonomy we select, we might expect Primiero’s taxonomy to provide insights into how to reduce the frequency of software error better than the empirical taxonomy. Whether using one software error taxonomy can help to reduce the frequency of software errors better than another taxonomy is ultimately an empirical question. (shrink)

This book is a collection of original, interdisciplinary essays on the topic of medical error. Given the complexities of understanding, preventing, and responding to medical error in ethically responsible ways, the scope of the book is fairly broad. The contributors include top scholars and practitioners working in bioethics, communication, law, medicine and philosophy. Their contributions examine preventable causes of medical error, disproportionate impacts of errors on vulnerable populations, disclosure and apology after discovering medical errors, and ethical issues (...) arising in specific medical contexts, such as radiation oncology, psychopathy, and palliative care. They also offer practical recommendations for respecting autonomy, distributing burdens and benefits justly, and minimizing injury to patients and other stakeholders. Ethics and Error in Medicine will be of interest to a wide range of researchers, students, and practitioners in bioethics, philosophy, communication studies, law, and medicine who are interested in the ethics of medical error. (shrink)

Unsupervised grammar induction systems commonly judge potential constituents on the basis of their effects on the likelihood of the data. Linguistic justifications of constituency, on the other hand, rely on notions such as substitutability and varying external contexts. We describe two systems for distributional grammar induction which operate on such principles, using part-of-speech tags as the contextual features. The advantages and disadvantages of these systems are examined, including precision/recall trade-offs, error analysis, and extensibility.

Matrix models are widely used in biology to predict the temporal evolution of stage-structured populations. One issue related to matrix models that is often disregarded is the sampling variability. As the sample used to estimate the vital rates of the models are of finite size, a sampling error is attached to parameter estimation, which has in turn repercussions on all the predictions of the model. In this study, we address the question of building confidence bounds around the predictions of (...) matrix models due to sampling variability. We focus on a density-dependent Usher model, the maximum likelihood estimator of parameters, and the predicted stationary stage vector. The asymptotic distribution of the stationary stage vector is specified, assuming that the parameters of the model remain in a set of the parameter space where the model admits one unique equilibrium point. Tests for density-dependence are also incidentally provided. The model is applied to a tropical rain forest in French Guiana. (shrink)

In this paper, we mainly investigate the coordinated tracking control issues of multiple Euler–Lagrange systems considering constant communication delays and output constraints. Firstly, we devise a distributed observer to ensure that every agent can get the information of the virtual leader. In order to handle uncertain problems, the neural network technique is adopted to estimate the unknown dynamics. Then, we utilize an asymmetric barrier Lyapunov function in the control design to guarantee the output errors satisfy the time-varying output constraints. Two (...) distributed adaptive coordinated control schemes are proposed to guarantee that the followers can track the leader accurately. The first scheme makes the tracking errors between followers and leader be uniformly ultimately bounded, and the second scheme further improves the tracking accuracy. Finally, we utilize a group of manipulator networks simulation experiments to verify the validity of the proposed distributed control laws. (shrink)

In this article we present symmetric diffusion networks, a family of networks that instantiate the principles of continuous, stochastic, adaptive and interactive propagation of information. Using methods of Markovion diffusion theory, we formalize the activation dynamics of these networks and then show that they can be trained to reproduce entire multivariate probability distributions on their outputs using the contrastive Hebbion learning rule (CHL). We show that CHL performs gradient descent on an error function that captures differences between desired and (...) obtained continuous multivariate probability distributions. This allows the learning algorithm to go beyond expected values of output units and to approximate complete probability distributions on continuous multivariate activation spaces. We argue that learning continuous distributions is an important task underlying a variety of real‐life situations that were beyond the scope of previous connectionist networks. Deterministic networks, like back propagation, cannot learn this task because they are limited to learning average values of independent output units. Previous stochastic connectionist networks could learn probability distributions but they were limited to discrete variables. Simulations show that symmetric diffusion networks can be trained with the CHL rule to approximate discrete and continuous probability distributions of various types. (shrink)

This paper argues that epistemic errors rooted in group- or identity- based biases, especially those pertaining to disability, are undertheorized in the literature on medical error. After sketching dominant taxonomies of medical error, we turn to the field of social epistemology to understand the role that epistemic schemas play in contributing to medical errors that disproportionately affect patients from marginalized social groups. We examine the effects of this unequal distribution through a detailed case study of ableism. There (...) are four primary mechanisms through which the epistemic schema of ableism distorts communication between nondisabled physicians and disabled patients: testimonial injustice, epistemic overconfidence, epistemic erasure, and epistemic derailing. Measures against epistemic injustices in general and against schema-based medical errors in particular are ultimately issues of justice that must be better addressed at all levels of health care practice. (shrink)

The large scale, time varying, and diversification of physically coupled networked infrastructures such as power grid and transportation system lead to the complexity of their controller design, implementation, and expansion. For tackling these challenges, we suggest an online distributed reinforcement learning control algorithm with the one-layer neural network for each subsystem or called agents to adapt the variation of the networked infrastructures. Each controller includes a critic network and action network for approximating strategy utility function and desired control law, respectively. (...) For avoiding a large number of trials and improving the stability, the training of action network introduces supervised learning mechanisms into reduction of long-term cost. The stability of the control system with learning algorithm is analyzed; the upper bound of the tracking error and neural network weights are also estimated. The effectiveness of our proposed controller is illustrated in the simulation; the results indicate the stability under communication delay and disturbances as well. (shrink)

Ex ante predicted outcomes should be interpreted as counterfactuals (potential histories), with errors as the spread between outcomes. But error rates have error rates. We reapply measurements of uncertainty about the estimation errors of the estimation errors of an estimation treated as branching counterfactuals. Such recursions of epistemic uncertainty have markedly different distributial properties from conventional sampling error, and lead to fatter tails in the projections than in past realizations. Counterfactuals of error rates always lead to (...) fat tails, regardless of the probability distribution used. A mere .01% branching error rate about the STD (itself an error rate), and .01% branching error rate about that error rate, etc. (recursing all the way) results in explosive (and infinite) moments higher than 1. Missing any degree of regress leads to the underestimation of small probabilities and concave payoffs (a standard example of which is Fukushima). The paper states the conditions under which higher order rates of uncertainty (expressed in spreads of counterfactuals) alters the shapes the of final distribution and shows which a priori beliefs about conterfactuals are needed to accept the reliability of conventional probabilistic methods (thin tails or mildly fat tails). (shrink)

The distribution of the flap allophone [ɾ] of American English, along with the other allophones of /t/,[t h,t =, ʔ, t] has been accounted for in various formal frameworks by assuming a number of different abstract mechanisms and entities. The desirability or usefulness of these formalisms is not at issue in the present paper. Instead, a computationally explicit model of categorization is used (Skousen 1989, 1992) in order to account for the distribution of the allophones of /t/ without (...) recourse to such formalisms. The simulations that were carried out suggest that they are not needed because analogy to surface apparent variables such as phones and word boundaries is sufficient to predict allophony. In analogy, the particular allophone of /t/ (i.e. [ɾ, t h, t =, ʔ, t]) that appears in a given context is determined on the basis of similarity to stored exemplars in the mental lexicon. From an acquisitional standpoint, categorization by analogy to stored exemplars dispenses with the need for rule induction although it does suggest that speakers group functionally related sounds into mental categories, a process that is influenced to a great deal by orthography. Analogy also explains the stochastic nature of linguistic performance. In the present study, 3,719 tokens of the allophones of the phoneme /t/ were extracted from the TIMIT corpus and constitute the database from which analogs were chosen. The variables used included the three phones or boundaries on either side of /t/, and the stress of the syllables preceding and following /t/. The model proves quite successful in predicting the correct allophone, and the errors made are generally possible alternative pronunciations (e.g. moun[ʔ]ain, moun[t h ]ain). The success rate changes little when only small sub-samples of the database are incorporated. In addition, exemplar-modeling is found to be quite robust because even when a feature such as stress is eliminated, (a feature which is critical in most rule approaches), allophony is still highly predictable. (shrink)

A new lifetime distribution, called exponential doubly Poisson distribution, is proposed with decreasing, increasing, and upside-down bathtub-shaped hazard rates. One of the reasons for introducing the new distribution is that it can describe the failure time of a system connected in the form of a parallel-series structure. Some properties of the proposed distribution are addressed. Four methods of estimation for the involved parameters are considered based on progressively type II censored data. These methods are maximum likelihood, (...) moments, least squares, and weighted least squares estimations. Through an extensive numerical simulation, the performance of the estimation methods is compared based on the average of mean squared errors and the average of absolute relative biases of the estimates. Two real datasets are used to compare the proposed distribution with some other well-known distributions. The comparison indicates that the proposed distribution is better than the other distributions to match the data provided. (shrink)

For much of 2021, allocating the scarce supply of Covid‐19 vaccines was the world's most pressing bioethical challenge, and similar challenges may recur for novel therapies and future vaccines. In the United States, the Centers for Disease Control and Prevention's Advisory Committee on Immunization Practices (ACIP) identified three fundamental ethical principles to guide the process: maximize benefits, promote justice, and mitigate health inequities. We argue that critical components of the recommended protocol were internally inconsistent with these principles. Specifically, the ACIP (...) violated its principles by recommending overly broad health care worker priority in phase 1a, using being at least seventy‐five years of age as the only criterion to identify individuals at high risk of death from Covid‐19 during phase 1b, failing to recommend place‐based vaccine distribution, and implicitly endorsing first‐come, first‐served allocation. More rigorous empirical work and the development of a complete ethical framework that recognizes trade‐offs between principles may have prevented these mistakes and saved lives. (shrink)

Humans can pronounce a nonword. Some researchers have interpreted this behavior as requiring a sequential mechanism by which a grapheme-phoneme correspondence rule is applied to each grapheme in turn. However, several parallel-distributed processing models in English have simulated human nonword reading accuracy without a sequential mechanism. Interestingly, the Japanese psycholinguistic literature went partly in the same direction, but it has since concluded that a sequential parsing mechanism is required to reproduce human nonword reading accuracy. In this study, by manipulating the (...) list composition, we demonstrated that past psycholinguistic studies in Japanese have overestimated human nonword reading accuracy. When the more fairly reevaluated human performance was targeted, a newly implemented Japanese PDP model simulated the target accuracy as well as the error patterns. These findings suggest that PDP models are a more parsimonious way of explaining reading across various languages. (shrink)

Theories of language production propose that utterances are constructed by a mechanism that separates linguistic content from linguistic structure, Linguistic content is retrieved from the mental lexicon, and is then inserted into slots in linguistic structures or frames. Support for this kind of model at the phonological level comes from patterns of phonological speech errors. W present an alternative account of these patterns using a connectionist or parallel distributed proceesing (PDP) model that learns to produce sequences of phonological features. The (...) model's errors exhibit some of the properties of human speech errors, specifically, properties that have been attributed to the action of phonological rules, frames, or other structural generalizations. (shrink)

Distributional models of semantics learn word meanings from contextual co‐occurrence patterns across a large sample of natural language. Early models, such as LSA and HAL (Landauer & Dumais, 1997; Lund & Burgess, 1996), counted co‐occurrence events; later models, such as BEAGLE (Jones & Mewhort, 2007), replaced counting co‐occurrences with vector accumulation. All of these models learned from positive information only: Words that occur together within a context become related to each other. A recent class of distributional models, referred to as (...) neural embedding models, are based on a prediction process embedded in the functioning of a neural network: Such models predict words that should surround a target word in a given context (e.g., word2vec; Mikolov, Sutskever, Chen, Corrado, & Dean, 2013). An error signal derived from the prediction is used to update each word's representation via backpropagation. However, another key difference in predictive models is their use of negative information in addition to positive information to develop a semantic representation. The models use negative examples to predict words that should not surround a word in a given context. As before, an error signal derived from the prediction prompts an update of the word's representation, a procedure referred to as negative sampling. Standard uses of word2vec recommend a greater or equal ratio of negative to positive sampling. The use of negative information in developing a representation of semantic information is often thought to be intimately associated with word2vec's prediction process. We assess the role of negative information in developing a semantic representation and show that its power does not reflect the use of a prediction mechanism. Finally, we show how negative information can be efficiently integrated into classic count‐based semantic models using parameter‐free analytical transformations. (shrink)

This paper investigates confabulations in Korsakoff’s syndrome (KS) from the perspective of error-based teleosemantics, a framework that emphasizes the role of error detection in mental representation. I introduce a distributed and communicative model of memory, framing it as a network of information transfer encompassing both internal cognitive processes and external social interactions. This model, grounded in teleosemantics, allows for an understanding of how confabulations arise not solely from internal memory deficits, but also from disruptions in the feedback loops (...) between individuals and their social environments. By analyzing cases of provoked and spontaneous confabulations in KS patients, I demonstrate how this framework illuminates the crucial role of external consumers (e.g., caregivers) in either reinforcing or correcting erroneous memory reports. This approach, emphasizing error detection mechanisms, offers a new lens for examining the interplay between individual cognitive processes and social scaffolding in shaping memory and its pathologies. (shrink)

The origin of the genetic code has been attributed in part to an accidental assignment of codons to amino acids. Although several lines of evidence indicate the subsequent expansion and improvement of the genetic code, the hypothesis of Francis Crick concerning a frozen accident occurring at the early stage of genetic code evolution is still widely accepted. Considering Crick’s hypothesis, mathematical descriptions of hypothetical scenarios involving a huge number of possible coexisting random genetic codes could be very important to explain (...) the origin and evolution of a selected genetic code. This work aims to contribute in this regard, that is, it provides a theoretical framework in which statistical parameters of error functions are calculated. Given a genetic code and an amino acid property, the functional code robustness is estimated by means of a known error function. In this work, using analytical calculations, general expressions for the average and standard deviation of the error function distributions of completely random codes with standard stop codons were obtained. As a possible biological application of these results, any set of amino acids and any pure or mixed amino acid properties can be used in the calculations, such that, in case of having to select a set of amino acids to create a genetic code, possible advantages of natural selection of the genetic codes could be discussed. (shrink)

This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work.This work is in the public domain in (...) the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work.As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant. (shrink)

Bernoulli’s 1713 golden theorem is viewed retrospectively in the context of modern model-based frequentist inference that revolves around the concept of a prespecified statistical model Mθx\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal{M}}_{{{\varvec{\uptheta}}}} \left( {\mathbf{x}} \right)$$\end{document}, defining the inductive premises of inference. It is argued that several widely-accepted claims relating to the golden theorem and frequentist inference are either misleading or erroneous: (a) Bernoulli solved the problem of inference ‘from probability to frequency’, and thus (b) the golden theorem (...) cannot justify an approximate Confidence Interval (CI) for the unknown parameter θ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta$$\end{document}, (c) Bernoulli identified the probability PA\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$P\left( A \right)$$\end{document} with the relative frequency 1n∑k=1nxk\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{1}{n}\sum\nolimits_{k = 1}^{n} {x_{k} }$$\end{document} of event A as a result of conflating f(x0|θ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f({\mathbf{x}}_{0} |\theta )$$\end{document} with f(θ|x0),\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f(\theta |{\mathbf{x}}_{0} ),$$\end{document} where x0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{x}}_{0}$$\end{document} denotes the observed data, and (d) the same ‘swindle’ is currently perpetrated by the p value testers. In interrogating the claims (a)–(d), the paper raises several foundational issues that are particularly relevant for statistical induction as it relates to the current discussions on the replication crises and the trustworthiness of empirical evidence, arguing that: [i] The alleged Bernoulli swindle is grounded in the unwarranted claim θ^nx0≃θ∗,\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\theta }_{n} \left( {{\mathbf{x}}_{0} } \right) \simeq \theta^{*},$$\end{document} for a large enough n, where θ^nX\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hat{\theta }_{n} \left( {\mathbf{X}} \right)$$\end{document} is an optimal estimator of the true value θ∗\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta^{*}$$\end{document} of θ. [ii] Frequentist error probabilities are not conditional on hypotheses (H0 and H1) framed in terms of an unknown parameter θ since θ is neither a random variable nor an event. [iii] The direct versus inverse inference problem is a contrived and misplaced charge since neither conditional distribution f(x0|θ)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f({\mathbf{x}}_{0} |\theta )$$\end{document} and f(θ|x0)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$f(\theta |{\mathbf{x}}_{0} )$$\end{document} exists (formally or logically) in model-based (Mθx\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal{M}}_{{{\varvec{\uptheta}}}} \left( {\mathbf{x}} \right)$$\end{document}) frequentist inference. (shrink)

This article examines mixed ℋ -infinity and passivity synchronization of Markovian jumping neutral-type complex dynamical network models with randomly occurring coupling delays and actuator faults. The randomly occurring coupling delays are considered to design the complex dynamical networks in practice. These delays complied with certain Bernoulli distributed white noise sequences. The relevant data including limits of actuator faults, bounds of the nonlinear terms, and external disturbances are available for designing the controller structure. Novel Lyapunov–Krasovskii functional is constructed to verify the (...) stability of the error model and performance level. Jensen’s inequality and a new integral inequality are applied to derive the outcomes. Sufficient conditions for the synchronization error system are given in terms of linear matrix inequalities, which can be analyzed easily by utilizing general numerical programming. Numerical illustrations are given to exhibit the usefulness of the obtained results. (shrink)

In the mid-nineteenth century, healthcare delivery began transitioning from an individual, private payment model to a third-party payment model, dominated by the insurance industry. During the same time, productivity shifted from a transformational model, centered on the provider-patient relationship, to a transactional model, based on the distribution of services. The emergence of medical insurance and other third-party payers removed providers and patients from discussions about treatment plans, payment, and risk. This resulted in a weakening, if not fracturing, of the (...) provider-patient relationship. All healthcare providers enter their profession to care for people, yet what has most frequently been lost in the transformed relationship over the past century is context, communication, and trust—all elements of a relational ethic, or what ethicist and psychologist Carol Gilligan first described as, “the ethics of care.” This loss of relationality has led to a model of healthcare delivery that is fractured, isolated, and uncoordinated, with an epidemic of medical errors that by some estimates results in the death of approximately 400,000 individuals per year. Thus far, isolated foci on patient quality, outcomes, and safety have been feckless and unimpressive. However, new advanced payment models, such as value-based purchasing and patient-centered medical homes, have the potential to reduce medical error by addressing its root cause. In linking payment to factors such as context, communication, and trust, they bring relationality back into the healthcare system. This essay traces the historic devolution of the provider-patient relationship and the promise of new payment models to restore it. (shrink)

In order to deal with high-dimensional distributed data, this article develops a novel and communication-efficient approach for sparse and high-dimensional data with the penalized quantile regression. In each round, the proposed method only requires the master machine to deal with a sparse penalized quantile regression which could be realized fastly by proximal alternating direction method of multipliers algorithm and the other worker machines to compute the subgradient on local data. The advantage of the proximal ADMM algorithm is that it could (...) make every parameter of iteration to have closed formula even in high-dimensional case, which greatly improves the speed of calculation. As for the communication efficiency, the proposed method does not sacrifice any statistical accuracy and provably improves the estimation error obtained by centralized method, provided the penalty levels are chosen properly. Moreover, the asymptotic properties of the proposed estimation and the convergence of the algorithm are convincible. Especially, it presents extensive experiments on both the numerical simulations and the HIV drug resistance data analysis, which all confirm the significant efficiency of our proposed method in quantile regression for distributed data by comparative and empirical analysis. (shrink)

Between 1787, and the end of his life in 1832, Bentham turned his attention to the development and application of economic ideas and principles within the general structure of his legislative project. For seventeen years this interest was manifested through a number of books and pamphlets, most of which remained in manuscript form, that develop a distinctive approach to economic questions. Although Bentham was influenced by Adam Smith'sAn Inquiry into the Nature and Causes of the Wealth of Nations, he neither (...) adopted a Smithian vocabulary for addressing questions of economic principle and policy, nor did he accept many of the distinctive features of Smith's economic theory. One consequence of this was that Bentham played almost no part in the development of the emerging science of political economy in the early nineteenth century. The standard histories of economics all emphasize how little he contributed to the mainstream of late eighteenth and early nineteenth-century debate by concentrating attention on his utilitarianism and the psychology of hedonism on which it is premised. Others have argued that the calculating nature of his theory of practical reason reduced the whole legislative project to a crude attempt to apply economics to all aspects of social and political life. Put at its simplest this argument amounts to the erroneous claim that Bentham's science of legislation is reducible to the science of political economy. A different but equally dangerous error would be to argue that because Bentham's conception of the science of legislation comprehends all the basic forms of social relationships, there can be no science of political economy as there is no autonomous sphere of activity governed by the principles of economics. This approach is no doubt attractive from an historical point of view given that the major premise of this argument is true, and that many of Bentham's ‘economic’ arguments are couched in terms of his theory of legislation. Yet it fails to account for the undoubted importance of political economy within Bentham's writings, not just on finance, economic policy, colonies and preventive police, but also in other aspects of his utilitarian public policy such as prison reform, pauper management, and even constitutional reform. All of these works reflect a conception of political economy in its broadest terms. However, this conception of political economy differs in many respects from that of Bentham's contemporaries, and for this reason Bentham's distinctive approach to problems of economics and political economy has largely been misunderstood. (shrink)

A new three-parameter extension of the generalized-exponential distribution, which has various hazard rates that can be increasing, decreasing, bathtub, or inverted tub, known as the Marshall-Olkin generalized-exponential distribution has been considered. So, this article addresses the problem of estimating the unknown parameters and survival characteristics of the three-parameter MOGE lifetime distribution when the sample is obtained from progressive type-II censoring via maximum likelihood and Bayesian approaches. Making use of the s-normality of classical estimators, two types of approximate (...) confidence intervals are constructed via the observed Fisher information matrix. Using gamma conjugate priors, the Bayes estimators against the squared-error and linear-exponential loss functions are derived. As expected, the Bayes estimates are not explicitly expressed, thus the Markov chain Monte Carlo techniques are implemented to approximate the Bayes point estimates and to construct the associated highest posterior density credible intervals. The performance of proposed estimators is evaluated via some numerical comparisons and some specific recommendations are also made. We further discuss the issue of determining the optimum progressive censoring plan among different competing censoring plans using three optimality criteria. Finally, two real-life datasets are analyzed to demonstrate how the proposed methods can be used in real-life scenarios. (shrink)

Opponents of biotechnology ofteninvoke the Precautionary Principle to advancetheir cause, whereas biotech enthusiasts preferto appeal to ``sound science.'' Publicauthorities are still groping for a usefuldefinition. A crucial issue in this debate isthe distribution of the burden of proof amongthe parties favoring and opposing certaintechnological developments. Indeed, the debateon the significance and scope of thePrecautionary Principle can be fruitfullyre-framed as a debate on the proper division ofburdens of proof. In this article, we attemptto arrive at a more refined way of (...) thinkingabout this problem in order to escape from theexisting polarization of views between ``guiltyuntil proven innocent'' and ``innocent untilproven guilty.'' This way of thinking alsoenables a critical review of currentdemarcations between risk assessment and riskmanagement, or science and politics, and of themorally laden controversy on the relativeimportance of type-I and type-II errors instatistical testing. (shrink)

The spin-j Einstein–Podolsky–Rosen–Bohm experiment is examined in the context of how the quantum theoretic probability distributions for the spin measurement outcomes are to be coarse-grained in order to yield classical behavior in the $$j \rightarrow \infty $$ limit. A coarse-graining protocol is found that can be viewed as imperfection either in the detection process or in state preparation process, and is in both viewpoints minimal in the sense that it is no more than what is needed to wash out the (...) execess quantum correlations. In the first point of view the coarse-grained distribution can be written in terms of a Bell-type factorizable hidden variable model wherein the conditional distributions for the spin measurement outcome of each particle is not just nonnegative but actually attains the value zero for some choice of measurement axis. In the second point of view the coarse-grained distribution arises from a spin state whose Wigner function is not just nonnegative but actually attains the value zero for some spin orientations. That such a remarkable dual interpretation should be possible suggests that this type of complementary coarse graining is an intrinsic aspect of how classicality is obtained in the large j limit, but this conclusion remains speculative. (shrink)

We study a regression tree algorithm tailored to casualty insurance pure premium estimation problems. Casualty insurance premium is mainly determined by the expected amount that the insurance companies have to pay for the contract. Therefore, casualy insurance companies have to estimate the expected insurance amount on the basis of insurance risk factors. This problem is formulated as a regression problem, i.e. estimation of conditional mean E[Y|x], where Y is insurance amounts and x is risk factors. In this paper, we aim (...) to implement the regression problem in regression tree framework. The difficulty of the problem lies in the fact that the distribution of insurance amount P is highly skewed and exhibits a long-tail toward positive direction. Conventional least-square-error regression tree algorithm is notoriously unstable under such long-tailed error distribution. On the other hand, several types of robust regression trees, such as least-absolute-error regression tree, are neither appropriate in this situation because they yields significant bias to conditional mean E[Y|x]. In this paper, we propose a two-stage tree fitting algorithm. In the first stage, the algorithm constructs a quantile tree, a kind of robust regression tree, which is stable but biased to conditional mean E[Y|x]. In the second stage, the algorithm corrects the bias using least-square error regression tree. We discuss the theoretical background of the algorithm and empirically investigate the performances. We applied the proposed algorithm to a car insurance data set of 318,564 records provided from a north-american insurance company and obtained significantly better results than conventional regression tree algorithm. (shrink)

The evidence of your own eyes has often been regarded as unproblematic. But we know that people make mistaken observations. This can be looked on as unimportant if there issome class of statements that can serve as evidence for others, or if every statement in our corpus of knowledge is allowed to be no more than probable. Neither of these alternatives is plausible when it comes to machine or robotic observation. Then we must take the possibility of error seriously, (...) and we must be prepared to deal with error quantitatively. The problem of using internal evidence to arrive at error distributions is the main focus of the paper. (shrink)

Quantitative laws are more typical of science than are generalizations involving observational predicates, yet much discussion of scientific inference takes the confirmation of a universal generalization by its instances to be typical and paradigmatic. The important difference is that measurement necessarily involves error. It is argued that because of error laws can no more be refuted by observation than they can be verified by observation. Without much background knowledge, tests of a law mainly provide evidence for the (...) class='Hi'>distribution of errors of measurement of the quantities involved. With more background knowledge, the data may contribute either to our knowledge of the error distributions, or to the grounds we have for accepting or rejecting the law. With enough background knowledge, data may verify as well as refute laws. (shrink)

In order to improve the accuracy and reliability of wireless location in NLOS environment, a wireless location algorithm based on artificial neural network is proposed for NLOS positioning error caused by non-line-of-sight propagation, such as occlusion and signal reflection. The mapping relationship between TOA and TDOA measurement data and coordinates is established. The connection weights of neural network are estimated as the state variables of nonlinear dynamic system. The multilayer perceptron network is trained by the real-time neural network training (...) algorithm based on extended Kalman. Combined with the statistical characteristics of NLOS error, the state component NLOS bias estimation is modified to realize TDOA data reconstruction. Simulation and experimental data analysis show that the algorithm can effectively weaken the influence of NLOS error. The localization method does not depend on the specific NLOS error distribution, nor does it need LOS and NLOS recognition. It can significantly improve the mobile positioning accuracy. (shrink)

This study is to explore the optimization of the adaptive genetic algorithm in the backpropagation neural network, so as to expand the application of the BPNN model in nonlinear issues. Traffic flow prediction is undertaken as a research case to analyse the performance of the optimized BPNN. Firstly, the advantages and disadvantages of the BPNN and genetic algorithm are analyzed based on their working principles, and the AGA is improved and optimized. Secondly, the optimized AGA is applied to optimize the (...) standard BPNN, and the optimized algorithm is named as OAGA-BPNN. Finally, three different cases are proposed based on the actual scenario of traffic flow prediction to analyse the optimized algorithm on the matrix laboratory platform by simulation. The results show that the average error distribution of the GA-BPNN algorithm is about 1% with small fluctuation range, better calculation accuracy, and generalization performance in contrast to the BPNN. The average output error of the AGA-BPNN fluctuates around 0 and remains in a relatively stable range as a whole in contrast to that of GA-BPNN; the maximum fitness level keeps increasing during the evolution process but approaches the average value in later process, so the population diversity is hard to be guaranteed. The output error of the OAGA-BPNN fluctuates little compared with that of AGA-BPNN, and its maximum fitness continues to increase in the evolution process with guaranteed population diversity. In short, the OAGA-BPNN algorithm can achieve the best performance in terms of calculation accuracy, generalization performance, and population evolution. (shrink)

A longstanding paradox was first reported by David Miller in 1975 and highlighted by Karl Popper in 1979. Miller showed that the ranking of predictions from two theories, in terms of closeness to observation, appears to be reversed when the problem is transformed into a different mathematical space. He concluded that “… no false theory can … be closer to the truth than is another theory”. This flies in the face of normal scientific practice and is thus paradoxical; it is (...) named here the “Miller-Popper paradox”. This paper proposes a resolution of the paradox, through consideration of the inevitable errors and uncertainties in both observations and predictions. It is proved that, for linear transformations and Gaussian error distributions, the transformation between spaces creates no change in quantitative measures of “closeness-to-observation” when these measures are based in probability theory. The extension of this result to nonlinear transformations and to non-Gaussian error distributions is also discussed. These results demonstrate that concepts used in comparison of predictions with observations—concepts of “closeness”, “consistency”, “agreement”, “falsification”, etc.—all imply some knowledge of the uncertainty characteristics of both predictions and observations. (shrink)

This paper argues that the difference between contemporary software intensive scientific practice and more traditional non-software intensive varieties results from the characteristically high conditionality of software. We explain why the path complexity of programs with high conditionality imposes limits on standard error correction techniques and why this matters. While it is possible, in general, to characterize the error distribution in inquiry that does not involve high conditionality, we cannot characterize the error distribution in inquiry that (...) depends on software. Software intensive science presents distinctive error and uncertainty modalities that pose new challenges for the epistemology of science. (shrink)

In order for an “ideal” to make relevance, it is not mandatory, certainly, to be able to reach it. It is enough that it is possible to approach it, and even only to a certain extent. To be able to approach the ideal arbitrarily closely is not needed, even “theoretically.” To make sense of the “improvement” we can get in approaching an ideal, the measure of how close the ideal needs to be must be determined. In the thermometer's case, the (...) measure was determined by the bias in the error distribution at different points on the scale. An essentially internal measure was given, a measure related closely to the pertinent kind of measurement. (shrink)

Previous experiments have shown a serial-position effect (SPE) in paired-associate (PA) learning where the pairs contained stimuli pre- viously learned in serial order. The present experiment extended the number of pairs from 10 to 14. Pairs containing stimuli from terminal serial positions were learned with significantly fewer errors than pairs whose stimuli derived from central positions. The latter produced a dip in the PA error distribution suggesting the presence of sequential associations in SL between items occupying central positions.

Most legal evidence scholars agree that proof of guilt beyond a reasonable doubt requires the belief that the defendant probably committed the alleged acts. However, they also agree that this is not a sufficient condition, as this belief may be unreasonable. I focus on two popular proposals for additional conditions: (i) that the degree of belief should be robust and (ii) that it should be reasonable given the available evidence (should be an evidential probability). Both criteria face difficulties regarding their (...) meaning and utility. I propose an interpretation which overcomes these difficulties and which combines the two conditions into a single criterion on which both conditions are about the possibility of overlooked, exculpatory information. Proof of guilt beyond a reasonable doubt requires that we are sufficiently certain that we did not miss such information for the sake of achieving accurate beliefs and a just error distribution. How certain we can be that we did not miss anything relevant depends on our higher-order evidence. One important type of higher-order evidence is whether the hypotheses under consideration are sufficiently detailed. This point leads to a novel way of combining the Bayesian and explanation-based accounts of rational legal proof. (shrink)