Computing Critical Values of Exact Tests by Incorporating Monte Carlo Simulations Combined with Statistical Tables

Various exact tests for statistical inference are available for powerful and accurate decision rules provided that corresponding critical values are tabulated or evaluated via Monte Carlo methods. This article introduces a novel hybrid method for computing p‐values of exact tests by combining Monte Carlo simulations and statistical tables generated a priori. To use the data from Monte Carlo generations and tabulated critical values jointly, we employ kernel density estimation within Bayesian‐type procedures. The p‐values are linked to the posterior means of quantiles. In this framework, we present relevant information from the Monte Carlo experiments via likelihood‐type functions, whereas tabulated critical values are used to reflect prior distributions. The local maximum likelihood technique is employed to compute functional forms of prior distributions from statistical tables. Empirical likelihood functions are proposed to replace parametric likelihood functions within the structure of the posterior mean calculations to provide a Bayesian‐type procedure with a distribution‐free set of assumptions. We derive the asymptotic properties of the proposed nonparametric posterior means of quantiles process. Using the theoretical propositions, we calculate the minimum number of needed Monte Carlo resamples for desired level of accuracy on the basis of distances between actual data characteristics (e.g. sample sizes) and characteristics of data used to present corresponding critical values in a table. The proposed approach makes practical applications of exact tests simple and rapid. Implementations of the proposed technique are easily carried out via the recently developed STATA and R statistical packages.     

An extension of a change-point problem

We consider a specific classification problem in the context of change-point detection. We present generalized classical maximum likelihood tests for homogeneity of the observed sample in a simple form which avoids the complex direct estimation of unknown parameters. This paper proposes a martingale approach to transformation of test statistics. For sequential and retrospective testing problems, we propose the adapted Shiryayev–Roberts statistics in order to obtain simple tests with asymptotic power one. An important application of the developed methods is in the analysis of exposure's measurements subject to limits of detection in occupational medicine.     

A bayesian treatpent of nonresponse when sampling from a dichotomous population

In the situation where significant nonresponse is present in a sample survey, a prior distribution is developed which can reflect vague prior beliefs about the differences in the attitudes of respondents and nonrespondents. A posterior interval estimate is derived for the population proportion of interest.     

Energy statistics: A class of statistics based on distances

Energy distance is a statistical distance between the distributions of random vectors, which characterizes equality of distributions. The name energy derives from Newton's gravitational potential energy, and there is an elegant relation to the notion of potential energy between statistical observations. Energy statistics are functions of distances between statistical observations in metric spaces. Thus even if the observations are complex objects, like functions, one can use their real valued nonnegative distances for inference. Theory and application of energy statistics are discussed and illustrated. Finally, we explore the notion of potential and kinetic energy of goodness-of-fit.     

Bayesian experimental design for a toxicokinetic-toxicodynamic model

The aim of this study is to apply the Bayesian method of identifying optimal experimental designs to a toxicokinetic-toxicodynamic model that describes the response of aquatic organisms to time dependent concentrations of toxicants. As for experimental designs, we restrict ourselves to pulses and constant concentrations. A design of an experiment is called optimal within this set of designs if it maximizes the expected gain of knowledge about the parameters. Focus is on parameters that are associated with the auxiliary damage variable of the model that can only be inferred indirectly from survival time series data. Gain of knowledge through an experiment is quantified both with the ratio of posterior to prior variances of individual parameters and with the entropy of the posterior distribution relative to the prior on the whole parameter space. The numerical methods developed to calculate expected gain of knowledge are expected to be useful beyond this case study, in particular for multinomially distributed data such as survival time series data.     

Thick populism: democracy-enhancing popular participation

ABSTRACT

This article posits that some forms of popular participation offer important resources for democratic renewal. It develops a conceptual distinction between thin and thick varieties of populism. Thin populist movements mobilize popular support to replace elite leaders by undermining or corroding the deliberative and inclusionary principles of representative government. In contrast, thick populist movements seek to modify or alter the practices and conventions of representative government by offering democracy-enhancing and trust-building organizational forms and political practices. This distinction between thin and thick populism helps identify a swath of normative and practical common-ground occupied by populists and deliberative democratic reformers and innovators, who have also held deeply critical views of representative democracy. The article discusses four contemporary examples of democratic innovation (broadly understood) to illustrate how thick populism can take root in organizations, institutions, campaigns, and in the efforts of everyday citizens. Consideration is given to the lessons that contemporary forms of thick populism offer for advocates of participatory and deliberative democratic innovation.     

A Robust Approach for Mitigating Risks in Cyber Supply Chains

In recent years, there have been growing concerns regarding risks in federal information technology (IT) supply chains in the United States that protect cyber infrastructure. A critical need faced by decisionmakers is to prioritize investment in security mitigations to maximally reduce risks in IT supply chains. We extend existing stochastic expected budgeted maximum multiple coverage models that identify “good” solutions on average that may be unacceptable in certain circumstances. We propose three alternative models that consider different robustness methods that hedge against worst‐case risks, including models that maximize the worst‐case coverage, minimize the worst‐case regret, and maximize the average coverage in the $(1?\alpha )$ worst cases (conditional value at risk). We illustrate the solutions to the robust methods with a case study and discuss the insights their solutions provide into mitigation selection compared to an expected‐value maximizer. Our study provides valuable tools and insights for decisionmakers with different risk attitudes to manage cybersecurity risks under uncertainty.     

Training Environments, Work Attitudes, and Turnover Intention

A recent and extensive review of research concluded that worker age–viewed both chronologically and in terms of career stage–was a decisive determinant of work attitudes including turnover intention, i.e., intention to remain in an occupation or to leave it. This paper reports a study of work attitudes among 732 American, British, and Spanish students undergoing training as merchant marine engineering officers. All were of the same age and were at the same point in their careers. Wide differences were shown in terms of attitudes toward the occupation including turnover intention. The single most important variable was the national context in which training was taking place: The Americans exhibited the most negative work attitudes and the higher turnover intention; the Spanish had the most positive attitudes and the lowest turnover intention; the British tended to fall in the middle. Social class origin had some, though irregular, effects. The national differences are explained in terms of the training environments existing in the schools as well as structural features surrounding the merchant marine industry and the organization of training for the occupation in each country. Hence, though age may be a decisive element in work attitudes, this study suggests that the social organization within which age factors are located may determine the scope and forms of its effects.     

A Note on the Minimax Solution for the Two-Stage Group Testing Problem

Group testing is an active area of current research and has important applications in medicine, biotechnology, genetics, and product testing. There have been recent advances in design and estimation, but the simple Dorfman procedure introduced by R. Dorfman in 1943 Dorfman, R. (1943), The Detection of Defective Members of Large Populations, The Annals of Mathematical Statistics, 14, 436–440.[Crossref] , [Google Scholar] is widely used in practice. In many practical situations, the exact value of the probability p of being affected is unknown. We present both minimax and Bayesian solutions for the group size problem when p is unknown. For unbounded p, we show that the minimax solution for group size is 8, while using a Bayesian strategy with Jeffreys’ prior results in a group size of 13. We also present solutions when p is bounded from above. For the practitioner, we propose strong justification for using a group size of between 8 and 13 when a constraint on p is not incorporated and provide useable code for computing the minimax group size under a constrained p.