Robust location estimation with missing data

In a missing data setting, we have a sample in which a vector of explanatory variables ${\bf x}_i$ is observed for every subject i, while scalar responses $y_i$ are missing by happenstance on some individuals. In this work we propose robust estimators of the distribution of the responses assuming missing at random (MAR) data, under a semiparametric regression model. Our approach allows the consistent estimation of any weakly continuous functional of the response's distribution. In particular, strongly consistent estimators of any continuous location functional, such as the median, L‐functionals and M‐functionals, are proposed. A robust fit for the regression model combined with the robust properties of the location functional gives rise to a robust recipe for estimating the location parameter. Robustness is quantified through the breakdown point of the proposed procedure. The asymptotic distribution of the location estimators is also derived. The proofs of the theorems are presented in Supplementary Material available online. The Canadian Journal of Statistics 41: 111–132; 2013 © 2012 Statistical Society of Canada     

The nation's drug problem is commercial recreational pharmacology

Overdose deaths, overprescribing of opioids, illicit production of fentanyl, a resurgence of methamphetamine and cocaine, and an epidemic of vaping‐related illnesses and substance use disorders cover the nation's headlines today, as they have for years. These problems are manifestations of commercial recreational pharmacology, the personally controlled use of psychoactive drugs that super‐stimulate brain reward for pleasure. While substance use and addiction have been known throughout history, the recent increasingly greater acceptance of commercial recreational pharmacology in the United States is exacting a toll on the public health of the nation. The most dramatic example is a decline in U.S. life expectancy, which fell three years in a row from 2016–2018 as a result of overdose deaths.     

Immigration,opportunity, and assimilation in a technology economy

Theory and Society - We examine access to institutions and opportunity for entrepreneurs in a rising tech economy. A significant proportion of entrepreneurs and CEOs of tech firms in the American...     

Erratum to: Mapping the Regional Divide in Europe: A Measure for Assessing Quality of Government in 206 European Regions

Social Indicators Research -     

A Portfolio Approach to Procurement Contracts

The purpose of this paper is to develop a general framework for supply contracts in which portfolios of contracts can be analyzed and optimized. We focus on a multi‐period environment with convex contract, spot market, and inventory holding costs. We specialize the model to the case of a portfolio consisting of option contracts. We characterize the optimal replenishment policy and show that it has a simple structure. Namely, the use of every different option contract and the spot market is dictated by a modified base‐stock policy. In addition, we derive conditions to determine when an option is relatively attractive compared to other options or the spot market. Finally, we present our computational study, where we report the sensitivity of the results to the parameters of the model. Our experiments indicate that portfolio contracts not only increase the manufacturer's expected profit, but can also reduce its financial risk.     

Path dependent societal transformation: Stratification in hybrid mixed economies

Theory and Society -     

Reducing Prejudice: Lessons From Social-Cognitive Factors Underlying Perceiver Differences in Prejudice

This article describes research findings on individual differences in stereotyping and shows how these findings have been and can be further applied to prejudice reduction efforts. A notable strength of this 'new' generation of individual difference work is its dynamic nature—that individual differences can be both stable and malleable. The first section of this article reviews work showing that both adults and children differ in social-cognitive factors related to stereotyping, namely the way theyprocess social information and their endorsement of social ideologies. The second section describes intervention strategies that target these factors. In the final section, limitations and future directions of basic and applied research on individual differences and stereotyping arediscussed.     

Swapping the Nested Fixed Point Algorithm: A Class of Estimators for Discrete Markov Decision Models

This paper proposes a new nested algorithm (NPL) for the estimation of a class of discrete Markov decision models and studies its statistical and computational properties. Our method is based on a representation of the solution of the dynamic programming problem in the space of conditional choice probabilities. When the NPL algorithm is initialized with consistent nonparametric estimates of conditional choice probabilities, successive iterations return a sequence of estimators of the structural parameters which we call K–stage policy iteration estimators. We show that the sequence includes as extreme cases a Hotz–Miller estimator (for K=1) and Rust's nested fixed point estimator (in the limit when K→∞). Furthermore, the asymptotic distribution of all the estimators in the sequence is the same and equal to that of the maximum likelihood estimator. We illustrate the performance of our method with several examples based on Rust's bus replacement model. Monte Carlo experiments reveal a trade–off between finite sample precision and computational cost in the sequence of policy iteration estimators.     

The Effect of Misclassification Costs on Neural Network Classifiers

The potential of neural networks for classification problems has been established by numerous successful applications reported in the literature. One of the major assumptions used in almost all studies is the equal cost consequence of misclassification. With this assumption, minimizing the total number of misclassification errors is the sole objective in developing a neural network classifier. Often this is done simply to ease model development and the selection of classification decision points. However, it is not appropriate for many real situations such as quality assurance, direct marketing, bankruptcy prediction, and medical diagnosis where misclassification costs have unequal consequences for different categories. In this paper, we investigate the issue of unequal misclassification costs in neural network classifiers. Through an application in thyroid disease diagnosis, we find that different cost considerations have significant effects on the classification performance and that appropriate use of cost information can aid in optimal decision making. A cross-validation technique is employed to alleviate the problem of bias in the training set and to examine the robustness of neural network classifiers with regard to sampling variations and cost differences.     

Using Neural Networks to Model the Behavior and Decisions of Gamblers,in Particular,Cyber-Gamblers

This article describes the use of neural networks (a type of artificial intelligence) and an empirical data sample of, inter alia, the amounts of bets laid and the winnings/losses made in successive games by a number of cyber-gamblers to longitudinally model gamblers’ behavior and decisions as to such bet amounts and the temporal trajectory of winnings/losses. The data was collected by videoing Texas Holdem gamblers at a cyber-gambling website. Six “persistent” gamblers were identified, totaling 675 games. The neural networks on average were able to predict bet amounts and cumulative winnings/losses in successive games accurately to three decimal places of the dollar. A more important conclusion is that the influence of a gambler’s skills, strategies, and personality on his/her successive bet amounts and cumulative winnings/losses is almost totally reflected by the pattern(s) of his/her winnings/losses in the few initial games and his/her gambling account balance. This partially invalidates gamblers’ illusions and fallacies that they can outperform others or even bankers. For government policy-makers, gambling industry operators, economists, sociologists, psychiatrists, and psychologists, this article provides models for gamblers’ behavior and decisions. It also explores and exemplifies the usefulness of neural networks and artificial intelligence at large in the research on gambling.