A model with long-term survivors: negative binomial Birnbaum-Saunders

Abstract

We propose a cure rate survival model by assuming that the number of competing causes of the event of interest follows the negative binomial distribution and the time to the event of interest has the Birnbaum-Saunders distribution. Further, the new model includes as special cases some well-known cure rate models published recently. We consider a frequentist analysis for parameter estimation of the negative binomial Birnbaum-Saunders model with cure rate. Then, we derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. We illustrate the usefulness of the proposed model in the analysis of a real data set from the medical area.     

A Diagnostic Test for Normality Within the Power Exponential Family

This article develops the locally uniformly most powerful unbiased Lagrange multiplier test of normality of regression disturbances within the family of power exponential distributions. The small sample power properties of the test are compared in a Monte Carlo study with 6 well-known tests across 12 alternative nonnormal distributions. In addition, the finite sample power properties for nonnormal alternatives within the power exponential family are summarized by estimating response surfaces. The results suggest that the proposed text is computationally convenient and possesses relatively attractive power properties even against alternatives outside the power exponential family.     

A Telephone Intervention for Substance-Using Adult Male Perpetrators of Intimate Partner Violence

OBJECTIVE: To preliminarily evaluate telephone-delivered motivational enhancement therapy (MET) in motivating unadjudicated and nontreatment seeking intimate partner violence (IPV) perpetrators, who also use substances, to self-refer into treatment. METHOD: 124 adult men were recruited via a multimedia marketing campaign and were randomly assigned to the intervention (MET) or comparison group following a baseline assessment. Participants in the MET condition received a personalized feedback report on their IPV and substance-use behaviors, consequences, and social norms beliefs. RESULTS: Results supported the likely effectiveness of MET in short-term reduction of IPV behavior, increasing motivation for treatment seeking, and changing perceived norms for IPV and substance abuse (SA). CONCLUSIONS: Applications for brief MET interventions to facilitate voluntary treatment entry among substance-using IPV perpetrators are discussed.     

Sign Restrictions,Structural Vector Autoregressions,and Useful Prior Information

This paper makes the following original contributions to the literature. (i) We develop a simpler analytical characterization and numerical algorithm for Bayesian inference in structural vector autoregressions (VARs) that can be used for models that are overidentified, just‐identified, or underidentified. (ii) We analyze the asymptotic properties of Bayesian inference and show that in the underidentified case, the asymptotic posterior distribution of contemporaneous coefficients in an n‐variable VAR is confined to the set of values that orthogonalize the population variance–covariance matrix of ordinary least squares residuals, with the height of the posterior proportional to the height of the prior at any point within that set. For example, in a bivariate VAR for supply and demand identified solely by sign restrictions, if the population correlation between the VAR residuals is positive, then even if one has available an infinite sample of data, any inference about the demand elasticity is coming exclusively from the prior distribution. (iii) We provide analytical characterizations of the informative prior distributions for impulse‐response functions that are implicit in the traditional sign‐restriction approach to VARs, and we note, as a special case of result (ii), that the influence of these priors does not vanish asymptotically. (iv) We illustrate how Bayesian inference with informative priors can be both a strict generalization and an unambiguous improvement over frequentist inference in just‐identified models. (v) We propose that researchers need to explicitly acknowledge and defend the role of prior beliefs in influencing structural conclusions and we illustrate how this could be done using a simple model of the U.S. labor market.     

Strategic Design Responsiveness: An Empirical Analysis of US Retail Store Networks

This study uses a service operations management (SOM) strategy lens to investigate chain store retailers' strategic design responsiveness (SDR)—a term that captures the degree to which retailers dynamically coordinate investments in human and structural capital with the complexity of their service and product offerings. Labor force and physical capital are respectively used as proxies for investments in human capital and structural capital, whereas gross margins are proxies for product/service offering complexity. Consequently, SDR broadly reflects three salient complementary choices of SOM design strategy. We test the effects of “brick and mortar” chain store retailers' SDR on current and future firm performance using publically available panel data collected from Compustat and the University of Michigan American Customer Satisfaction Index databases for the period 1996–2011. We find that retailers that fail to keep pace with investments in both structural and human capital exhibit short‐term financial benefits, but have worse ongoing operational performance. These findings corroborate the importance of managers strategically maintaining the complementarity of design‐related choices for improving and maintaining business performance.     

Mini-batch learning of exponential family finite mixture models

Mini-batch algorithms have become increasingly popular due to the requirement for solving optimization problems, based on large-scale data sets. Using an existing online expectation–maximization (EM) algorithm framework, we demonstrate how mini-batch (MB) algorithms may be constructed, and propose a scheme for the stochastic stabilization of the constructed mini-batch algorithms. Theoretical results regarding the convergence of the mini-batch EM algorithms are presented. We then demonstrate how the mini-batch framework may be applied to conduct maximum likelihood (ML) estimation of mixtures of exponential family distributions, with emphasis on ML estimation for mixtures of normal distributions. Via a simulation study, we demonstrate that the mini-batch algorithm for mixtures of normal distributions can outperform the standard EM algorithm. Further evidence of the performance of the mini-batch framework is provided via an application to the famous MNIST data set.     

A Comparison of Some Methods for the Selection of a Common Eigenvector Model for the Covariance Matrices of Two Groups

Two new nonparametric common principal component model selection procedures based on bootstrap distributions of the vector correlations of all combinations of the eigenvectors from two groups are proposed. The performance of these methods is compared in a simulation study to the two parametric methods previously suggested by Flury in 1988, as well as modified versions of two nonparametric methods proposed by Klingenberg in 1996 and then by Klingenberg and McIntyre in 1998. The proposed bootstrap vector correlation distribution (BVD) method is shown to outperform all of the existing methods in most of the simulated situations considered.