Universal versus contextual effects on TQM: a triangulation study using neural networks

The objective of this study is to extend previous research on total quality management (TQM)-context-performance relationships and ‘fit’ using multiple methods. We combine artificial neural networks (ANNs) with structural equation modelling (SEM) to analyse several hypotheses and propositions. This is the first study in this area of research that utilises ANNs and a triangulation technique in the presence of several contextual factors. The SEM analyses suggest that company size and industry type may have contingency effects on some of the TQM practices and/or TQM-performance relationships. However, the ANN models have shown that these two contingency factors do not moderate TQM outcomes, implying that all organisations can benefit from TQM regardless of size and type. As well, these models show that formal TQM implementation and/or ISO certifications do not add any predictive power to the ANN models except in one case: TQM implementation and/or ISO certification added to organisational effectiveness and customer results to predict financial and market (F&M) results. The results further indicate that even though implementing TQM alone has a bigger impact on F&M results than obtaining ISO certification alone, combining the two will have an even greater impact on these results. Joint implementation leads to greater improvements in organisational effectiveness, which, in turn, has a positive effect on customer results and consequently F&M results. This is a unique finding within the context of moderator effects on TQM-performance relationships.     

Bayesian Analysis in Regression Models Using Pseudo-Likelihoods

This article deals with the issue of using a suitable pseudo-likelihood, instead of an integrated likelihood, when performing Bayesian inference about a scalar parameter of interest in the presence of nuisance parameters. The proposed approach has the advantages of avoiding the elicitation on the nuisance parameters and the computation of multidimensional integrals. Moreover, it is particularly useful when it is difficult, or even impractical, to write the full likelihood function.

We focus on Bayesian inference about a scalar regression coefficient in various regression models. First, in the context of non-normal regression-scale models, we give a theroetical result showing that there is no loss of information about the parameter of interest when using a posterior distribution derived from a pseudo-likelihood instead of the correct posterior distribution. Second, we present non trivial applications with high-dimensional, or even infinite-dimensional, nuisance parameters in the context of nonlinear normal heteroscedastic regression models, and of models for binary outcomes and count data, accounting also for possibile overdispersion. In all these situtations, we show that non Bayesian methods for eliminating nuisance parameters can be usefully incorporated into a one-parameter Bayesian analysis.     

一类n阶非线性微分方程多点边值问题解的唯一性

本文利用Leray-Schauder度理论建立了n阶非线性微分方程y(n)=f(x,y,y′,…y(n-1)),0     

Efficient Estimation of Parameters of the Negative Binomial Distribution

In this article we investigate a class of moment-based estimators, called power method estimators, which can be almost as efficient as maximum likelihood estimators and achieve a lower asymptotic variance than the standard zero term method and method of moments estimators. We investigate different methods of implementing the power method in practice and examine the robustness and efficiency of the power method estimators.     

A class of mean residual life regression models with censored survival data

When describing a failure time distribution, the mean residual life is sometimes preferred to the survival or hazard rate. Regression analysis making use of the mean residual life function has recently drawn a great deal of attention. In this paper, a class of mean residual life regression models are proposed for censored data, and estimation procedures and a goodness-of-fit test are developed. Both asymptotic and finite sample properties of the proposed estimators are established, and the proposed methods are applied to a cancer data set from a clinic trial.     

分析对流-扩散方程显格式稳定性的一种方法

给出了分析二维对流-扩散方程FTCS显格式（时间向前,空间中心的差分格式）稳定条件的一种方法.这种方法可用于分析一维到高维对流-扩散方程差分格式的稳定性.     

The Role of Criminal Social Identity in the Relationship between Criminal Friends and Criminal Thinking Style within a Sample of Recidivistic Prisoners

Previous research suggests a direct relationship between criminal friends and criminal thinking style; however, social identity theory proposes that identity mediates the impact of social group members on development of thinking styles. This research project is the first attempt to empirically test the mediating role of criminal social identity in the development of criminal thinking styles within a recidivistic prison sample (N = 312). The structural equation model of criminal thinking style presented and tested in this study supports the central predictions of social identity theory, with findings demonstrating an indirect effect of antisocial friend associations on criminal thinking through in-group affect and in-group ties with criminal in-group members that reflect two of three dimensions of the measure of criminal social identity applied in the current study. Further implications in relation to theory and previous studies are discussed.     

Neighborhood Conditions,Parenting, and Adolescent Functioning

ABSTRACT

This study examined youth self-reported data from a 4-year longitudinal study of 5th and 8th grade youth and their parents. Relationships between neighborhood conditions, parenting, and youth antisocial behavior were tested using structural equation modeling. The findings of this study appear to suggest there are two social forces that affect youth antisocial behavior: parenting and neighborhood conditions. Higher levels of parental acceptance and monitoring are directly and significantly associated with lower levels of youth antisocial behavior, as are higher levels of community social integration and lower levels of youth loneliness. Overall, it was found that some measures of neighborhood conditions and some measures of parenting were relatively equally predictive of youth antisocial behavior. Other measures of neighborhood conditions were predictive of parenting, but not strongly enough to also impact adolescent functioning.     

A semigroup approach to the strong ergodic theorem of the multistate stable population process

In this paper we first formulate the dynamics of multistate stable population processes as a partial differential equation. Next, we rewrite this equation as an abstract differential equation in a Banach space, and solve it by using the theory of strongly continuous semigroups of bounded linear operators. Subsequently, we investigate the asymptotic behavior of this semigroup to show the strong ergodic theorem which states that there exists a stable distribution independent of the initial distribution. Finally, we introduce the dual problem in order to obtain a logical definition for the reproductive value and we discuss its applications.     

Hypothesis Testing of Hazard Ratio Parameters in Marginal Models for Multivariate Failure Time Data

Marginal hazard models for multivariate failure time data have been studied extensively in recent literature. However, standard hypothesis test statistics based on the likelihood method are not exactly appropriate for this kind of model. In this paper, extensions of the three commonly used likelihood hypothesis test statistics are discussed. Generalized Wald, generalized score and generalized likelihood ratio tests for hazard ratio parameters in a marginal hazard model for multivariate failure time data are proposed and their asymptotic distributions examined. The finite sample properties of these statistics are studied through simulations. The proposed method is applied to data from Busselton Population Health Surveys.