Bayesian estimation and case influence diagnostics for the zero-inflated negative binomial regression model

In recent years, there has been considerable interest in regression models based on zero-inflated distributions. These models are commonly encountered in many disciplines, such as medicine, public health, and environmental sciences, among others. The zero-inflated Poisson (ZIP) model has been typically considered for these types of problems. However, the ZIP model can fail if the non-zero counts are overdispersed in relation to the Poisson distribution, hence the zero-inflated negative binomial (ZINB) model may be more appropriate. In this paper, we present a Bayesian approach for fitting the ZINB regression model. This model considers that an observed zero may come from a point mass distribution at zero or from the negative binomial model. The likelihood function is utilized to compute not only some Bayesian model selection measures, but also to develop Bayesian case-deletion influence diagnostics based on q-divergence measures. The approach can be easily implemented using standard Bayesian software, such as WinBUGS. The performance of the proposed method is evaluated with a simulation study. Further, a real data set is analyzed, where we show that ZINB regression models seems to fit the data better than the Poisson counterpart.     

Generalized Estimation of the BLUP in Mixed-Effects Models: A Comparison with ML and REML

The Best Linear Unbiased Predictor (BLUP) in mixed models is a function of the variance components and they are estimated using maximum likelihood (ML) or restricted ML methods. Nonconvergence of BLUP would occur due to a drawback of the standard likelihood-based approaches. In such situations, ML and REML either do not provide any BLUPs or all become equal. To overcome this drawback, we provide a generalized estimate (GE) of BLUP that does not suffer from the problem of negative or zero variance components, and compare its performance against the ML and REML estimates of BLUP. Simulated and published data are used to compare BLUP.     

A Comparison of the Performance of Phase II Simple Linear Profile Control Charts when Parameters are Estimated

The standard deviation of the average run length (SDARL) is an important performance metric in studying the performance of control charts with estimated in-control parameters. Only a few studies in the literature, however, have considered this measure when evaluating control chart performance. The current study aims at comparing the in-control performance of three phase II simple linear profile monitoring approaches; namely, those of Kang and Albin (2000), Kim et al. (2003), and Mahmoud et al. (2010). The comparison is performed under the assumption of estimated parameters using the SDARL metric. In general, the simulation results of the current study show that the method of Kim et al. (2003) has better overall statistical performance than the competing methods in terms of SDARL values. Some of the recommended approaches based solely on the usual average run length properties can have poor SDARL performance.     

Advancing an ethical framework for long-term care

This article represents an effort to formulate an ethical framework for long-term care with the explicit purpose of providing a catalyst to promote further discourse and expand consideration of what an ethic of long-term care might entail. Grounding the discussion, an introduction to traditional ethical philosophy is presented, focusing mainly on the fundamentals of deontological and teleological ethical theories. Attention then shifts to a review of the more frequently cited principles found in the long-term care ethics literature, followed by a critique of the current reliance upon principlism to resolve ethical dilemmas in long-term care. In response to this criticism, an agent-driven ethical framework stressing dignity and respect for personhood, drawn from the philosophy of Immanuel Kant, is advanced.     

Disability, social exclusion and the consequential experience of justiciable problems

In this article, we examine the situation of disabled people in England and Wales with regard to one specific aspect of social exclusion—experience of justiciable problems, and the potential effects such problems can have on their lives. Having defined 'disability', we examine how this fits within the wider dialogue on social exclusion issues. By analysing the results of the Legal Services Research Centre's (LSRC) periodic survey of justiciable problems, we find that disabled respondents were not only more likely to experience a problem, they also experienced more problems. Increased likelihood of a problem was observed in the majority of problem categories and particularly those relating to issues of social exclusion, such as housing and welfare benefits. This propensity to experience multiple problems can lead to a negative impact on the lives of long-term ill and disabled people.