Bayesian methods for generalized linear models with covariates missing at random

The authors propose methods for Bayesian inference for generalized linear models with missing covariate data. They specify a parametric distribution for the covariates that is written as a sequence of one‐dimensional conditional distributions. They propose an informative class of joint prior distributions for the regression coefficients and the parameters arising from the covariate distributions. They examine the properties of the proposed prior and resulting posterior distributions. They also present a Bayesian criterion for comparing various models, and a calibration is derived for it. A detailed simulation is conducted and two real data sets are examined to demonstrate the methodology.     

A nonparametric repeated significance test with adaptive target sample size

In this article a general result is derived that, along with a functional central limit theorem for a sequence of statistics, can be employed in developing a nonparametric repeated significance test with adaptive target sample size. This method is used in deriving a repeated significance test with adaptive target sample size for the shift model. The repeated significance test is based on a functional central limit theorem for a sequence of partial sums of truncated observations. Based on numerical results presented in this article one can conclude that this nonparametric sequential test performs quite well.     

Using the EM-algorithm for survival data with incomplete categorical covariates

Incomplete covariate data is a common occurrence in many studies in which the outcome is survival time. With generalized linear models, when the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM by the method of weights proposed in Ibrahim (1990). In this article, we extend the EM by the method of weights to survival outcomes whose distributions may not fall in the class of generalized linear models. This method requires the estimation of the parameters of the distribution of the covariates. We present a clinical trials example with five covariates, four of which have some missing values.     

History of sexual abuse and parental alcohol misuse: Risk,outcomes and protective factors in adolescents

This study examined the school performance, suicidal involvement, disordered eating behaviors, pregnancy risk and chemical use of teenagers with a history of sexual abuse or parental alcohol misuse and found that they had higher rates of these adverse outcomes than among a comparison group of teenagers without such background risk factors. Adolescents with dual-risk background reported higher levels of suicide risk, disordered eating, sexual behaviors and chemical abuse than teenagers with only one background risk factor. Among index group members, protective factors against these adverse outcomes included a high degree of religiosity and the ability to discuss problems with family or friends. Risk factors that increased the likelihood of adverse outcomes included depression, perceived substance use in school and worries about family financial security.The first author was supported in the preparation of this article by a grant from the Lois and Samuel Silberman Fund of New York.     

Do bootstrap confidence procedures behave well uniformly in P?

An important statistical problem is to construct a confidence set for some functional T(P) of some unknown probability distribution P. Typically, this involves approximating the sampling distribution J_n(P) of some pivot based on a sample of size n from P. A bootstrap procedure is to estimate J_n(P) by J_n(&Pcirc;_n), where P?_n is the empirical measure based on a sample of size n from P. Typically, one has that J_n(P) and J_n(P?_n) are close in an appropriate sense. Two questions are addressed in this note. Are J_n(P) and J_n(P?_n) uniformly close as P varies as well? If so, do confidence statements about T(P) possess a corresponding uniformity property? In the case T(P) = P, the answer to the first questions is yes; the answer to the second is no. However, bootstrap confidence statements about T(P) can be made uniform over a restricted, though large, class of P. Similar results apply to other functional T(P).     

Canonical correlation analysis of residential and vocational outcomes following psychiatric rehabilitation

This analysis uses canonical correlation techniques to explore client outcomes following psychosocial rehabilitation services in two rehabilitation areas: vocational and residential. At issue are the ways in which these two outcomes are affected by a series of eight client characteristics and service delivery features. The overarching question is the effectiveness of the program in establishing independence vocationally and residentially, and whether the same set of factors influences these two outcomes for persons with mental illness. Subjects were 212 ex-clients of a psychosocial rehabilitation agency who were working six months after leaving the program. The analysis derived two canonical functions both of which were significant. Canonical loadings and cross loadings indicated that client level of functioning and program tenure loaded most strongly with residential status on the first canonical variate while education loaded most strongly with salary on the second. These results support the notions that different predictors account for the variance associated with different outcomes following psychosocial rehabilitation. Discussion of the relevance of strong predictor variables highlights ways they can impact program design. Finally, the usefulness of canonical correlation in longitudinal program evaluation research is explored.     

Bayesian phylogenetic inference from animal mitochondrial genome arrangements

Summary. The determination of evolutionary relationships is a fundamental problem in evolutionary biology. Genome arrangement data are potentially more informative than deoxyribonucleic acid sequence data for inferring evolutionary relationships between distantly related taxa. We describe a Bayesian framework for phylogenetic inference from mitochondrial genome arrangement data using Markov chain Monte Carlo methods. We apply the method to assess evolutionary relationships between eight animal phyla.     

Traditional vs. Modern Food Systems? Insights from Vegetable Supply Chains to Ho Chi Minh City (Vietnam)

This article describes the development of vegetable marketing in Ho Chi Minh City (Vietnam), where modern distribution outlets are competing fiercely with traditional traders for wholesale and retail customers. Data from interviews with supply chain stakeholders and a survey of vegetable wholesalers have been used to compare the performance of modern and traditional chains, and the findings reveal the chains as segmented in their product focus, the modern sector focusing exclusively on quality. Modern marketing channels are generally more efficient than traditional ones but still account for only around 2% of vegetable distribution. The article argues that policy‐makers should not promote the ‘modernisation’ of food systems at the expense of traditional channels which meet important consumer needs.