The demography of disability and the effects of immigrant history: Older Asians in the United States

Using data from the 2000 U.S. census, we compare the older Asian population with U.S.-born, non-Hispanic whites with respect to three indicators of disability. Insofar as any Asian "advantage" in health vis-a-vis whites exists among the population aged 65 and over, our evidence suggests that it occurs primarily among the U.S.-born segments of this population. We also investigate how differences in disability levels among Asian immigrant groups are influenced by country of birth and by the combined effects of duration of residence in the United States and life cycle stage at entry. These results highlight the diversity of the older Asian population with respect to the ways in which immigration and origin history are linked to disability outcomes. We conclude that in later life, immigrant status confers few disability advantages among the Asian population in the United States.     

Changes in HIV/AIDS knowledge and testing behavior in Africa: how much and for whom?

Demographic and Health Survey data from nine African countries make it clear that HIV/AIDS prevention knowledge has been increasing. Still, in many cases, fewer than half of adult respondents can identify specific prevention behaviors. Knowledge is lowest in rural areas and among women. HIV testing generally remains rare but is highly variable across countries, likely reflecting differences in the supply of testing services. In most cases, schooling and wealth impacts on prevention knowledge have either been stable or have increased; hence, in the majority of contexts, initial disparities in knowledge by education and wealth levels have persisted or widened.

Factors influencing Soay sheep survival

We present a survival analysis of Soay sheep mark recapture and recovery data. Unlike previous conditional analyses, it is not necessary to assume equality of recovery and recapture probabilities; instead these are estimated by maximum likelihood. Male and female sheep are treated separately, with the higher numbers and survival probabilities of the females resulting in a more complex model than that used for the males. In both cases, however, age and time aspects need to be included and there is a strong indication of a reduction in survival for sheep aged 7 years or more. Time variation in survival is related to the size of the population and selected weather variables, by using logistic regression. The size of the population significantly affects the survival probabilities of male and female lambs, and of female sheep aged 7 or more years. March rainfall and a measure of the North Atlantic oscillation are found to influence survival significantly for all age groups considered, for both males and females. Either of these weather variables can be used in a model. Several phenotypic and genotypic individual covariates are also fitted. The only covariate which is found to influence survival significantly is the type of horn of first-year female sheep. There is a substantial variation in the recovery probabilities over time, reflecting in part the increased effort when a population crash was expected. The goodness of fit of the model is checked by using graphical procedures.     

Choice Functions: Rationality re-Examined

On analyzing the problem that arises whenever the set of maximal elements is large, and a selection is then required (see Peris & Subiza 1998), we realize that logical ways of selecting among maximals violate the classical notion and axioms of rationality. We arrive at the same conclusion if we analyze solutions to the problem of choosing from a tournament (where maximal elements do not necessarily exist). So, in our opinion the notion of rationality must be discussed, not only in the traditional sense of external conditions (Sen 1993), but in terms of the internal information provided by the binary relation.     

Correlation in a Bayesian framework

The authors consider the correlation between two arbitrary functions of the data and a parameter when the parameter is regarded as a random variable with given prior distribution. They show how to compute such a correlation and use closed form expressions to assess the dependence between parameters and various classical or robust estimators thereof, as well as between p‐values and posterior probabilities of the null hypothesis in the one‐sided testing problem. Other applications involve the Dirichlet process and stationary Gaussian processes. Using this approach, the authors also derive a general nonparametric upper bound on Bayes risks.     

A Weighted Least Squares Approach to Levene's Test of Homogeneity of Variance

In 1960 Levene suggested a potentially robust test of homogeneity of variance based on an ordinary least squares analysis of variance of the absolute values of mean-based residuals. Levene's test has since been shown to have inflated levels of significance when based on the F-distribution, and tests a hypothesis other than homogeneity of variance when treatments are unequally replicated, but the incorrect formulation is now standard output in several statistical packages. This paper develops a weighted least squares analysis of variance of the absolute values of both mean-based and median-based residuals. It shows how to adjust the residuals so that tests using the F -statistic focus on homogeneity of variance for both balanced and unbalanced designs. It shows how to modify the F -statistics currently produced by statistical packages so that the distribution of the resultant test statistic is closer to an F-distribution than is currently the case. The weighted least squares approach also produces component mean squares that are unbiased irrespective of which variable is used in Levene's test. To complete this aspect of the investigation the paper derives exact second-order moments of the component sums of squares used in the calculation of the mean-based test statistic. It shows that, for large samples, both ordinary and weighted least squares test statistics are equivalent; however they are over-dispersed compared to an F variable.     

The exponentiated power exponential regression model with different regression structures: application in nursing data

We define the exponentiated power exponential distribution and propose a regression model with different systematic structures based on the new distribution. We show that the new regression model can be applied to dispersion data since it represents a parametric family of models that includes as sub-models some widely-known regression models. It then can be used more effectively in the analysis of real data. We use maximum likelihood estimation and derive the appropriate matrices for assessing local influence on the parameter estimates under different perturbation schemes. Some global-influence measurements are also investigated and simulation studies are performed to evaluate the accuracy of the estimates. We provide an application of the regression model with four systematic structures to nursing activities score data in the Unit of the Medical Clinic of University of São Paulo (USP) Hospital.     

Learning from a lot: Empirical Bayes for high‐dimensional model‐based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, stored in public repositories. We review applications of a variety of empirical Bayes methods to several well‐known model‐based prediction methods, including penalized regression, linear discriminant analysis, and Bayesian models with sparse or dense priors. We discuss “formal” empirical Bayes methods that maximize the marginal likelihood but also more informal approaches based on other data summaries. We contrast empirical Bayes to cross‐validation and full Bayes and discuss hybrid approaches. To study the relation between the quality of an empirical Bayes estimator and p, the number of variables, we consider a simple empirical Bayes estimator in a linear model setting. We argue that empirical Bayes is particularly useful when the prior contains multiple parameters, which model a priori information on variables termed “co‐data”. In particular, we present two novel examples that allow for co‐data: first, a Bayesian spike‐and‐slab setting that facilitates inclusion of multiple co‐data sources and types and, second, a hybrid empirical Bayes–full Bayes ridge regression approach for estimation of the posterior predictive interval.     

Infant Behavior and Competence Following Prenatal Exposure to a Natural Disaster: The QF2011 Queensland Flood Study

This study utilized a natural disaster to investigate the effects of prenatal maternal stress (PNMS) arising from exposure to a severe flood on maternally reported infant social–emotional and behavioral outcomes at 16 months, along with potential moderation by infant sex and gestational timing of flood exposure. Women pregnant during the Queensland floods in January 2011 completed measures of flood‐related objective hardship and posttraumatic stress (PTS). At 16 months postpartum, mothers completed measures describing depressive symptoms and infant social–emotional and behavioral problems (n = 123) and competence (n = 125). Greater maternal PTS symptoms were associated with reduced infant competence. A sex difference in infant behavioral problems emerged at higher levels of maternal objective hardship and PTS; boys had significantly more behavioral problems than girls. Additionally, greater PTS was associated with more behavioral problems in boys; however, this effect was attenuated by adjustment for maternal depressive symptoms. No main effects or interactions with gestational timing were found. Findings highlight specificity in the relationships between PNMS components and infant outcomes and demonstrate that the effects of PNMS exposure on behavior may be evident as early as infancy. Implications for the support of families exposed to a natural disaster during pregnancy are discussed.     

A preliminary look at relationships between married gay men and lesbians and their parents-in-law: Five case studies

With marriage between lesbians and between gays now legal in the United States, it is important to gain an understanding of how in-law relationships function for this population. Five case studies, part of a larger study of in-law relationships, are presented that help further knowledge of the potential obstacles that some couples may face in relation to their in-laws. Common experiences include relationships improving over time; struggling with acceptance; feeling closer to parents-in-law because other adult children or adult children-in-law have fallen out of favor; recognizing the significant role that the child-in-law’s spouse plays in the relationship between the child-in-law and the parent-in-law; understanding the role that the larger community plays; considering both parents-in-law as separate voices in the in-law dyad; and experiencing feelings of ambivalence that arise between family members. Practice implications for clinicians are included as are implications for future research.