Analysis of quality-of-life adjusted failure time data in the presence of competing, possibly informative, censoring mechanisms

We derive estimators of the mean of a function of a quality-of-life adjusted failure time, in the presence of competing right censoring mechanisms. Our approach allows for the possibility that some or all of the competing censoring mechanisms are associated with the endpoint, even after adjustment for recorded prognostic factors, with the degree of residual association possibly different for distinct censoring processes. Our methods generalize from a single to many censoring processes and from ignorable to non-ignorable censoring processes.     

Assimilation effects beyond the labor market: time allocations of Mexican immigrants to the US

I use the American Time Use Survey from 2003 to 2012 to analyze, from a gender and marital status perspective, how the time Mexican immigrants devote to market work, household production, personal care, and leisure activities compare to the corresponding time allocations of US natives. Furthermore, I estimate the effect of duration of residence in the US on the immigrants overall patterns of time use. Time diary evidence indicates that, upon arrival to the US, Mexican immigrant men devote more time to market work and commuting than comparable non-Hispanic natives, regardless of their marital status. On the other hand, Mexican immigrant women devote the same amount of time to these activities as the non-Hispanic reference groups, irrespective of the marital status. In addition, the trend data show that immigrant men decrease their paid work with years since migration while immigrant women increase it. Estimates indicate that, for married immigrant men there is a tradeoff between market work and household work, whereas for single immigrant men the tradeoff is between market work and leisure. Finally, Mexican immigrant women relinquish mostly passive leisure and sleep time to meet demands of family and market work.     

Review of walking hazards for railroad workers

This is a review of walking tasks in the railroad environment, and the injuries that result from slips, trips, falls, or other acute or even non-traumatic exposures. The lack of federal regulations for railroad walkways has led several states to develop and enforce their own regulations. Support from the research literature for such regulations has come from biomechanical studies of the effects of walking on railroad ballast, which will be reviewed.     

He Said What? Guided Cognitive Reframing About the Co‐Resident Father/Stepfather–Adolescent Relationship

We studied young adolescents' seeking out support to understand conflict with their co‐resident fathers/stepfathers, and the cognitive and affective implications of such support‐seeking, phenomena we call guided cognitive reframing. Our sample included 392 adolescents (M_age = 12.5, 52.3% female) who were either of Mexican or European ancestry and lived with their biological mothers and either a stepfather or a biological father. More frequent reframing was associated with more adaptive cognitive explanations for father/stepfather behavior. Cognitions explained the link between seeking out and feelings about the father/stepfather and self. Feelings about the self were more strongly linked to depressive symptoms than cognitions. We discuss the implications for future research on social support, coping, guided cognitive reframing, and father–child relationships.     

On the Testability of Identification in Some Nonparametric Models With Endogeneity

This paper examines three distinct hypothesis testing problems that arise in the context of identification of some nonparametric models with endogeneity. The first hypothesis testing problem we study concerns testing necessary conditions for identification in some nonparametric models with endogeneity involving mean independence restrictions. These conditions are typically referred to as completeness conditions. The second and third hypothesis testing problems we examine concern testing for identification directly in some nonparametric models with endogeneity involving quantile independence restrictions. For each of these hypothesis testing problems, we provide conditions under which any test will have power no greater than size against any alternative. In this sense, we conclude that no nontrivial tests for these hypothesis testing problems exist.     

Inference in Nonparametric Instrumental Variables With Partial Identification

This paper develops methods for hypothesis testing in a nonparametric instrumental variables setting within a partial identification framework. We construct and derive the asymptotic distribution of a test statistic for the hypothesis that at least one element of the identified set satisfies a conjectured restriction. The same test statistic can be employed under identification, in which case the hypothesis is whether the true model satisfies the posited property. An almost sure consistent bootstrap procedure is provided for obtaining critical values. Possible applications include testing for semiparametric specifications as well as building confidence regions for certain functionals on the identified set. As an illustration we obtain confidence intervals for the level and slope of Brazilian fuel Engel curves. A Monte Carlo study examines finite sample performance.     

Developmental Outcomes of Internationally Adopted Children

This study followed 106 international adoptees over an 18-month period. Mothers completed the Ages and Stages Questionnaire (ASQ) at 6, 12, and 24 months post-adoption, assessing their children's development in multiple domains. Results revealed that the sample overall demonstrated linear improvement over time in most domains, but children with initially low scores remained significantly lower than others at the 18-month follow-up. ASQ scores were unrelated to age at adoption, but significant differences by birth country emerged. Across most domains, children from Eastern Europe had generally lower scores than children from other birth regions.     

A Simple Distribution-Free Algorithm for Generating Simulated High-Dimensional Correlated Data with an Autoregressive Structure

A distribution-free method to generate high-dimensional sequences of dependent variables with an autoregressive structure is presented. The quantile or fractile correlation (i.e., the moment correlation of the quantiles) is used as measure of dependence among a set of contiguous variables. The proposed algorithm breaks the sequence in small parts and avoids having to define one large correlation matrix for the entire high-dimensional sequence of variables. Simulations based on proteomics data are presented. Results suggest that negligible or no loss of fractile correlation occurs by splitting the generation of a sequence into small parts.