A robust QTL mapping procedure

In quantitative trait linkage studies using experimental crosses, the conventional normal location-shift model or other parameterizations may be unnecessarily restrictive. We generalize the mapping problem to a genuine nonparametric setup and provide a robust estimation procedure for the situation where the underlying phenotype distributions are completely unspecified. Classical Wilcoxon–Mann–Whitney statistics are employed for point and interval estimation of QTL positions and effects.     

Maternal Lifetime Trauma Exposure,Prenatal Cortisol,and Infant Negative Affectivity

Little research has examined the impact of maternal lifetime trauma exposure on infant temperament. We examined associations between maternal trauma history and infant negative affectivity and modification by prenatal cortisol exposure in a sociodemographically diverse sample of mother–infant dyads. During pregnancy, mothers completed measures of lifetime trauma exposure and current stressors. Third‐trimester cortisol output was assessed from maternal hair. When infants were 6 months old, mothers completed the Infant Behavior Questionnaire‐Revised. In analyses that controlled for infant sex and maternal age, education, race/ethnicity, and stress during pregnancy, greater maternal trauma exposure was associated with increased infant distress to limitations and sadness. Higher and lower prenatal cortisol exposure modified the magnitude and direction of association between maternal trauma history and infant rate of recovery from arousal. The association between maternal trauma history and infant distress to limitations was somewhat stronger among infants exposed to higher levels of prenatal cortisol. The analyses suggested that maternal lifetime trauma exposure is associated with several domains of infant negative affectivity independently of maternal stress exposures during pregnancy and that some of these associations may be modified by prenatal cortisol exposure. The findings have implications for understanding the intergenerational impact of trauma exposure on child developmental outcomes.     

Estimation and diagnostic for skew-normal partially linear models

Partially linear models (PLMs) are an important tool in modelling economic and biometric data and are considered as a flexible generalization of the linear model by including a nonparametric component of some covariate into the linear predictor. Usually, the error component is assumed to follow a normal distribution. However, the theory and application (through simulation or experimentation) often generate a great amount of data sets that are skewed. The objective of this paper is to extend the PLMs allowing the errors to follow a skew-normal distribution [A. Azzalini, A class of distributions which includes the normal ones, Scand. J. Statist. 12 (1985), pp. 171–178], increasing the flexibility of the model. In particular, we develop the expectation-maximization (EM) algorithm for linear regression models and diagnostic analysis via local influence as well as generalized leverage, following [H. Zhu and S. Lee, Local influence for incomplete-data models, J. R. Stat. Soc. Ser. B 63 (2001), pp. 111–126]. A simulation study is also conducted to evaluate the efficiency of the EM algorithm. Finally, a suitable transformation is applied in a data set on ragweed pollen concentration in order to fit PLMs under asymmetric distributions. An illustrative comparison is performed between normal and skew-normal errors.     

Asymmetrically fair rules for an indivisible good problem with a budget constraint

We study a particular restitution problem where there is an indivisible good (land or property) over which two agents have rights: the dispossessed agent and the owner. A third party, possibly the government, seeks to resolve the situation by assigning rights to one and compensate the other. There is also a maximum amount of money available for the compensation. We characterize a family of asymmetrically fair rules that are immune to strategic behavior, guarantee minimal welfare levels for the agents, and satisfy the budget constraint.     

The Equivalence of Neyman Optimum Allocation for Sampling and Equal Proportions for Apportioning the U.S. House of Representatives

We present a surprising though obvious result that seems to have been unnoticed until now. In particular, we demonstrate the equivalence of two well-known problems—the optimal allocation of the fixed overall sample size n among L strata under stratified random sampling and the optimal allocation of the H = 435 seats among the 50 states for apportionment of the U.S. House of Representatives following each decennial census. In spite of the strong similarity manifest in the statements of the two problems, they have not been linked and they have well-known but different solutions; one solution is not explicitly exact (Neyman allocation), and the other (equal proportions) is exact. We give explicit exact solutions for both and note that the solutions are equivalent. In fact, we conclude by showing that both problems are special cases of a general problem. The result is significant for stratified random sampling in that it explicitly shows how to minimize sampling error when estimating a total T_Y while keeping the final overall sample size fixed at n; this is usually not the case in practice with Neyman allocation where the resulting final overall sample size might be near n + L after rounding. An example reveals that controlled rounding with Neyman allocation does not always lead to the optimum allocation, that is, an allocation that minimizes variance.     

Respiratory illness in Saskatoon infants: The impact of housing and neighbourhood characteristics

Respiratory illness is an important childhood illness and a significant cause of infant mortality. This study examined the relationship between neighbourhood level variables and rates of respiratory illness for children less than 2 years of age, born in Saskatoon between 1992 and 1994. Rates of respiratory illness, as measured by proportion of children hospitalized and frequency of hospitalization and ambulatory visits to physicians, were higher among infants living in socially disadvantaged neighbourhoods. Multivariate predictive models for rates of respiratory illness support the research hypothesis that housing characteristics, in the presence of other neighbourhood characteristics, have a significant impact on determining the rates of respiratory illness among Saskatoon children.     

Conditional moving linear regression: Modeling the recruitment process for ALLHAT

Effective recruitment is a prerequisite for successful execution of a clinical trial. ALLHAT, a large hypertension treatment trial (N = 42,418), provided an opportunity to evaluate adaptive modeling of recruitment processes using conditional moving linear regression. Our statistical modeling of recruitment, comparing Brownian and fractional Brownian motion, indicates that fractional Brownian motion combined with moving linear regression is better than classic Brownian motion in terms of higher conditional probability of achieving a global recruitment goal in 4-week ahead projections. Further research is needed to evaluate how recruitment modeling can assist clinical trialists in planning and executing clinical trials.     

The Next Generation of Collective Action Research

The articles presented in this volume describe part of a new generation of interest and vigor in the social psychological study of collective action. This new wave builds nicely on the foundation set by social identity, self-categorization, and relative deprivation theories but also introduces a number of important innovative perspectives and variables. In this commentary, I review some of these expansions and additions, raise a number of conceptual concerns that arise out of these new directions, and discuss more generally some novel and important directions that emerge from the work presented in the volume and in other recent work on collective action.     

The power functions of the likelihood ratio tests for a simply ordered trend in normal means

Likelihood ratio tests for the homogeneity of k normal means with the alternative restricted by an increasing trend are considered as well as the likelihood ratio tests of the null hypothesis that the means satisfy the trend. While the work is primarily a survey of results concerning the power functions of these tests, the extensions of some results to the case of not necessarily equal sample sizes are presented. For the case of known or unknown population variances, exact expressions are given for the power functions for k=3,4, and approximations are discussed for larger k. The topics of consistency, bias and monotonicity of the power functions are included. Also, Bartholomew's conjectures concerning minimal and maximal powers are investigated, with results of a new numerical study given.