Optimality of blue's in a general linear model with incorrect design matrix

We consider the Gauss-Markoff model (Y,X₀β,σ²V) and provide solutions to the following problem: What is the class of all models (Y,Xβ,σ²V) such that a specific linear representation/some linear representation/every linear representation of the BLUE of every estimable parametric functional p'β under (Y,X₀β,σ²V) is (a) an unbiased estimator, (b) a BLUE, (c) a linear minimum bias estimator and (d) best linear minimum bias estimator of p'β under (Y,Xβ,σ²V)? We also analyse the above problems, when attention is restricted to a subclass of estimable parametric functionals.     

Natural disaster preparedness in college students: Implications for institutions of higher learning

Objective: There is growing interest in the effectiveness of disaster preparedness at universities. Although several studies have examined student preparedness perceptions, a better understanding of factors that may influence actual preparedness is needed. Participants: Seven hundred sixty-five undergraduate and graduate students at a southeastern U.S. university completed an online survey in September 2013. Methods: Participants were administered an online survey that included questions regarding disaster preparedness and their experiences with disasters. Results: Students' disaster concern was more related to perceived preparedness over actual preparedness; disaster experience significantly predicted both actual and perceived preparedness. Perceived university preparedness was a significant predictor of disaster concern. Conclusion: The results suggest that perceived and actual preparedness are related but not entirely equivalent, which emphasizes the importance of differentiating the two constructs. Limitations of the current study and recommendations for future research are provided.     

The History and Timing of Depression Onset as Predictors of Young Adult Self‐Esteem

Depression often emerges early in the lifecourse and is consistently shown to be associated with poor self‐esteem. The 3 main objectives of the current study are to (1) evaluate the association between a history major depression and self‐esteem in young adulthood, (2) assess the relationship between timing of depression onset and young adult self‐esteem, and (3) help rule out the alternative interpretation that the relationship between major depression and self‐esteem is due to state dependence bias stemming from recent depressive symptoms and stressful life events. To address these objectives we use data from a 2‐wave panel study based on a community sample of young adults in Miami‐Dade County, Florida (n=1,197). Results indicated that a history of major depression during sensitive periods of social development is associated with negative changes in self‐esteem over a 2‐year period during the transition to young adulthood. Among those with a history of depression, earlier onset was more problematic than later onset for young adult self‐esteem, although the difference disappeared once the level of self‐esteem 2 years prior was controlled. The linkages between the history and timing of depression onset with self‐esteem were observed net of recent depressive symptoms and stressful life events and are thus robust to an alternative interpretation of state dependence. The findings support the argument that major depression, especially if it develops earlier during child‐adolescent development, has negative consequences for one's self‐esteem.     

Joint confidence regions in the multivariate calibration problem

Consider a vector valued response variable related to a vector valued explanatory variable through a normal multivariate linear model. The multivariate calibration problem deals with statistical inference on unknown values of the explanatory variable. The problem addressed is the construction of joint confidence regions for several unknown values of the explanatory variable. The problem is investigated when the variance covariance matrix is a scalar multiple of the identity matrix and also when it is a completely unknown positive definite matrix. The problem is solved in only two cases: (i) the response and explanatory variables have the same dimensions, and (ii) the explanatory variable is a scalar. In the former case, exact joint confidence regions are derived based on a natural pivot statistic. In the latter case, the joint confidence regions are only conservative. Computational aspects and the practical implementation of the confidence regions are discussed and illustrated using an example.     

Antisocial Behavior of Adoptees and Nonadoptees: Prediction from Early History and Adolescent Relationships

This study examined the contribution of demographic characteristics, early maltreatment, and peer and family relationships during adolescence to the prediction of aggressive and nonaggressive antisocial behavior (AASB and NAASB, respectively) during young adulthood; and determined whether adoption status has additional ability to predict ASB, once background, early experience, peer, and family variables were controlled. Data from adolescent and parent interviews were used from Waves 1 (predictors) and 3 (outcomes) of the National Longitudinal Study of Adolescent Health (Add Health). The sample included 337 adopted and 10,339 nonadopted adolescents whose mean ages were 15.8 at W1 and 21.7 at W3. Although AASB and NAASB were predicted by background characteristics, early maltreatment, peer relations, and family relationships, adoption status had little to no additional predictive power once the other variables were controlled.     

The Consequences of Migration to the United States for Short-Term Changes in the Health of Mexican Immigrants

Although many studies have attempted to examine the consequences of Mexico-U.S. migration for Mexican immigrants’ health, few have had adequate data to generate the appropriate comparisons. In this article, we use data from two waves of the Mexican Family Life Survey (MxFLS) to compare the health of current migrants from Mexico with those of earlier migrants and nonmigrants. Because the longitudinal data permit us to examine short-term changes in health status subsequent to the baseline survey for current migrants and for Mexican residents, as well as to control for the potential health selectivity of migrants, the results provide a clearer picture of the consequences of immigration for Mexican migrant health than have previous studies. Our findings demonstrate that current migrants are more likely to experience recent changes in health status—both improvements and declines—than either earlier migrants or nonmigrants. The net effect, however, is a decline in health for current migrants: compared with never migrants, the health of current migrants is much more likely to have declined in the year or two since migration and not significantly more likely to have improved. Thus, it appears that the migration process itself and/or the experiences of the immediate post-migration period detrimentally affect Mexican immigrants’ health.     

Gender Differences in Happiness and Life Satisfaction Among Adolescents in Hong Kong: Relationships and Self-Concept

This study uses survey data from adolescents (N = 1,428) in Hong Kong to test the association of gender with happiness and life satisfaction through relationship style and self-concept. While self-esteem and purpose in life are associated with higher happiness and life satisfaction, having more close friends is related to higher happiness, but not necessarily life satisfaction. On the other hand, boys with higher academic achievement are happier, but not more satisfied; the opposite holds true for girls. Our results provide a much-needed investigation of the differential effect of gender on the subjective well-being of adolescents. Contributing to the theoretical debate about the concepts of subjective well-being, we argue that happiness and life satisfaction are empirically and conceptually distinct. Life satisfaction might be characterized by more profound enjoyment and achievement in life than happiness.     

Improved nonparametric tolerance intervals based on interpolated and extrapolated order statistics

The standard approach to construct nonparametric tolerance intervals is to use the appropriate order statistics, provided a minimum sample size requirement is met. However, it is well-known that this traditional approach is conservative with respect to the nominal level. One way to improve the coverage probabilities is to use interpolation. However, the extension to the case of two-sided tolerance intervals, as well as for the case when the minimum sample size requirement is not met, have not been studied. In this paper, an approach using linear interpolation is proposed for improving coverage probabilities for the two-sided setting. In the case when the minimum sample size requirement is not met, coverage probabilities are shown to improve by using linear extrapolation. A discussion about the effect on coverage probabilities and expected lengths when transforming the data is also presented. The applicability of this approach is demonstrated using three real data sets.     

A Sensitivity Analysis of the Social Vulnerability Index

The Social Vulnerability Index (SoVI), created by Cutter et al. (2003) , examined the spatial patterns of social vulnerability to natural hazards at the county level in the United States in order to describe and understand the social burdens of risk. The purpose of this article is to examine the sensitivity of quantitative features underlying the SoVI approach to changes in its construction, the scale at which it is applied, the set of variables used, and to various geographic contexts. First, the SoVI was calculated for multiple aggregation levels in the State of South Carolina and with a subset of the original variables to determine the impact of scalar and variable changes on index construction. Second, to test the sensitivity of the algorithm to changes in construction, and to determine if that sensitivity was constant in various geographic contexts, census data were collected at a submetropolitan level for three study sites: Charleston, SC; Los Angeles, CA; and New Orleans, LA. Fifty-four unique variations of the SoVI were calculated for each study area and evaluated using factorial analysis. These results were then compared across study areas to evaluate the impact of changing geographic context. While decreases in the scale of aggregation were found to result in decreases in the variance explained by principal components analysis (PCA), and in increases in the variance of the resulting index values, the subjective interpretations yielded from the SoVI remained fairly stable. The algorithm's sensitivity to certain changes in index construction differed somewhat among the study areas. Understanding the impacts of changes in index construction and scale are crucial in increasing user confidence in metrics designed to represent the extremely complex phenomenon of social vulnerability.     

Admissible linear estimation in singular linear models with respect to a restricted parameter set

The admissibility results of Hoffmann (1977), proved in the context of a nonsingular covariance matrix are extended to the situation where the covariance matrix is singular. Admissible linear estimators in the Gauss-Markoff model are characterised and admissibility of the Best Linear Unbiased Estimator is investigated.