Simultaneous estimation and inference for multiple response variables

Multivariate data arise frequently in biomedical and health studies where multiple response variables are collected across subjects. Unlike a univariate procedure fitting each response separately, a multivariate regression model provides a unique opportunity in studying the joint evolution of various response variables. In this paper, we propose two estimation procedures that improve estimation efficiency for the regression parameter by accommodating correlations among the response variables. The proposed procedures do not require knowledge of the true correlation structure nor does it estimate the parameters associated with the correlation. Theoretical and simulation results confirm that the proposed estimators are more efficient than the one obtained from the univariate approach. We further propose simple and powerful inference procedures for a goodness-of-fit test that possess the chi-squared asymptotic properties. Extensive simulation studies suggest that the proposed tests are more powerful than the Wald test based on the univariate procedure. The proposed methods are also illustrated through the mother’s stress and children’s morbidity study.     

Robust Designs in Generalized Linear Models: A Quantile Dispersion Graphs Approach

This article studies design selection for generalized linear models (GLMs) using the quantile dispersion graphs (QDGs) approach in the presence of misspecification in the link and/or linear predictor. The uncertainty in the linear predictor is represented by a unknown function and estimated using kriging. For addressing misspecified link functions, a generalized family of link functions is used. Numerical examples are shown to illustrate the proposed methodology.     

Flexible regression modeling

The author proposes a new method for flexible regression modeling of multi‐dimensional data, where the regression function is approximated by a linear combination of logistic basis functions. The method is adaptive, selecting simple or more complex models as appropriate. The number, location, and (to some extent) shape of the basis functions are automatically determined from the data. The method is also affine invariant, so accuracy of the fit is not affected by rotation or scaling of the covariates. Squared error and absolute error criteria are both available for estimation. The latter provides a robust estimator of the conditional median function. Computation is relatively fast, particularly for large data sets, so the method is well suited for data mining applications.     

Robust testing with generalized partial linear models for longitudinal data

By approximating the nonparametric component using a regression spline in generalized partial linear models (GPLM), robust generalized estimating equations (GEE), involving bounded score function and leverage-based weighting function, can be used to estimate the regression parameters in GPLM robustly for longitudinal data or clustered data. In this paper, score test statistics are proposed for testing the regression parameters with robustness, and their asymptotic distributions under the null hypothesis and a class of local alternative hypotheses are studied. The proposed score tests reply on the estimation of a smaller model without the testing parameters involved, and perform well in the simulation studies and real data analysis conducted in this paper.     

Attention and Executive Functions as Mediators of Attachment and Behavior Problems

The purpose of this study was to provide a more comprehensive understanding of the relation between early parent‐child interactions and subsequent behavior problems and how certain cognitive processes mediate this relation. Specifically, this study investigated whether attention, inhibition, and planning skills mediate the relation between attachment security and behavior problems. Data were collected as part of the National Institute of Child Health and Development‐Study of Early Child Care and Youth Development when children (N = 1004) were between 36 months and the third grade. Results from structural equation models indicated that sustained attention mediated the relation between disorganized attachment and social problems. Planning mediated the relation between disorganized attachment and subsequent thought problems, attention problems, and delinquent behavior. Avoidant, ambivalent, and disorganized attachment directly predicted several behavior problems. Implications for theory and future research are discussed.     

论图书馆传统职能和现代信息服务职能的统一

伴随着科技进步与计算机技术的迅猛发展,图书馆的传统职能虽然不会改变,但是其现代信息服务职能将不断变革和发展.适应这一新形势的需要,应采取补缺、创新、服务、辐射和重组等策略,不断完善信息服务方式,开发出新的信息产品,以满足用户需求.     

Probability weighting in different domains: The role of affect,fungibility, and stakes

This paper reports the results of a laboratory experiment in which probability weighting functions for risky gains were elicited non-parametrically in over 500 incentivized subjects. I compared probability weights for monetary rewards to two less fungible domains involving vouchers for different types of consumption, inducing stronger or weaker (positive) emotions. The level of stakes was also manipulated. I found that the probability to win monetary rewards was weighted almost linearly in the high stakes condition, the probability to win vouchers associated with stronger positive affect was underweighted and the probability to win affect-poor vouchers was strongly underweighted. Substantial underweighting also prevailed in all three domains in the low stakes condition.     

On the Product of Inverse Wishart and Normal Distributions with Applications to Discriminant Analysis and Portfolio Theory

Abstract. In this article we analyse the product of the inverse Wishart matrix and a normal vector. We derive the explicit joint distribution of the components of the product. Furthermore, we suggest several exact tests of general linear hypothesis about the elements of the product. We illustrate the developed techniques on examples from discriminant analysis and from portfolio theory.     

Stochastic comparisons of order statistics,with applications in relibility

This paper reviews recent developments in the stochastic comparison of order statistics. The results discussed are basically: (l) Stochastic comparisons of linear combinations of order statistics from distributions F and G where G^?1 F is convex or starshaped. (2) Stochastic comparisons of individual order statistics and of vectors of order statistics from underlying heterogeneous distributions by the use of majorization and Schur function theory. (3) Stochastic comparison of random processes. Applications to reliability problems are presented illustrating the use and value of the theoretical results described     

Bayesian variable selection with spherically symmetric priors

We propose that Bayesian variable selection for linear parametrizations with Gaussian iid likelihoods should be based on the spherical symmetry of the diagonalized parameter space. Our r-prior results in closed forms for the evidence for four examples, including the hyper-g prior and the Zellner–Siow prior, which are shown to be special cases. Scenarios of a single-variable dispersion parameter and of fixed dispersion are studied, and asymptotic forms comparable to the traditional information criteria are derived. A simulation exercise shows that model comparison based on our r-prior gives good results comparable to or better than current model comparison schemes.