The Role of Giving and Receiving Emotional Support on Depressive Symptomatology among Older Couples: An Application of the Actor-Partner Interdependence Model

The present study investigated whether perception of receiving emotional support mediates the relationship between one partner's giving of emotional support and the other partner's depressive symptomatology using a population-based sample of 423 couples from the Changing Lives of Older Couples study. A path model was used guided by the Actor-Partner Interdependence Model. Results indicated that spouses' giving emotional support was related to the degree to which their spouse reported receiving emotional support. Perception of receiving emotional support, in turn, was related to lower depressive symptomatology of the support recipient. Both husbands and wives can benefit from emotional support through their perception of receiving emotional support, and spouses' perceptions, as well as their actions, should be considered in support transactions.     

When Adaptations Go Awry: Functional and Dysfunctional Aspects of Social Anxiety

Adaptations are psychological and behavioral mechanisms designed through evolution to serve specific purposes ultimately related to reproductive success. Although adaptations are inherently functional, in some cases their operation can nevertheless cause personal and social dysfunction. We describe a theoretical framework for understanding, predicting, and reducing the dysfunctional consequences of psychological adaptations. We discuss three general sources of dysfunction: a) the existence of adaptive tradeoffs, b) mismatches between current environments and ancestral environments, and c) individual differences. The paper applies this framework primarily to the topic of social anxiety, a psychological phenomenon marked by concerns pertaining to social rejection and embarrassment. Although social anxiety can serve useful functions, it can also involve excessive worry, negative affect, and avoidance of social situations, leading to significant distress and social impairment. We consider sources of dysfunction in social anxiety and discuss implications for policy, including recommendations for psychological, situational, and biological interventions. We also discuss broader applications of this theoretical framework to other areas of social life.     

Data skeletons: simultaneous estimation of multiple quantiles for massive streaming datasets with applications to density estimation

We consider the problem of density estimation when the data is in the form of a continuous stream with no fixed length. In this setting, implementations of the usual methods of density estimation such as kernel density estimation are problematic. We propose a method of density estimation for massive datasets that is based upon taking the derivative of a smooth curve that has been fit through a set of quantile estimates. To achieve this, a low-storage, single-pass, sequential method is proposed for simultaneous estimation of multiple quantiles for massive datasets that form the basis of this method of density estimation. For comparison, we also consider a sequential kernel density estimator. The proposed methods are shown through simulation study to perform well and to have several distinct advantages over existing methods.     

On Christensen's conjecture

In this paper we give solution to a conjecture appearing in Christensen (1997, p. 360) in relation to the definitions of standardized residuals in loglinear models. Work done when he was visiting as the Distinguished Lukacs Professor on leave from the Complutense University of Madrid. An erratum to this article is available at .     

Bayesian analysis of human movement curves

Summary.  We consider the Bayesian analysis of human movement data, where the subjects perform various reaching tasks. A set of markers is placed on each subject and a system of cameras records the three-dimensional Cartesian co-ordinates of the markers during the reaching movement. It is of interest to describe the mean and variability of the curves that are traced by the markers during one reaching movement, and to identify any differences due to covariates. We propose a methodology based on a hierarchical Bayesian model for the curves. An important part of the method is to obtain identifiable features of the movement so that different curves can be compared after temporal warping. We consider four landmarks and a set of equally spaced pseudolandmarks are located in between. We demonstrate that the algorithm works well in locating the landmarks, and shape analysis techniques are used to describe the posterior distribution of the mean curve. A feature of this type of data is that some parts of the movement data may be missing—the Bayesian methodology is easily adapted to cope with this situation.     

Adaptive sampling without replacement of clusters

In a common form of adaptive cluster sampling, an initial sample of units is selected by random sampling without replacement and, whenever the observed value of the unit is sufficiently high, its neighboring units are added to the sample, with the process of adding neighbors repeated if any of the added units are also high valued. In this way, an initial selection of a high-valued unit results in the addition of the entire network of surrounding high-valued units and some low-valued “edge” units where sampling stops. Repeat selections can occur when more than one initially selected unit is in the same network or when an edge unit is shared by more than one added network. Adaptive sampling without replacement of networks avoids some of this repeat selection by sequentially selecting initial sample units only from the part of the population not already in any selected network. The design proposed in this paper carries this step further by selecting initial units only from the population, exclusive of any previously selected networks or edge units.     

Posterior consistency of logistic Gaussian process priors in density estimation

We establish weak and strong posterior consistency of Gaussian process priors studied by Lenk [1988. The logistic normal distribution for Bayesian, nonparametric, predictive densities. J. Amer. Statist. Assoc. 83 (402), 509–516] for density estimation. Weak consistency is related to the support of a Gaussian process in the sup-norm topology which is explicitly identified for many covariance kernels. In fact we show that this support is the space of all continuous functions when the usual covariance kernels are chosen and an appropriate prior is used on the smoothing parameters of the covariance kernel. We then show that a large class of Gaussian process priors achieve weak as well as strong posterior consistency (under some regularity conditions) at true densities that are either continuous or piecewise continuous.     

Tests for assessment of agreement using probability criteria

For the assessment of agreement using probability criteria, we obtain an exact test, and for sample sizes exceeding 30, we give a bootstrap-t$t$ test that is remarkably accurate. We show that for assessing agreement, the total deviation index approach of Lin [2000. Total deviation index for measuring individual agreement with applications in laboratory performance and bioequivalence. Statist. Med. 19, 255–270] is not consistent and may not preserve its asymptotic nominal level, and that the coverage probability approach of Lin et al. [2002. Statistical methods in assessing agreement: models, issues and tools. J. Amer. Statist. Assoc. 97, 257–270] is overly conservative for moderate sample sizes. We also show that the nearly unbiased test of Wang and Hwang [2001. A nearly unbiased test for individual bioequivalence problems using probability criteria. J. Statist. Plann. Inference 99, 41–58] may be liberal for large sample sizes, and suggest a minor modification that gives numerically equivalent approximation to the exact test for sample sizes 30 or less. We present a simple and accurate sample size formula for planning studies on assessing agreement, and illustrate our methodology with a real data set from the literature.     

Inferences on correlation coefficients: One-sample,independent and correlated cases

This article concerns inference on the correlation coefficients of a multivariate normal distribution. Inferential procedures based on the concepts of generalized variables (GVs) and generalized p$p$-values are proposed for elements of a correlation matrix. For the simple correlation coefficient, the merits of the generalized confidence limits and other approximate methods are evaluated using a numerical study. The study indicates that the proposed generalized confidence limits are uniformly most accurate even for samples as small as three. The results are extended for comparing two independent correlations, overlapping and non-overlapping dependent correlations. For each problem, the properties of the GV approach and other asymptotic methods are evaluated using Monte Carlo simulation. The GV approach produces satisfactory results for all the problems considered. The methods are illustrated using a few practical examples.