Internationally adopted children's general and adoption‐specific stressors,coping strategies and psychological adjustment

We examined stress, coping and psychological adjustment of 68 children, aged 8–12, who were internationally adopted to Spain. Using the Kidcope, all children were asked about the most stressful general and adoption‐related problem they experienced and the use and effectiveness of various coping strategies when dealing with the problem. For all reported problems, the nature of the problem (personal, interpersonal, regarding others), the content, the degree of stress it created and its perceived controllability were analysed. Emotional and behavioural functionings were assessed with the Behaviour Assessment System for Children. About half of the children mentioned specific problems concerning the adoption, with inracial adoptees reporting less adoption‐related problems than transracial adoptees. For general and adoption‐related problems, interpersonal problems were mentioned most often. With regard to the content, ‘relationships’ and ‘victimization’ were mentioned most often for general and adoption‐related problems, respectively. Adoption‐related problems were appraised as less controllable. No differences emerged in terms of coping with general or adoption‐related problems except for ‘self‐criticism’. Overall, the children used many coping strategies and were generally well‐adjusted. Identifying the problems and coping strategies of adoptees is important in order to help these children and their families tackle these stressors.     

Analysis of local influence in geostatistics using Student's t-distribution

This article aims to estimate parameters of spatial variability with Student's t-distribution by the EM algorithm and present the study of local influence by means of two methods known as likelihood displacement and Q-displacement of likelihood, both using Student's t-distribution with fixed degrees of freedom (ν). The results showed that both methods are effective in the identification of influential points.     

Danger and Dementia: Caregiver Experiences and Shifting Social Roles During a Highly Active Hurricane Season

This study examined disaster preparedness and decision-making by caregivers of community-dwelling persons diagnosed with Alzheimer’s or a related dementia (ADRD). Interviews were conducted with 20 caregivers in South Florida. Twelve of these interviews include caregiving experiences during the highly active 2004–2005 hurricane seasons. Results indicate that persons in earlier stages of ADRD can, and often do, remain engaged in the disaster preparation and planning process. However, during the early stages, persons may also resist evacuation, even if the caregiver felt it was necessary. During later stages of the disease, caregivers reported less resistance to disaster-related decisions, however, with the tradeoff of less ability to assist with preparation.     

A Note on Whittle's Likelihood

The approximate likelihood function introduced by Whittle has been used to estimate the spectral density and certain parameters of a variety of time series models. In this note we attempt to empirically quantify the loss of efficiency of Whittle's method in nonstandard settings. A recently developed representation of some first-order non-Gaussian stationary autoregressive process allows a direct comparison of the true likelihood function with that of Whittle. The conclusion is that Whittle's likelihood can produce unreliable estimates in the non-Gaussian case, even for moderate sample sizes. Moreover, for small samples, and if the autocorrelation of the process is high, Whittle's approximation is not efficient even in the Gaussian case. While these facts are known to some extent, the present study sheds more light on the degree of efficiency loss incurred by using Whittle's likelihood, in both Gaussian and non-Gaussian cases.     

Measuring Time Series Predictability Using Support Vector Regression

Most studies involving statistical time series analysis rely on assumptions of linearity, which by its simplicity facilitates parameter interpretation and estimation. However, the linearity assumption may be too restrictive for many practical applications. The implementation of nonlinear models in time series analysis involves the estimation of a large set of parameters, frequently leading to overfitting problems. In this article, a predictability coefficient is estimated using a combination of nonlinear autoregressive models and the use of support vector regression in this model is explored. We illustrate the usefulness and interpretability of results by using electroencephalographic records of an epileptic patient.     

Accuracy of Power-Divergence Statistics for Testing Independence and Homogeneity in Two-Way Contingency Tables

The small-sample accuracy of seven members of the family of power-divergence statistics for testing independence or homogeneity in contingency tables was studied via simulation. The likelihood ratio statistic G ² and Pearson's X ² statistic are among these seven members, whose behavior was studied at nominal test sizes of.01 and.05 with marginal distributions that could be uniform or skewed and with a set of sample sizes that included sparseness conditions as measured through table density (i.e., the ratio of sample size to number of cells). The likelihood ratio statistic G ² rejected the null hypothesis too often even with large table density, whereas Pearson's X ² was sufficiently accurate and only presented a minor misbehavior when table density was less than two observations/cell. None of the other five statistics outperformed Pearson's X ². A nonasymptotic variant of X ² solved the minor inaccuracies of Pearson's X ² and turned out to be the most accurate statistic for testing independence or homogeneity, even with table densities of one observation/cell. These results clearly advise against the use of the likelihood ratio statistic G ².     

Alternative moment approximations for berkson's minimum logit chi-squared estimator

Expressions are derived for the bias to order J^-1 , the variance to order J^-2 and the mean squared error to order J^-2 of Berkson's minimum logit chi-squared estimator where J is the number of distinct design points. These moment approximations are numerically compared to Monte Carlo estimates of the true moments and the moment approximations of Amemiya (1980) which are appropriate when the “average” number of observations per design point is large. They are used to compare the mean squared error of the minimum logit chi-squared estimator to that of the maximum likelihood estimator and to investigate the effect of bias on confidence intenrals constructed using the minimum logit chi-squared estimator.     

Sensitivity measures of influence on the loading matrix in exploratory factor analysis

For the detection of influential observations on the loading matrix of the factor analysis model, we propose to use the infinitesimal version of two matrix coefficients, including Escoufier (1973)'s also discussed the application in factor analysis of some sensitivity measures used for similar purposes in principal component analysis.