Influence diagnostics in the capital asset pricing model under elliptical distributions

In this paper we consider the Capital Asset Pricing Model under Elliptical (symmetric) Distributions. This class of distributions, which contains the normal distribution, t, contaminated normal and power exponential, among others, offers a more flexible framework for modelling asset prices or returns. In order to analyze the sensibility to possible outliers and/or atypical returns of the maximum likelihood estimators, the local influence method was implemented. The results are illustrated by using a set of shares from companies who trade in the Chilean Stock Market. Our main conclusion is that symmetric distributions having heavier tails than those of the normal distribution, especially the t distribution with small degrees of freedom, show a better fit and allow the reduction of the influence of atypical returns in the maximum likelihood estimators.     

Influence diagnostics for the structural sharpe model under normal/independent distributions

A method is proposed in this paper to assess the local influence of minor perturbations for the Sharpe model when the normal distribution is replaced by normal/independent (NI) distributions. The family of NI distributions is an attractive class of symmetric heavy-tailed densities that includes as special cases the normal, t-Student, slash, and the contaminated normal distributions. Since the returns of the market are not observable, the statistical analysis is carried out in the context of an errors-in-variables model. An influence analysis for detecting influential observations (atypical returns) is developed to investigate the sensitivity of the maximum likelihood estimators. Diagnostic measures are obtained based on the conditional expectation of the complete-data log-likelihood function. The results are illustrated by using a set of shares of companies traded in the Chilean stock market.     

Physical Activity,Exercise, and Sedentary Behavior in College Students

Abstract

Objective: This article examines students’ perceptions of individual and institutional responsibility for sexual health so that institutions can better provide for the needs of their students to increase academic success and healthy relationship outcomes. Participants: Students from 2- and 4-year colleges in 1 state (N = 78). Methods: From May through November 2010, the authors used go-along interviews to examine students’ perceptions of resources for sexual health on their campuses. Results: Participants believed that it is the college's responsibility to provide resources and the responsibility of students to access resources. Participants at 2-year schools wanted referrals to resources, whereas participants at 4-year schools expected resources to be available and emphasized the importance of a supportive community. Conclusions: Students at 2- and 4-year colleges have different expectations of their institutions; by making resources and referrals for sexual health available, colleges can better serve their students, which will result in improved health outcomes.     

Estimation and Testing in Elliptical Functional Measurement Error Models

The purpose of this article is to investigate estimation and hypothesis testing by maximum likelihood and method of moments in functional models within the class of elliptical symmetric distributions. The main results encompass consistency and asymptotic normality of the method of moments estimators. Also, the asymptotic covariance matrix of the maximum likelihood estimator is derived, extending some existing results in elliptical distributions. A measure of asymptotic relative efficiency is reported. Wald-type statistics are considered and numerical results obtained by Monte Carlo simulation to investigate the performance of estimators and tests are provided for Student-t and contaminated normal distributions. An application to a real dataset is also included.     

Influence Assessment in an Heteroscedastic Errors-in-Variables Model

The main goal of this article is to consider influence assessment in models with error-prone observations and variances of the measurement errors changing across observations. The techniques enable to identify potential influential elements and also to quantify the effects of perturbations in these elements on some results of interest. The approach is illustrated with data from the WHO MONICA Project on cardiovascular disease.     

The structural Sharpe model under t-distributions

In this paper we consider Sharpe's single-index model or Sharpe's model, by assuming that the returns obtained follow a multivariate t elliptical distribution. Also, given that the returns of the market are not observable, the statistical analysis was made in the context of an errors-in-variables model. In order to analyze the sensibility to possible outliers and/or atypical returns of the maximum likelihood estimators the local influence method [10 Cook, R. D. 1986. Assessment of local influence. J. Roy. Statist. Soc. B, 48: 133–169.  [Google Scholar]] was implemented. The results are illustrated by using a set of shares of companies belonging to the Chilean Stock Market. The main conclusion is that the t model with small degrees of freedom is able to incorporate possible outliers and influential returns in the data.     

Robust inference in an heteroscedastic measurement error model

In this paper we deal with robust inference in heteroscedastic measurement error models. Rather than the normal distribution, we postulate a Student t distribution for the observed variables. Maximum likelihood estimates are computed numerically. Consistent estimation of the asymptotic covariance matrices of the maximum likelihood and generalized least squares estimators is also discussed. Three test statistics are proposed for testing hypotheses of interest with the asymptotic chi-square distribution which guarantees correct asymptotic significance levels. Results of simulations and an application to a real data set are also reported.     

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.     

Intimate partner violence and Hurricane Katrina: predictors and associated mental health outcomes

This study sought to establish the prevalence and correlates of intimate partner violence (IPV) victimization in the 6 months before and after Hurricane Katrina. Participants were 445 married or cohabiting persons who were living in the 23 southernmost counties of Mississippi at the time of Hurricane Katrina. Data for this study were collected as part of a larger, population-based, representative study. The percentage of women reporting psychological victimization increased from 33.6% prior to Hurricane Katrina to 45.2% following Hurricane Katrina (p < .001). The percentage of men reporting psychological victimization increased from 36.7% to 43.1% (p = .01). Reports of physical victimization increased from 4.2% to 8.3% for women (p = .01) but were unchanged for men. Significant predictors of post-Katrina victimization included pre-Katrina victimization, age, educational attainment, marital status, and hurricane-related stressors. Reports of IPV were associated with greater risk of post-Katrina depression and posttraumatic stress disorder. Data from the first population-based study to document IPV following a large-scale natural disaster suggest that IPV may be an important but often overlooked public health concern following disasters.     

Influence Diagnostics in log-Birnbaum-Saunders Regression Models

In this paper we present various diagnostic methods for a linear regression model under a logarithmic Birnbaum-Saunders distribution for the errors, which may be applied for accelerated life testing or to compare the median lives of several populations. Some influence methods, such as the local influence, total local influence of an individual and generalized leverage are derived, analysed and discussed. We also present a connection between the local influence and generalized leverage methods. A discussion of the computation of the likelihood displacement as well as the normal curvature in the local influence method are presented. Finally, an example with real data is given for illustration.