Spatial analysis of auto-multivariate lattice data

Most problems related to environmental studies are innately multivariate. In fact, in each spatial location more than one variable is usually measured. In geostatistics multivariate data analysis, where we intend to predict the value of a random vector in a new site, which has no data, cokriging method is used as the best linear unbiased prediction. In lattice data analysis, where almost exclusively the probability modeling of data is of concern, only auto-Gaussian model has been used for continuous multivariate data. For discrete multivariate data little work has been carried out. In this paper, an auto-multinomial model is suggested for analyzing multivariate lattice discrete data. The proposed method is illustrated by a real example of air pollution in Tehran, Iran.     

Maximum a-posteriori estimation of autoregressive processes based on finite mixtures of scale-mixtures of skew-normal distributions

This article investigates maximum a-posteriori (MAP) estimation of autoregressive model parameters when the innovations (errors) follow a finite mixture of distributions that, in turn, are scale-mixtures of skew-normal distributions (SMSN), an attractive and extremely flexible family of probabilistic distributions. The proposed model allows to fit different types of data which can be associated with different noise levels, and provides a robust modelling with great flexibility to accommodate skewness, heavy tails, multimodality and stationarity simultaneously. Also, the existence of convenient hierarchical representations of the SMSN random variables allows us to develop an EM-type algorithm to perform the MAP estimates. A comprehensive simulation study is then conducted to illustrate the superior performance of the proposed method. The new methodology is also applied to annual barley yields data.     

Levels of Mental Health Continuum and Personality Traits

Empirically, mental health and mental illness are not opposite ends of a single measurement continuum. In view of this fact, Keyes (J Health Soc Behav, 43:207–202, 2002) operationalizes mental health as a syndrome of symptoms of both positive feelings (emotional well-being) and positive functioning (psychological and social well-being) in life. In his comprehensive model, the presence of mental health is described as flourishing in life, and the absence of mental health is characterized as languishing in life. The aim of this study was to investigate the discriminatory power of Big Five personality traits in discriminating among the levels of mental health continuum using an Iranian university student sample. Findings revealed that respondents with different levels of mental health differed significantly on four of the five personality traits (extraversion, neuroticism, conscientiousness, and agreeableness). All in all, the results of this study converged with prior findings about the importance of Big Five personality traits in predicting well-being.

The Global Challenge of Jihadist Terrorism: A Quality-of-Life Model

Social Indicators Research - Living in poverty may have long-run negative effects. The interaction between families and public policies is crucial as it affects a child’s opportunities. The...     

A program to calculate bootstrap confidence intervals for the process capability index Cpk

Franklin and Wasserman (1991) introduced the use of Bootstrap sampling procedures for deriving nonparametric confidence intervals for the process capability index, C_pk, which are applicable for instances when at least twenty data points are available. This represents a significant reduction in the usually recommended sample requirement of 100 observations (see Gunther 1989). To facilitate and encourage the use of these procedures. a FORTRAN program is provided for computation of confidence intervals for C_pk. Three methods are provided for this calculation including the standard method, the percentile confidence interval, and the biased - corrected percentile confidence interval.     

Robust estimation of the correlation matrix of longitudinal data

We propose a double-robust procedure for modeling the correlation matrix of a longitudinal dataset. It is based on an alternative Cholesky decomposition of the form Σ=DLL ^? D where D is a diagonal matrix proportional to the square roots of the diagonal entries of Σ and L is a unit lower-triangular matrix determining solely the correlation matrix. The first robustness is with respect to model misspecification for the innovation variances in D, and the second is robustness to outliers in the data. The latter is handled using heavy-tailed multivariate t-distributions with unknown degrees of freedom. We develop a Fisher scoring algorithm for computing the maximum likelihood estimator of the parameters when the nonredundant and unconstrained entries of (L,D) are modeled parsimoniously using covariates. We compare our results with those based on the modified Cholesky decomposition of the form LD ² L ^? using simulations and a real dataset.     

Centrality and Dimensionality of 14 Indicators of Mental Well-Being in Four Countries: Developing an Integrative Framework to Guide Theorizing and Measurement

Social Indicators Research - The primary objective of this research is to develop an integrative framework for distinguishing and classifying well-being variables. Towards this end, rigorous...     

Robust Estimation of Parameters in Simple Linear Profiles Using M-Estimators

In many situations, the quality of a process or product may be better characterized and summarized by a relationship between the response variable and one or more explanatory variables. Parameter estimation is the first step in constructing control charts. Outliers may hamper proper classical estimators and lead to incorrect conclusions. To remedy the problem of outliers, robust methods have been developed recently. In this article, a robust method is introduced for estimating the parameters of simple linear profiles. Two weight functions, Huber and Bisquare, are applied in the estimation algorithm. In addition, a method for robust estimation of the error terms variance is proposed. Simulation studies are done to investigate and evaluate the performance of the proposed estimator, as well as the classical one, in the presence and absence of outliers under different scenarios by the means of MSE criterion. The results reveal that the robust estimators proposed in this research perform as well as classical estimators in the absence of outliers and even considerably better when outliers exist. The maximum value of variance estimate in one scenario obtained from classical estimator is 10.9, while this value is 1.66 and 1.27 from proposed robust estimators when its actual value is 1.