Reviewing the latest national policies and services for people with severe mental health disorders in government-funded institutions in Vietnam,and policy recommendations for service improvements

The aim of this paper is to provide the international community with a broad and updated picture of key policies and services for people with severe mental health disorders in Vietnam. In particular, the paper (1) reviews the most important national policies governing care for people with mental health disorders, (2) reviews the status of free-of-charge care provided to people with severe mental health disorders in a network of government-funded facilities across the nation and (3) discusses the future policy directions of Vietnam regarding people with mental health disorders.     

Heteroscedastic and heavy-tailed regression with mixtures of skew Laplace normal distributions

Joint modelling skewness and heterogeneity is challenging in data analysis, particularly in regression analysis which allows a random probability distribution to change flexibly with covariates. This paper, based on a skew Laplace normal (SLN) mixture of location, scale, and skewness, introduces a new regression model which provides a flexible modelling of location, scale and skewness parameters simultaneously. The maximum likelihood (ML) estimators of all parameters of the proposed model via the expectation-maximization (EM) algorithm as well as their asymptotic properties are derived. Numerical analyses via a simulation study and a real data example are used to illustrate the performance of the proposed model.     

Altruism from the house: the impact of home equity on charitable giving

This paper exploits the dramatic increase in house prices in early 2000 and the panel nature of the data to investigate the effect of home equity on charitable giving. Using the Panel Study of Income Dynamics from 2001 to 2007, we find that a 10% increase in home equity increases a household??s contributions to charity by almost 1%. This is in contrast to the effect of non-housing wealth, which we estimate to be one tenth the magnitude. Our results are consistent with the consumption literature that reports a distinct effect of housing wealth that is significantly larger than that of financial wealth.     

A Bayesian mixture model for differential gene expression

Summary.  We propose model-based inference for differential gene expression, using a nonparametric Bayesian probability model for the distribution of gene intensities under various conditions. The probability model is a mixture of normal distributions. The resulting inference is similar to a popular empirical Bayes approach that is used for the same inference problem. The use of fully model-based inference mitigates some of the necessary limitations of the empirical Bayes method. We argue that inference is no more difficult than posterior simulation in traditional nonparametric mixture-of-normal models. The approach proposed is motivated by a microarray experiment that was carried out to identify genes that are differentially expressed between normal tissue and colon cancer tissue samples. Additionally, we carried out a small simulation study to verify the methods proposed. In the motivating case-studies we show how the nonparametric Bayes approach facilitates the evaluation of posterior expected false discovery rates. We also show how inference can proceed even in the absence of a null sample of known non-differentially expressed scores. This highlights the difference from alternative empirical Bayes approaches that are based on plug-in estimates.     

On parameter estimation in Bernoulli trials with dependence

A simple estimator is proposed for the dependence parameter for the Klotz model of Bernoulli trials with Markov dependence and it is compared with the ratio estimator given by Price and the approximate maximum likelihood estimator given by Klotz. The proposed estimator is shown to have considerably smaller bias than the other two estimators with comparable mean squared errors, and has all the large sample optimal properties that the other two estimators have.     

A monte carlo comparison of regression trimmed means

Two proposals are made for constructing adaptive estimators of the parameters in a linear regression model. These estimators are based on regression trimmed means and use an idea of Jaeckel [(1971) Ann Math Statist 42, 1540-1552] and the bootstrap respectively. These adaptive trimmed means as well as some nonadaptive trimmed means are studied by Monte Carlo. A one-step biweight is also included for comparison purposes.     

Effect of Husbands' Employment Status on Their Wives' Subjective Well‐being in Korea

This study used data from the Korea Labor and Income Panel Survey (2001, 2003–2007) to estimate the effect of a husband's unemployment on his wife's subjective well‐being (SWB). Ordinal and pooled binary logistic regression models were estimated using 20,099 observations from a sample of 4,569 married women; a fixed‐effects logistic regression model was also estimated using 5,514 observations from a restricted sample of 1,070 wives who experienced at least 1 change in SWB over the follow‐up period. The findings indicated that a husband's unemployment was detrimental to his wife's SWB. This spillover effect appeared to be mediated in part through nonpecuniary factors (i.e., dissatisfaction with family and social relations). These findings suggest that, beyond income loss and the well‐being of unemployed individuals, the social cost of unemployment should consider the negative effects of unemployment on the family, in particular the spouse.     

Hierarchical-Likelihood Approach for Mixed Linear Models with Censored Data

Mixed linear models describe the dependence via random effects in multivariate normal survival data. Recently they have received considerable attention in the biomedical literature. They model the conditional survival times, whereas the alternative frailty model uses the conditional hazard rate. We develop an inferential method for the mixed linear model via Lee and Nelder's (1996) hierarchical-likelihood (h-likelihood). Simulation and a practical example are presented to illustrate the new method.     

Robust mixture regression modeling using the least trimmed squares (LTS)-estimation method

Mixture regression models are used to investigate the relationship between variables that come from unknown latent groups and to model heterogenous datasets. In general, the error terms are assumed to be normal in the mixture regression model. However, the estimators under normality assumption are sensitive to the outliers. In this article, we introduce a robust mixture regression procedure based on the LTS-estimation method to combat with the outliers in the data. We give a simulation study and a real data example to illustrate the performance of the proposed estimators over the counterparts in terms of dealing with outliers.