Paternal and Household Characteristics Associated With Child Neglect and Child Protective Services Involvement

ABSTRACT

This study examined the association of paternal and household characteristics with household-level measures of child neglect and Child Protective Services (CPS) involvement, measured when the index child was 5 years of age. Secondary analyses of the Fragile Families and Child Well-Being Study were conducted using a subsample of 1,089 residential, biological fathers. Logistic regression models indicated that paternal depression was associated with greater than doubled odds of child neglect and CPS involvement. Paternal alcohol use and parenting stress were associated with approximately 50% increased odds for child neglect, and a scale measuring 13 caregiving tasks to reflect positive father involvement with the child was also associated with less risk for child neglect. However, paternal alcohol use, parenting stress, and positive involvement with the child were not associated with CPS involvement. An implication of this study is that paternal psychosocial functioning is important to consider in conjunction with sociodemographic factors when examining maltreatment risk in two-parent families.     

The Effects of Parenting Stress,Perceived Mastery,and Maternal Depression on Parent–Child Interaction

ABSTRACT

The purpose of this study was to test a model that delineated the interrelationships among parenting stress, perceived mastery (defined as the belief that one's life chances are under one's own control), maternal depression, and parent–child interaction. A secondary data analysis of the Fragile Family and Child Well-Being Study was conducted, using a sample of 2,650 mothers. Results suggest that parenting stress undermines one's sense of perceived mastery which, in turn, resulted in depression. Moreover, the results also indicated that parenting stress directly affected maternal depression and parent–child interaction. There was no support for the hypothesis that parenting stress undermines one's sense of perceived mastery which, in turn, resulted in depression, which affected one's parent–child interaction. Implications of these findings for professionals working with mothers of young children are discussed. Future research should test this model using other forms of parenting behavior.     

Poverty,income inequality and externalised problem behaviours of children: an empirical study in Korea

The primary purpose of this study is to examine the effects of neighbourhood poverty and income inequality on children's externalised problem behaviours in Korea. For the analysis, we used the National Survey of Children and Youth data and employed a multilevel analysis method. The results show that income inequality level of a community is a significant factor in increasing children's externalised problem behaviours. When controlled for income inequality, poverty itself was not significantly related to the level of children's externalised problem behaviours. We provide social welfare practice and policy implications of the findings in the paper.     

Several biplot methods applied to gene expression data

DNA microarray experiments result in enormous amount of data, which need careful interpretation. Biplot approaches show simultaneous display of genes and samples in low-dimensional graphs and thus can be used to represent the relationships between genes and samples. There are several different types of biplots, and these methods need to be evaluated because each plot provides different result.     

A Locally Most Mean Powerful Based Score Test for ARCH and GARCH Regression Disturbances

This article considers the twin problems of testing for autoregressive conditional heteroscedasticity (ARCH) and generalized ARCH disturbances in the linear regression model. A feature of these testing problems, ignored by the standard Lagrange multiplier test, is that they are onesided in nature. A test that exploits this one-sided aspect is constructed based on the sum of the scores. The small-sample-size and power properties of two versions of this test under both normal and leptokurtic disturbances are investigated via a Monte Carlo experiment. The results indicate that both versions of the new test typically have superior power to two versions of the Lagrange multiplier test and possibly also more accurate asymptotic critical values.     

Alternative estimator for the parameters of a mixture of two binomial distributions

The method of moments has been widely used as a simple alternative to the maximum likelihood method, mainly because of its efficiency and simplicity in obtaining parameter estimators of a mixture of two binomial distributions. In this paper, an alternative estimate is proposed which is as competitive as of the method of moments when comparing the mean squared error and computational effort.     

Bayesian Estimation and Prediction for the Beta-Binomial Model

The beta-binomial distribution, which is generated by a simple mixture model, has been widely applied in the social, physical, and health sciences. Problems of estimation, inference, and prediction have been addressed in the past, but not in a Bayesian framework. This article develops Bayesian procedures for the beta-binomial model and, using a suitable reparameterization, establishes a conjugate-type property for a beta family of priors. The transformed parameters have interesting interpretations, especially in marketing applications, and are likely to be more stable. More specifically, one of these parameters is the market share and the other is a measure of the heterogeneity of the customer population. Analytical results are developed for the posterior and prediction quantities, although the numerical evaluation is not trivial. Since the posterior moments are more easily calculated, we also propose the use of posterior approximation using the Pearson system. A particular case (when there are two trials), which occurs in taste testing, brand choice, media exposure, and some epidemiological applications, is analyzed in detail. Simulated and real data are used to demonstrate the feasibility of the calculations. The simulation results effectively demonstrate the superiority of Bayesian estimators, particularly in small samples, even with uniform (“non-informed”) priors. Naturally, “informed” priors can give even better results. The real data on television viewing behavior are used to illustrate the prediction results. In our analysis, several problems with the maximum likelihood estimators are encountered. The superior properties and performance of the Bayesian estimators and the excellent approximation results are strong indications that our results will be potentially of high value in small sample applications of the beta-binomial and in cases in which significant prior information exists.     

Interaction Between Autocorrelation and Conditional Heteroscedasticity: A Random-Coefficient Approach

In applied econometrics, we tend to tackle specification problems one at a time rather than considering them jointly. This has serious consequences for statistical inference. One example of this is considering autocorrelation and autoregressive conditional heteroscedasticity (ARCH) separately. In this article we consider a linear regression model with random coefficient autoregressive disturbances that provides a convenient framework to analyze autocorrelation and ARCH simultaneously. Our stationarity conditions and testing results reveal the strong interaction between ARCH and autocorrelation. An empirical example of testing the unbiasedness of experts' expectations of inflation demonstrates that neglecting conditional heteroscedasticity or misspecifying the autocorrelation structure might result in unreliable inference.     

Stability of noisy Metropolis–Hastings

Pseudo-marginal Markov chain Monte Carlo methods for sampling from intractable distributions have gained recent interest and have been theoretically studied in considerable depth. Their main appeal is that they are exact, in the sense that they target marginally the correct invariant distribution. However, the pseudo-marginal Markov chain can exhibit poor mixing and slow convergence towards its target. As an alternative, a subtly different Markov chain can be simulated, where better mixing is possible but the exactness property is sacrificed. This is the noisy algorithm, initially conceptualised as Monte Carlo within Metropolis, which has also been studied but to a lesser extent. The present article provides a further characterisation of the noisy algorithm, with a focus on fundamental stability properties like positive recurrence and geometric ergodicity. Sufficient conditions for inheriting geometric ergodicity from a standard Metropolis–Hastings chain are given, as well as convergence of the invariant distribution towards the true target distribution.