Hierarchical bayesian curve fitting and smoothing

Estimation of a smooth function is considered when observations on this function added with Gaussian errors are observed. The problem is formulated as a general linear model, and a hierarchical Bayesian approach is then used to study it. Credible bands are also developed for the function. Sensitivity analysis is conducted to determine the influence of the choice of priors on hyperparameters. Finally, the methodology is illustrated using real and simulated examples where it is compared with classical cubic splines. It is also shown that our approach provides a Bayesian solution to some problems in discrete time series.     

A protective service system

This article presents the results of a small scale study of the protective service caseload of a mid-western metropolitan area. The basic characteristics of the caseload match the national picture presented by the American Humane Association's National Study on Child Abuse and Neglect. The present study focused on the degree to which “systematic” vs. “random” forces appear to be at work in the progress of cases through the system. The overall picture is of some systematic efforts to conduct the process of referrals to community agencies but of a great deal of “randomness” in the bureaucratic closing of cases. This is a major area of concern.     

A novel feature selection scheme for high-dimensional data sets: four-Staged Feature Selection

Classification of high-dimensional data set is a big challenge for statistical learning and data mining algorithms. To effectively apply classification methods to high-dimensional data sets, feature selection is an indispensable pre-processing step of learning process. In this study, we consider the problem of constructing an effective feature selection and classification scheme for data set which has a small number of sample size with a large number of features. A novel feature selection approach, named four-Staged Feature Selection, has been proposed to overcome high-dimensional data classification problem by selecting informative features. The proposed method first selects candidate features with number of filtering methods which are based on different metrics, and then it applies semi-wrapper, union and voting stages, respectively, to obtain final feature subsets. Several statistical learning and data mining methods have been carried out to verify the efficiency of the selected features. In order to test the adequacy of the proposed method, 10 different microarray data sets are employed due to their high number of features and small sample size.     

Poverty Among The Elderly: An Application of a Collective Consumption Model in Brazil

Social Indicators Research - The growth of the elderly population and changes in household composition raise important questions regarding the level of well-being of the elderly in developing...     

Trajectories of Breadth of Participation in Organized Activity During Childhood

This study aimed to identify the trajectories of breadth of participation in organized activities during childhood and to examine the predictors of membership in these trajectories (child's individual and family characteristics measured in Kindergarten). A sample of 1038 children, recruited in Kindergarten, was assessed yearly between Kindergarten and grade 4. Semiparametric group‐based modeling brought out four trajectories: the no participation group (13.5 percent), the increasing group (26.4 percent), the decreasing group (14.1 percent), and the high group (46.1 percent). Prosociality predicted membership in the no participation group, as compared with the increasing group. Social withdrawal predicted membership in the no participation group, as compared with the high group. High family income and higher maternal education predicted membership in the increasing, decreasing, and high trajectory groups, as compared with the no participation group. Higher paternal education predicted membership in the high group, as compared with the increasing group. Overall, family variables had a greater impact than individual variables on the probability that the child would participate in a broader range of organized activities.     

Zero-inflated compound Poisson distributions in integer-valued GARCH models

In this paper we introduce a wide class of integer-valued stochastic processes that allows to take into consideration, simultaneously, relevant characteristics observed in count data namely zero inflation, overdispersion and conditional heteroscedasticity. This class includes, in particular, the compound Poisson, the zero-inflated Poisson and the zero-inflated negative binomial INGARCH models, recently proposed in literature. The main probabilistic analysis of this class of processes is here developed. Precisely, first- and second-order stationarity conditions are derived, the autocorrelation function is deduced and the strict stationarity is established in a large subclass. We also analyse in a particular model the existence of higher-order moments and deduce the explicit form for the first four cumulants, as well as its skewness and kurtosis.     

Comparisons of the alternative biased estimators for the distributed lag models

The finite distributed lag models include highly correlated variables as well as lagged and unlagged values of the same variables. Some problems are faced for this model when applying the ordinary least squares (OLS) method or econometric models such as Almon and Koyck models. The primary aim of this study is to compare performances of alternative estimators to the OLS estimator defined by combining the Almon estimator with some estimators using Almon (1965) data. A simulation study with different model parameters is performed and the estimators are compared according to the root mean square error (RMSE) and prediction mean square error (PMSE).     

The Impact of the Within My Reach Relationship Training on Relationship Skills and Outcomes for Low‐Income Individuals

A federal grant was awarded to provide the Within My Reach healthy relationships curriculum to low‐income, at‐risk individuals involved with various social service agencies. The effectiveness of this curriculum was evaluated for 202 participants through measures of training and relationship outcomes pre‐, immediately post‐ and 6 months posttraining. Participants experienced high levels of training satisfaction; significant increases in knowledge, communication/conflict resolution skills, and relationship quality; as well as a trend in the reduction of relationship violence. An important implication of this research is that MFTs may broaden their service delivery to at‐risk individuals by collaborating with community agencies to adapt established relationship enhancement programs, evidence‐based tools, and principles that complement traditional couples therapy.