Con Job: An Estimate of Ex‐Felon Voter Turnout Using Document‐Based Data*

Objective. Ex‐felon voter turnout was estimated for the first time using government records rather than statistical models. Statistical models have estimated that 25–35 percent of eligible ex‐felons would vote in federal elections. Methods. Six‐hundred‐sixty recently released ex‐felons in Erie County, NY, who would have been legally eligible to register and vote in 2004 or 2005, were compared with data from the Erie County Board of Elections to determine whether they registered and voted in either 2004 or 2005. Results. Five percent this population of ex‐felons voted in either 2004 or 2005. Conclusions. Single‐digit turnout among ex‐felons raises questions about the assumptions underlying statistical estimates, and it also suggests that elections would have to be very close for ex‐felons to have an impact on the results.     

Inference in Semi‐Parametric Dynamic Models for Repeated Count Data

This paper deals with a longitudinal semi‐parametric regression model in a generalised linear model setup for repeated count data collected from a large number of independent individuals. To accommodate the longitudinal correlations, we consider a dynamic model for repeated counts which has decaying auto‐correlations as the time lag increases between the repeated responses. The semi‐parametric regression function involved in the model contains a specified regression function in some suitable time‐dependent covariates and a non‐parametric function in some other time‐dependent covariates. As far as the inference is concerned, because the non‐parametric function is of secondary interest, we estimate this function consistently using the independence assumption‐based well‐known quasi‐likelihood approach. Next, the proposed longitudinal correlation structure and the estimate of the non‐parametric function are used to develop a semi‐parametric generalised quasi‐likelihood approach for consistent and efficient estimation of the regression effects in the parametric regression function. The finite sample performance of the proposed estimation approach is examined through an intensive simulation study based on both large and small samples. Both balanced and unbalanced cluster sizes are incorporated in the simulation study. The asymptotic performances of the estimators are given. The estimation methodology is illustrated by reanalysing the well‐known health care utilisation data consisting of counts of yearly visits to a physician by 180 individuals for four years and several important primary and secondary covariates.     

Diagnostics in elliptical regression models with stochastic restrictions applied to econometrics

We propose an influence diagnostic methodology for linear regression models with stochastic restrictions and errors following elliptically contoured distributions. We study how a perturbation may impact on the mixed estimation procedure of parameters in the model. Normal curvatures and slopes for assessing influence under usual schemes are derived, including perturbations of case-weight, response variable, and explanatory variable. Simulations are conducted to evaluate the performance of the proposed methodology. An example with real-world economy data is presented as an illustration.     

The Impact of Learning and Labor Attrition on Worker Flexibility in Dual Resource Constrained Job Shops

This study investigates the impact of worker learning, worker flexibility, and labor attrition on the system performance of a dual resource constrained (DRC) job-shop. The effects of learning and labor attrition have not been previously addressed in DRC literature. Results from the study, consistent with previous literature, show that the greatest benefits are achieved when inter-departmental worker flexibility is incrementally introduced into the system. In addition, the learning environment, which depends on the initial processing time of jobs and the learning rates of workers, is shown to impact the acquisition of flexibility. The study also shows that the impact of labor attrition on system performance under certain shop conditions may be significant.     

Household Assets,the Role of Government Assistance,and Depression Among Low-Income Families in Shanghai

As China’s economy is rapidly changing from a planned to a capitalist economy, many families find themselves financially struggling. In some cases, conflicting values and attitudes may contribute to mental health challenges such as depression that would lead to further feelings of helplessness and immobilization. Using a random sample of 1006 low-income households from Pudong District of Shanghai, China, this study aims to examine the relationships between household assets, beliefs about government as the primary way to improve economic circumstances and self-reported depressive symptoms. In addition, this study investigates the mediation effects of beliefs that government is the best change agent for improved life circumstances on the relationship between household assets and depression. We found those who indicated that government was the main means for attaining a better life had significantly higher depression levels whereas higher numbers of household assets were associated with lower depression levels. We also found that viewing government as the most important change agent only partially mediated the relationship between household assets and depression (p?<?.001). Findings from this study support anti-poverty policies and social work related practice initiatives aimed at assisting low income families in China, in particular the need to address psychological as well as economic needs.

     

Building home on a fault line: Aging out of child welfare with a serious mental health diagnosis

Youth aging out of foster care are at high risk for homelessness. This research explains how and why homelessness occurs among youth with serious mental health struggles after aging out of residential and transitional living programmes. Using a longitudinal constructivist grounded theory design, we analyse 20 in‐depth interviews exploring youth's transition experiences and perceptions of transition success within 4 months of emancipation and at 6 and 12 months postemancipation. Monthly 15‐min check‐ins prevented attrition. A three‐phase transition process was observed driven by participant desires and actions to cultivate psychological home through establishing physical home alone away from social networks. Pre‐emancipation, the promise of home was privacy and opportunity for self‐redefinition. Shortly after emancipation, home became a physical and social space to be, a valuable resource to share and a demonstration of personal transition success. However, later in the year postemancipation, maintaining physical home proves nearly impossible given the risk context. Findings speak to how and why youth aging out appear overly self‐reliant upon emancipation. Cultivating a psychological sense of home has practice implications for child welfare providers who aim to prepare youth to live independently postemancipation.     

Testing the dilation order by using cumulative residual Tsallis entropy

In this paper, we introduce a new test for the dilation order based on cumulative residual Tsallis entropy of order α. The effect of the values of parameter α on the power of the test statistics is numerically investigated. The asymptotic distribution of the test statistic is given. The performance of the test statistic is evaluated using a simulation study. Finally, some numerical examples illustrating the theory are also given.     

A semi-parametric method for estimating the beta coefficients of the hidden two-sided asset return jumps

ABSTRACT

We introduce a new methodology for estimating the parameters of a two-sided jump model, which aims at decomposing the daily stock return evolution into (unobservable) positive and negative jumps as well as Brownian noise. The parameters of interest are the jump beta coefficients which measure the influence of the market jumps on the stock returns, and are latent components. For this purpose, at first we use the Variance Gamma (VG) distribution which is frequently used in modeling financial time series and leads to the revelation of the hidden market jumps' distributions. Then, our method is based on the central moments of the stock returns for estimating the parameters of the model. It is proved that the proposed method provides always a solution in terms of the jump beta coefficients. We thus achieve a semi-parametric fit to the empirical data. The methodology itself serves as a criterion to test the fit of any sets of parameters to the empirical returns. The analysis is applied to NASDAQ and Google returns during the 2006–2008 period.     

Communication in Statistics-Theory and methods improved GQL estimation method for the generalised BINMA(1) model

In a recent research, the quasi-likelihood estimation methodology was developed to estimate the regression effects in the Generalized BINMA(1) (GBINMA(1)) process. The method provides consistent parameter estimates but, in the intermediate computations, moment estimating equations were used to estimate the serial- and cross-correlation parameters. This procedure may not result optimal parameter estimates, in particular, for the regression effects. This paper provides an alternative simpler GBINMA(1) process based on multivariate thinning properties where the main effects are estimated via a robust generalized quasi-likelihood (GQL) estimation approach. The two techniques are compared through some simulation experiments. A real-life data application is studied.