ETHNIC ENCLOSURE OR ETHNIC COMPETITION: Ethnic Identification Among Hispanics in Texas

Recent theoretical debates have focused on the utility of two alternative perspectives on ethnic identification. However, empirical support for either is neither consistent nor conclusive and previous analyses seldom conceptually or empirically examine the aggregate macrostructural determinants of ethnic identification. This study reevaluates the relative merits of the ethnic enclosure and ethnic competition perspectives among Texan Hispanics using aggregate 1980 Census of Population data for 139 Texas cities with large Hispanic populations. The rejection of the ethnic enclosure thesis in some recent research is unwarranted, as the empirical evidence provides it substantial support and little for the ethnic competition perspective. Further analyses of structural factors in ethnic identification are merited.     

Workplace support and European fathers’ use of state policies promoting shared childcare

ABSTRACT

Social policies such as paternity leave and parental leave offer fathers the opportunity to be more involved in childcare than earlier generations of fathers. While such policies are increasingly offered by governments around the world, research by the International Network on Leave Policies and Research shows that many European fathers do not take advantage of these benefits, despite fathers’ growing interest in participation in early childcare. This article introduces a special issue devoted to understanding how the workplace can impact European fathers’ interest in and abilities to take leave, a topic that has received relatively little research attention. The articles in the special issue suggest that barriers to European fathers’ leavetaking are deeply embedded in workplace culture and work practices and will be difficult to eradicate without a dramatic challenge to the concept of the male ideal worker, who prioritizes work above family.     

Influential subsets on the variable selection

When one or few observations are deleted in the multiple linear regression model, they can affect the variable selection. In this paper we derived the formula for the Mallows C_p criterion when k observations are deleted and express it as a functionn of basic building blocks such as residuals and leverages. Also, two real date sets are used to see how the selected model changes as few observations re deleted.     

Estimating the number of true null hypotheses in multiple hypothesis testing

The overall Type I error computed based on the traditional means may be inflated if many hypotheses are compared simultaneously. The family-wise error rate (FWER) and false discovery rate (FDR) are some of commonly used error rates to measure Type I error under the multiple hypothesis setting. Many controlling FWER and FDR procedures have been proposed and have the ability to control the desired FWER/FDR under certain scenarios. Nevertheless, these controlling procedures become too conservative when only some hypotheses are from the null. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) proposed an adaptive FDR-controlling procedure that adapts the information of the number of true null hypotheses (m ₀) to overcome this problem. Since m ₀ is unknown, estimators of m ₀ are needed. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) suggested a graphical approach to construct an estimator of m ₀, which is shown to overestimate m ₀ (see Hwang in J. Stat. Comput. Simul. 81:207–220, 2011). Following a similar construction, this paper proposes new estimators of m ₀. Monte Carlo simulations are used to evaluate accuracy and precision of new estimators and the feasibility of these new adaptive procedures is evaluated under various simulation settings.     

Confidence sets and the stein effect

The problem of improving upon the usual set estimator of a multivariate normal mean has only recently seen significant advances. Improved sets that take advantage of the Stein effect have been constructed. It is shown here that the Stein effect is so powerful that one can construct improved confidence sets that can have zero radius on a set of positive probability. Other, somewhat more sensible, sets which attain arbitrarily small radius are also constructed, and it is argued that one way to eliminate unreasonable confidence sets is through a conditional evaluation.     

A modified generalized lasso algorithm to detect local spatial clusters for count data

Detecting local spatial clusters for count data is an important task in spatial epidemiology. Two broad approaches—moving window and disease mapping methods—have been suggested in some of the literature to find clusters. However, the existing methods employ somewhat arbitrarily chosen tuning parameters, and the local clustering results are sensitive to the choices. In this paper, we propose a penalized likelihood method to overcome the limitations of existing local spatial clustering approaches for count data. We start with a Poisson regression model to accommodate any type of covariates, and formulate the clustering problem as a penalized likelihood estimation problem to find change points of intercepts in two-dimensional space. The cost of developing a new algorithm is minimized by modifying an existing least absolute shrinkage and selection operator algorithm. The computational details on the modifications are shown, and the proposed method is illustrated with Seoul tuberculosis data.     

Operator performance and subjective response in control of flexible manufacturing systems

In this study, allocation of functions and system size (number of machines) were manipulated to test their effects on operator performance and subjective response in a flexible manufacturing system (FMS). An experiment was conducted using a cross-over design with three independent variables: number of machines, task allocation, and sequence of task presentation. Operator performance was measured by response time, and subjective response assessed using two questionnaires. Physiological measures were also taken (EKG and respiratory rate). The results revealed that the number of machines in the system did not affect performance or subjective or physiological response while the effect of allocation level was significant for both performance and subjective response.