#FamiliesBelongTogether: Facts and Fictions of Race and Family in U.S. Immigration Policy

Family reunification is widely seen as a relatively stable feature of the contemporary U.S. immigration regime protected by the nation’s liberal democratic institutions and humanitarian values. Drawing on critical scholarship that situates immigration policies in racial nation‐building projects, this article explores the development of U.S. family‐based admission policies from 1965 to the early 2000s. I bring attention to the role of racial family logics in the changing character and meaning of these policies. Racial family logics reflect the emergent and contested ways in which families are both idealized and institutionally organized in relation to the state, the economy, and other social institutions to support racial projects. A normative conception of “the family” as a white, heterosexual, male wage earner, nuclear household unit informed the 1965 U.S. Immigration and Nationality Act and its emphasis on family‐based admissions. However, by the 1990s, the landscape of immigration, race, and family in the United States had shifted quite dramatically. The family‐based admissions system was now associated with immigrants from Asia and Latin America rather than Europe. The “browning” of the system was accompanied by its incorporation into racialized projects of state discipline, surveillance, and control over those deemed “undeserving” in relation to neoliberal values of self‐reliant and self‐regulating families.     

A new kind of stochastic restricted biased estimator for logistic regression model

In the logistic regression model, the variance of the maximum likelihood estimator is inflated and unstable when the multicollinearity exists in the data. There are several methods available in literature to overcome this problem. We propose a new stochastic restricted biased estimator. We study the statistical properties of the proposed estimator and compare its performance with some existing estimators in the sense of scalar mean squared criterion. An example and a simulation study are provided to illustrate the performance of the proposed estimator.KEYWORDS: Logistic regression, maximum likelihood estimator, mean squared error matrix, ridge regression, simulation study, stochastic restricted estimatorMathematics Subject Classifications: Primary 62J05, Secondary 62J07     

Preliminary test estimation of the parameters of exponential and Pareto distributions for censored samples

This paper deals with the preliminary test estimation (PTE) of the parameters of the exponential and Pareto distributions in censored samples. The biases, risk functions, efficiency tables and the graphs for the relative efficiency for the proposed estimators for the parameters of the exponential and Pareto distributions are given. We find that the proposed estimators dominate the corresponding unrestricted (usual) estimators in the neighborhood of null hypothesis. The range of the parameters for which the proposed estimators dominate the corresponding usual estimators for different sample sizes and level of significance are given. The findings of the paper will be useful for the practitioners who are dealing with the censored samples in life testing experiments.     

Social Work Practice for Social Development in Bangladesh: Issues and Challenges

Social work addresses social issues that constrain the betterment of a community. However, social work practice is struggling to deal effectively with the development challenges of Bangladesh. Based on a literature review and the experiences of the authors, this paper explores the emerging issues where social work practice is anticipated as a promising alternative for bringing sustainable social development in Bangladesh. Simultaneously, the paper discovers the limitation of social work practice in Bangladesh addressing these challenges.     

Simultaneous estimation of several CDF’s: homogeneity constraint

Let {x_ij(1 ? j ? n_i)|i = 1, 2, …, k} be k independent samples of size n_j from respective distributions of functions F_j(x)(1 ? j ? k). A classical statistical problem is to test whether these k samples came from a common distribution function, F(x) whose form may or may not be known. In this paper, we consider the complementary problem of estimating the distribution functions suspected to be homogeneous in order to improve the basic estimator known as “empirical distribution function” (edf), in an asymptotic setup. Accordingly, we consider four additional estimators, namely, the restricted estimator (RE), the preliminary test estimator (PTE), the shrinkage estimator (SE), and the positive rule shrinkage estimator (PRSE) and study their characteristic properties based on the mean squared error (MSE) and relative risk efficiency (RRE) with tables and graphs. We observed that for k ? 4, the positive rule SE performs uniformly better than both shrinkage and the unrestricted estimator, while PTEs works reasonably well for k < 4.     

The price-quality relationship: A heuristic model

An on-the-shop-floor experiment is carried out to determine the price-quality relationship for a number of toiletries. Three different and distinct demand curves are obtained. A heuristic model to describe these curves is constructed. The implications of this model for the profit-orientated businessman are then analysed.