A class of distributions connected to order statistics with nonintegral sample size

Newton's binomial series expansion is used to develop a (class of) distribution function(s) F_r:∝ which may be interpreted as the distribution of the r^thorder statistic with nonintegral sample size∝. It is shown that F_r:∝ has properties similar to F_r:n. Multivariate extension is considered and an elementary proof of the integral representation for the joint distribution of a subset of order statistics is given. An application is included.     

Estimation of Slope for Linear Regression Model with Uncertain Prior Information and Student-t Error

This article considers estimation of the slope parameter of the linear regression model with Student-t errors in the presence of uncertain prior information on the value of the unknown slope. Incorporating uncertain non sample prior information with the sample data the unrestricted, restricted, preliminary test, and shrinkage estimators are defined. The performances of the estimators are compared based on the criteria of unbiasedness and mean squared errors. Both analytical and graphical methods are explored. Although none of the estimators is uniformly superior to the others, if the non sample information is close to its true value, the shrinkage estimator over performs the rest of the estimators.     

A novel weighted likelihood estimation with empirical Bayes flavor

We propose a novel approach to estimation, where a set of estimators of a parameter is combined into a weighted average to produce the final estimator. The weights are chosen to be proportional to the likelihood evaluated at the estimators. We investigate the method for a set of estimators obtained by using the maximum likelihood principle applied to each individual observation. The method can be viewed as a Bayesian approach with a data-driven prior distribution. We provide several examples illustrating the new method and argue for its consistency, asymptotic normality, and efficiency. We also conduct simulation studies to assess the performance of the estimators. This straightforward methodology produces consistent estimators comparable with those obtained by the maximum likelihood method. The method also approximates the distribution of the estimator through the “posterior” distribution.     

罗兴亚难民：孟加拉国的安全困境

作为缅甸的少数民族,罗兴亚人是全球公民权扩张的最大受害者。缅甸当局拒不承认他们的公民权或财产权,因此数以万计的罗兴亚人被迫逃往孟加拉国,给该国原本就不充裕的资源分配带来极大的压力,同时难民与伊斯兰教极端组织之间的密切联系又威胁着孟加拉国的国家安全。作为这些难民的第一避难所,孟加拉国不可能不顾国际道义,忽视对罗兴亚人基本人权的保护,由此导致政府陷入安全困境。     

Economic and social inclusion of person with disability and transgender through skills development program of BRAC: A qualitative study

This article is based on the Skills Training for Advancing Resources (STAR) project for the youth initiative of the Bangladesh Rural Advancement Committee (BRAC) in Bangladesh. The objective was to explore the consequences of social and economic inclusion of this project for the persons with disability (PWDs) and transgender (TG) youth people. The research focused on how inclusive programmatic interventions created multidimensional impacts among the “marginalized” and “excluded” PWD and TG youths at the grassroots level. This study adopted a qualitative approach where in-depth case interviews and observation were applied for data collection. PWD and TG graduates, master craft persons (MCPs), employers, and program staff members were the participants in this study. Results found that PWD and TG youths faced vulnerabilities and social stigma in their lives and livelihood trajectories due to their physical inability and low level of social dignity. The STAR project has a certain level of contribution to the livelihoods of PWD and TG people, where these helped them to gain their social, cultural, and economic capital. Findings would be an important guideline for policymakers, NGO managers, and human rights workers.     

Stopping for efficacy in single-arm phase II clinical trials

Phase II clinical trials investigate whether a new drug or treatment has sufficient evidence of effectiveness against the disease under study. Two-stage designs are popular for phase II since they can stop in the first stage if the drug is ineffective. Investigators often face difficulties in determining the target response rates, and adaptive designs can help to set the target response rate tested in the second stage based on the number of responses observed in the first stage. Popular adaptive designs consider two alternate response rates, and they generally minimise the expected sample size at the maximum uninterested response rate. Moreover, these designs consider only futility as the reason for early stopping and have high expected sample sizes if the provided drug is effective. Motivated by this problem, we propose an adaptive design that enables us to terminate the single-arm trial at the first stage for efficacy and conclude which alternate response rate to choose. Comparing the proposed design with a popular adaptive design from literature reveals that the expected sample size decreases notably if any of the two target response rates are correct. In contrast, the expected sample size remains almost the same under the null hypothesis.     

Mixtures of autoregressive-autoregressive conditionally heteroscedastic models: semi-parametric approach

We propose data generating structures which can be represented as a mixture of autoregressive-autoregressive conditionally heteroscedastic models. The switching between the states is governed by a hidden Markov chain. We investigate semi-parametric estimators for estimating the functions based on the quasi-maximum likelihood approach and provide sufficient conditions for geometric ergodicity of the process. We also present an expectation–maximization algorithm for calculating the estimates numerically.     

American families: policy issues

"Increases in the number of children living in single-parent (usually female-headed) households and in the proportion of mothers who work outside their homes have raised concern in the United States about the effects of these trends on the well-being of children and the possible need for policy intervention. This paper discusses the arguments for and against policies that affect families. We review a number of such policies and what research suggests about their likely effects. The policies discussed...include those concerning child support, welfare, income taxes, child and dependent care, family leave, family planning, programs to improve parenting skills and family function, and economic growth."     

Model-based hierarchical clustering with Bregman divergences and Fishers mixture model: application to depth image analysis

Model-based clustering is a method that clusters data with an assumption of a statistical model structure. In this paper, we propose a novel model-based hierarchical clustering method for a finite statistical mixture model based on the Fisher distribution. The main foci of the proposed method are: (a) provide efficient solution to estimate the parameters of a Fisher mixture model (FMM); (b) generate a hierarchy of FMMs and (c) select the optimal model. To this aim, we develop a Bregman soft clustering method for FMM. Our model estimation strategy exploits Bregman divergence and hierarchical agglomerative clustering. Whereas, our model selection strategy comprises a parsimony-based approach and an evaluation graph-based approach. We empirically validate our proposed method by applying it on simulated data. Next, we apply the method on real data to perform depth image analysis. We demonstrate that the proposed clustering method can be used as a potential tool for unsupervised depth image analysis.     

On prediction from the location-scale model with a compound error distribution

The prediction distribution of future response(s) given a set of data from a location-scale model with a compound error distribution has been derived by utilizing the structural relations of the model. The compound error distribution has been specialized to cover the case of multivariate t-distribution.