An optimization approach applied to the optimal stratification problem

A new optimization algorithm is presented to solve the stratification problem. Assuming the number L of strata and the total sample size n are fixed, we obtain strata boundaries by using an objective function associated with the variance. In this problem, strata boundaries must be determined so that the elements in each stratum are more homogeneous among themselves. To produce more homogeneous strata, this paper proposes a new algorithm that uses the Greedy Randomized Adaptive Search Procedure (GRASP) methodology. Computational results are presented for a set of problems, with the application of the new algorithm and some algorithms from literature.     

Some asymptotic results for the integrated empirical process with applications to statistical tests

The main purpose of this paper is to investigate the strong approximation of the integrated empirical process. More precisely, we obtain the exact rate of the approximations by a sequence of weighted Brownian bridges and a weighted Kiefer process. Our arguments are based in part on the Komlós et al. (1975 Komlós, J., Major, P., Tusnády, G. (1975). An approximation of partial sums of independent RV's and the sample DF. I. Z. Wahrscheinlichkeitstheorie und Verw. Gebiete 32:111–131.[Crossref], [Web of Science ®] , [Google Scholar])'s results. Applications include the two-sample testing procedures together with the change-point problems. We also consider the strong approximation of the integrated empirical process when the parameters are estimated. Finally, we study the behavior of the self-intersection local time of the partial-sum process representation of the integrated empirical process.     

Assistant professors of color confront the inequitable terrain of academia: a community cultural wealth perspective

Abstract

This qualitative study adopted Yosso’s community cultural wealth (CCW) framework to examine how 16 assistant professors of color (APOC) drew upon various forms of capital (navigational, aspirational, social, resistant, linguistic, familial) to deal with racism and marginalization in academia. Findings revealed how APOC: dealt with students’ stereotypes of them, maintained their authentic selves to make academia more accessible and relevant, persevered with integrity despite hostility or marginalization, self-advocated for quality mentorship, and engaged in strategic service while avoiding cultural taxation and tokenism. Findings highlighted the positive cultural assets APOC enact within the academy while reiterating the need to address racist and marginalizing policies and practices in higher education. Variations in experiences based on gender and international status that can be explored further in future research also emerged. Working at a Hispanic-serving institution (HSI) also did not eliminate or lessen racist or marginalizing experiences for participants.     

Medicaid expansion and ethnoracial disparities in health insurance coverage

ABSTRACT

Medicaid expansion, as part of the Patient Protection and Affordable Care Act (PPACA), was touted by many policymakers as a potentially powerful force for reducing ethnoracial disparities in health insurance coverage. Using a unique county-level dataset at two time points (before and after the passage of the PPACA), we test whether Medicaid Expansion predicted change in ethnoracial disparities. Specifically, we use fixed-effect regression models to predict the Black-white, Hispanic-white, and Asian-white disparities in health insurance coverage. Using states that did not expand Medicaid as a counterfactual, our models show that this policy expansion was not significantly associated with a decrease in ethnoracial health insurance disparities. Overall, these findings suggest the need for scholars and policymakers to be more cautious and tempered about Medicaid expansion’s relationship with ethnoracial disparities in health insurance coverage. We discuss the implications of these findings for research on ethnoracial health insurance disparities, as well as policy efforts to increase coverage in minority communities.     

广东省劳动年龄人口变动特征及其成因分析

本文描述2000年以来广东省劳动年龄人口的变动特征,并探讨了产生变动的人口学因素。研究显示,广东省劳动年龄人口的比例在下降,年龄结构趋于老化,受教育年限在提高,就业人口总量逐年上升,但增速明显放缓。就业人口集中在第二、三产业。省内各区域劳动年龄人口比重处于较高水平,区域内比重的差距在减少,说明人口流动更加合理有序。研究显示,广东省劳动年龄人口的变动受到常住人口规模、人口转变、人口迁移流动和劳动参与率等因素的影响。     

用户在线知识付费研究综述

互联网知识经济时代,在线知识付费平台因能帮助用户解决知识过载带来的知识焦虑等问题,受到了用户的高度关注,得到了极大发展。通过总结国内外用户在线知识付费研究取得的成果与存在的不足,发现已有研究中关于用户在线知识付费的类型、途径等方面的研究还较为欠缺。未来研究可基于在线知识付费平台参与者视角深入探讨相关问题,包括用户视角、平台视角、知识供给者视角和关系视角。     

Are There Gender-based Effects for an Educational Program for Parents of Adolescents With Risk Behaviors?

The goals of this study were to evaluate the gender-based effectiveness of the parental intervention of the Suspertu (“recovery” in English) prevention program for adolescents with risk behaviors in Spain. The sample included 229 parents (105 fathers and 124 mothers). One open-label trial with repeated measures was used. Parents received a family therapy and a group education. The intervention improved parents’ authoritative parenting style, decreasing the degree of parental stress and reducing parent’s psychopathological symptoms and maladjustment. Improvement in both groups (fathers and mothers) was mainly observed from pre- to post-intervention assessment. According to the results, prevention programs for adolescents with risk behaviors may be a suitable context both to teach parents to deal appropriately with their children’s risk behaviors, and to improve parent’s psychological state.     

Identification of average marginal effects under misspecification when covariates are normal

A previously known result in the econometrics literature is that when covariates of an underlying data generating process are jointly normally distributed, estimates from a nonlinear model that is misspecified as linear can be interpreted as average marginal effects. This has been shown for models with exogenous covariates and separability between covariates and errors. In this paper, we extend this identification result to a variety of more general cases, in particular for combinations of separable and nonseparable models under both exogeneity and endogeneity. So long as the underlying model belongs to one of these large classes of data generating processes, our results show that nothing else must be known about the true DGP—beyond normality of observable data, a testable assumption—in order for linear estimators to be interpretable as average marginal effects. We use simulation to explore the performance of these estimators using a misspecified linear model and show they perform well when the data are normal but can perform poorly when this is not the case.     

On the one parameter unit-Lindley distribution and its associated regression model for proportion data

In this paper considering an appropriate transformation on the Lindley distribution, we propose the unit-Lindley distribution and investigate some of its statistical properties. An important fact associated with this new distribution is that it is possible to obtain the analytical expression for bias correction of the maximum likelihood estimator. Moreover, it belongs to the exponential family. This distribution allows us to incorporate covariates directly in the mean and consequently to quantify their influences on the average of the response variable. Finally, a practical application is presented to show that our model fits much better than the Beta regression.     

Optimal exact design of double acceptance sampling plans by attributes

The design of double acceptance sampling (AS) plans for attributes based on the operating characteristic curve paradigm is usually addressed by enumeration algorithms. These AS plans may be non optimal regarding the sample size to inspect as they were obtained without the requirement that the constraints at the OC curve controlled points are not violated for minimum Average Sample Number (ASN) scenarios. An approach based on mathematical programming is proposed to systematically design double AS plans for attributes, where the characteristics controlled are modelled by binomial or Poisson distributions. Specifically, Mixed Integer Nonlinear Programming (MINLP) formulations are developed and combined with an enumeration algorithm that allows finding ASN minimax optimal plans. A theoretical result is developed with the purpose of assuring the global optimum design is reached by iteration where a convenient solver is used to find local optima. To validate the algorithm, we compare our results with those of tables commonly used for practical purposes, consider different rates of risk, and setups commonly used in Lot Quality Assurance Plans (LQAS) for health monitoring programmes. Finally, we compare AS plans determined for processes described by binomial and Poisson distributions.