Strong law of large numbers and complete convergence for non-identically distributed WOD random variables

In this paper, we establish the strong law of large numbers and complete convergence for non-identically distributed WOD random variables. We derive some new inequalities of Fuk–Nagaev type for the sums of non-identically distributed WD random variables. All these results further extend and refine previous ones.     

Optimal designs for multivariate logistic mixed models with longitudinal data

This paper considers the optimal design problem for multivariate mixed-effects logistic models with longitudinal data. A decomposition method of the binary outcome and the penalized quasi-likelihood are used to obtain the information matrix. The D-optimality criterion based on the approximate information matrix is minimized under different cost constraints. The results show that the autocorrelation coefficient plays a significant role in the design. To overcome the dependence of the D-optimal designs on the unknown fixed-effects parameters, the Bayesian D-optimality criterion is proposed. The relative efficiencies of designs reveal that both the cost ratio and autocorrelation coefficient play an important role in the optimal designs.     

Regression analysis of case-cohort studies in the presence of dependent interval censoring

The case-cohort design is widely used as a means of reducing the cost in large cohort studies, especially when the disease rate is low and covariate measurements may be expensive, and has been discussed by many authors. In this paper, we discuss regression analysis of case-cohort studies that produce interval-censored failure time with dependent censoring, a situation for which there does not seem to exist an established approach. For inference, a sieve inverse probability weighting estimation procedure is developed with the use of Bernstein polynomials to approximate the unknown baseline cumulative hazard functions. The proposed estimators are shown to be consistent and the asymptotic normality of the resulting regression parameter estimators is established. A simulation study is conducted to assess the finite sample properties of the proposed approach and indicates that it works well in practical situations. The proposed method is applied to an HIV/AIDS case-cohort study that motivated this investigation.     

A primal dual active set with continuation algorithm for high-dimensional nonconvex SICA-penalized regression

The smooth integration of counting and absolute deviation (SICA) penalty has been demonstrated theoretically and practically to be effective in nonconvex penalization for variable selection and parameter estimation. However, solving the nonconvex optimization problem associated with SICA penalty in high-dimensional setting remains to be enriched, mainly due to the singularity at the origin and the nonconvexity of the SICA penalty function. In this paper, we develop a fast primal dual active set (PDAS) with continuation algorithm for solving the nonconvex SICA-penalized least squares in high dimensions. Upon introducing the dual variable, the PDAS algorithm iteratively identify and update the active set in the optimization using both the primal and dual information, and then solve a low-dimensional least square problem on the active set. When combined with a continuation strategy and a high-dimensional Bayesian information criterion (BIC) selector on the tuning parameters, the proposed algorithm is very efficient and accurate. Extensive simulation studies and analysis of a high-dimensional microarray gene expression data are presented to illustrate the performance of the proposed method.     

HDDA: DataSifter: statistical obfuscation of electronic health records and other sensitive datasets

There are no practical and effective mechanisms to share high-dimensional data including sensitive information in various fields like health financial intelligence or socioeconomics without compromising either the utility of the data or exposing private personal or secure organizational information. Excessive scrambling or encoding of the information makes it less useful for modelling or analytical processing. Insufficient preprocessing may compromise sensitive information and introduce a substantial risk for re-identification of individuals by various stratification techniques. To address this problem, we developed a novel statistical obfuscation method (DataSifter) for on-the-fly de-identification of structured and unstructured sensitive high-dimensional data such as clinical data from electronic health records (EHR). DataSifter provides complete administrative control over the balance between risk of data re-identification and preservation of the data information. Simulation results suggest that DataSifter can provide privacy protection while maintaining data utility for different types of outcomes of interest. The application of DataSifter on a large autism dataset provides a realistic demonstration of its promise practical applications.     

Does severity matter?: An investigation of crisis severity from defensive attribution theory perspective

Although the concept of crisis severity has existed in the crisis communication literature for two decades, it has not been theoretically tested and explained. Based on Defensive Attribution Theory (DAT), this study intends to specify the relationship between crisis severity and crisis responsibility as well as organizational reputation within the SCCT model. Through a 2 (crisis severity) × 3 (crisis type) between-subject factorial experiment (n?=?274), the study revealed that higher crisis severity causes worse reputation regardless of crisis type. For accidental crisis, higher crisis severity leads to higher intentionality, one dimension of crisis responsibility. The findings indicate that crisis severity still has value in SCCT and deserves further investigation.     

Economic Inequality is Associated with Lower Internet Use: A Nationally Representative Study

Social Indicators Research - Previous studies have documented the associations of objective class with either social capital or subjective social status, yet little attention has been paid to the...     

话语重塑与概念流变:从中华民族到中华民族共同体

准确梳理和认识从中华民族到中华民族共同体的概念流变,有利于准确理解中华民族共同体的内涵,是铸牢中华民族共同体意识的基础性工作.中华民族概念是在近代中国内忧外患的民族危机和话语重塑中产生和发展的,经历了"大汉族主义""五族共和""国族主义"等内涵演变.中华人民共和国成立后,民族区域自治制度和民族政策趋向于关注和关怀少数民族"多元",中华民族概念在相当长的时期内被虚化.新时代以来,习近平提出的"中华民族共同体"是对"中华民族""中华各民族""中国各民族"等概念的更高层次表达,是中华民族概念的最优表达.     

Vocational Experiences and Quality of Life of Migrants: Overview and Future Research

Quality of life is a complicated concept that has attracted broad attention from multiple disciplines. This article introduces a set of studies that employ diverse approaches to explore how work, vocational, and career experiences shape quality of life among migrants, and proposes a future agenda to extend this stream of research. Specifically, operationalizing quality of life in different ways, research showcased in this special issue has demonstrated how international and intranational migrants achieve, maintain, and enhance their well-being in different life domains. Reflecting on the literature and the showcased studies, this article proposes that future research should advance the current inquiry by better categorizing vocational experiences, uniquely operationalizing of quality of life in migration contexts, taking a more culturally sensitive perspective, and broadening methodological approaches.