Finite mixture of regression models for a stratified sample

Despite the popularity and importance, there is limited work on modelling data which come from complex survey design using finite mixture models. In this work, we explored the use of finite mixture regression models when the samples were drawn using a complex survey design. In particular, we considered modelling data collected based on stratified sampling design. We developed a new design-based inference where we integrated sampling weights in the complete-data log-likelihood function. The expectation–maximisation algorithm was developed accordingly. A simulation study was conducted to compare the new methodology with the usual finite mixture of a regression model. The comparison was done using bias-variance components of mean square error. Additionally, a simulation study was conducted to assess the ability of the Bayesian information criterion to select the optimal number of components under the proposed modelling approach. The methodology was implemented on real data with good results.     

A volume based approach to establish B-spline based expressions for density functions and its application to progressive hybrid censoring

We propose an approach to determine the distribution of particular linear combinations of hybrid censored order statistics which is based on the calculation of volumes of polytopes. For this purpose, we establish efficient and compact volume formulas in terms of B-splines. Further, we illustrate our approach for ten different progressive hybrid censoring schemes under an exponential assumption.     

死者人格利益法律保护之构建

死者无人格权,但死者具有人格利益。为了维护社会利益和善良风俗,我国法学界和司法实务界对死者人格利益保护也达成了共识。完善死者人格利益应在保护范围、行使主体、法律责任、免责事由及保护期限等方面给予立法规制。     

社会学民族志的力量:重返早期社会研究的田野工作

自20世纪70年代以来，社会学民族志在田野工作和叙事复兴中与传统人文学汇聚，显著扩展了自己的学科边界。但是，这种扩展并没有解决社会学民族志在深度的经验发生和强烈的理论诉求之间遭遇的紧张。对于这种紧张，本文主张再访早期社会研究的田野，即19世纪末期到一战之前的若干典型研究，从中反思早期社会学田野工作是如何在“直接经验”的倾向中完成类型化，以及如何在这个经验感获得的同时改变对社会世界的道德想象。早期社会学的田野工作的力量之一不在于以规范的诉求压制经验感，而是以独特的情感结构连接经验的观察和道德理想的重生。由此，我们不仅需要反思诸如社会调查到社区研究这样的简单思路，更要重新理解理论史上所谓的“进步主义式”的道德本身。这一工作对重新认识早期社会研究和今天的社会学民族志都具有意义上的准备。     

A new generalized Weibull distribution in income economic inequality curves

Researchers have been developing various extensions and modified forms of the Weibull distribution to enhance its capability for modeling and fitting different data sets. In this note, we investigate the potential usefulness of the new modification to the standard Weibull distribution called odd Weibull distribution in income economic inequality studies. Some mathematical and statistical properties of this model are proposed. We obtain explicit expressions for the first incomplete moment, quantile function, Lorenz and Zenga curves and related inequality indices. In addition to the well-known stochastic order based on Lorenz curve, the stochastic order based on Zenga curve is considered. Since the new generalized Weibull distribution seems to be suitable to model wealth, financial, actuarial and especially income distributions, these findings are fundamental in the understanding of how parameter values are related to inequality. Also, the estimation of parameters by maximum likelihood and moment methods is discussed. Finally, this distribution has been fitted to United States and Austrian income data sets and has been found to fit remarkably well in compare with the other widely used income models.     

章公祖师佛像跨国追索诉讼国际私法问题研究

从章公祖师佛像跨国追索诉讼主体资格问题切入,分析佛像的法律性质,探讨涉案物权准据法的确定及适用问题。该案原告应由阳春村全体村民担任。涉案佛像属于文物,建议村民在荷兰法院提起原物返还之诉。受案法院应当适用物权冲突法确定准据法,即可能适用中国内地、中国香港或荷兰法律。被告在购买时应当对涉案佛像来源产生合理怀疑,因此不构成“善意”;原告应尽可能搜集证据证明《荷兰民法典》规定的20年消灭时效因起诉而中断。依据《香港货品售卖条例》和中国内地物权法理论与实践,被告均不能取得系争佛像的所有权。     

当代资本主义经济停滞问题:制度结构与治理危机——基于积累的社会结构理论的分析

关于经济停滞问题的探讨是对马克思主义经济危机理论的传承。2008年金融危机爆发十余年,资本主义经济停滞趋于常态化。积累的社会结构理论从资本积累与支撑这一积累的制度环境之间的矛盾出发,发现了主流经济学探讨资本主义经济停滞所忽略的制度性因素。积累的社会结构学派沿着"中间层次"的路径,将2008年金融危机界定为系统性的结构危机,认为新自由主义SSA的失灵是此次经济停滞持续的原因。资本主义经济停滞引发欧美发达资本主义国家的社会治理危机升级,一系列政治、社会和文化危机不断加剧。积累的社会结构理论从微观层面深刻把握了资本主义制度转型的轨迹,但并未深入探讨开展根本的制度变革。在面临百年未有之大变局的时代境遇下,中国在参与全球治理的过程中不断探索并推动世界经济政治新秩序朝着更加公正、合理的方向发展。     

On hysteretic vector autoregressive model with applications

This paper proposes a new hysteretic vector autoregressive (HVAR) model in which the regime switching may be delayed when the hysteresis variable lies in a hysteresis zone. We integrate an adapted multivariate Student-t distribution from amending the scale mixtures of normal distributions. This HVAR model allows for a higher degree of flexibility in the degrees of freedom for each time series. We use the proposed model to test for a causal relationship between any two target time series. Using posterior odds ratios, we overcome the limitations of the classical approach to multiple testing. Both simulated and real examples herein help illustrate the suggested methods. We apply the proposed HVAR model to investigate the causal relationship between the quarterly growth rates of gross domestic product of United Kingdom and United States. Moreover, we check the pairwise lagged dependence of daily PM2.5 levels in three districts of Taipei.     

Accurate computation of single and product moments of order statistics under progressive censoring

An accurate procedure is proposed to calculate approximate moments of progressive order statistics in the context of statistical inference for lifetime models. The study analyses the performance of power series expansion to approximate the moments for location and scale distributions with high precision and smaller deviations with respect to the exact values. A comparative analysis between exact and approximate methods is shown using some tables and figures. The different approximations are applied in two situations. First, we consider the problem of computing the large sample variance–covariance matrix of maximum likelihood estimators. We also use the approximations to obtain progressively censored sampling plans for log-normal distributed data. These problems illustrate that the presented procedure is highly useful to compute the moments with precision for numerous censoring patterns and, in many cases, is the only valid method because the exact calculation may not be applicable.     

Inference and Decision Making for 21st-Century Drug Development and Approval

ABSTRACT

The cost and time of pharmaceutical drug development continue to grow at rates that many say are unsustainable. These trends have enormous impact on what treatments get to patients, when they get them and how they are used. The statistical framework for supporting decisions in regulated clinical development of new medicines has followed a traditional path of frequentist methodology. Trials using hypothesis tests of “no treatment effect” are done routinely, and the p-value < 0.05 is often the determinant of what constitutes a “successful” trial. Many drugs fail in clinical development, adding to the cost of new medicines, and some evidence points blame at the deficiencies of the frequentist paradigm. An unknown number effective medicines may have been abandoned because trials were declared “unsuccessful” due to a p-value exceeding 0.05. Recently, the Bayesian paradigm has shown utility in the clinical drug development process for its probability-based inference. We argue for a Bayesian approach that employs data from other trials as a “prior” for Phase 3 trials so that synthesized evidence across trials can be utilized to compute probability statements that are valuable for understanding the magnitude of treatment effect. Such a Bayesian paradigm provides a promising framework for improving statistical inference and regulatory decision making.