Ranking and estimation of related means in two—way models—a Bayesian approach

A hierarchical Bayesian approach to ranking and selection as well as estimation of related means in two—way models is considered. Using the method of Monte Carlo simulation with importance sampling, we are able to carry out efficiently the three or four dimensional integrations as needed. An example is included to illustrate the methodology.     

中国高等教育中的自主招生与人才选拔：来自北大、清华和人大的发现

本文通过对“首都大学生成长追踪调查”中三所精英大学（北京大学、清华大学和中国人民大学）具有代表性样本的数据分析，从教育公平和人才选拔效率两个角度检验了自主招生政策实施的效果。研究结果表明，从教育公平的方面来讲，获得自主招生破格录取的学生更有可能来自父母受过高等教育的家庭、城市家庭和好的重点高中。从人才选拔效率的角度来讲，获得自主招生破格录取学生的学业表现、社会活动能力、非认知能力、毕业后的计划和实际去向与统招学生却并无显著差别。本文的发现对于如何完善自主招生政策、促进教育公平、科学选拔和培养优秀人才等议题具有重要的政策启示意义。     

Is prenatal sex selection associated with lower female child mortality?

I examine whether prenatal sex selection has substituted postnatal excess female mortality by analysing the dynamics of child sex ratios between 1980 and 2015 using country-level life table data. I decompose changes in child sex ratios into a ‘fertility’ component attributable to prenatal sex selection and a ‘mortality’ component attributable to sex differentials in postnatal survival. Although reductions in numbers of excess female deaths have accompanied increases in missing female births in all countries experiencing the emergence of prenatal sex selection, relative excess female mortality has persisted in some countries but not others. In South Korea, Armenia, and Azerbaijan, mortality reductions favouring girls accompanied increases in prenatal sex selection. In India, excess female mortality was much higher and largely stable as prenatal sex selection emerged, but slight reductions were seen in the 2000s. In China, although absolute measures showed reductions, relative excess female mortality persisted as prenatal sex selection increased.     

基于次梯度投影的泛投资组合选择策略

在线投资组合选择(online portfolio selection)问题是当前量化投资领域一个重要的研究问题.近些年来,可投资标的的爆炸式增长急需能够有效计算的投资组合选择策略,而现有高绩效算法大多具有指数级或多项式级的时间复杂度,不利于在实际中应用.由此,本文提出了一种基于次梯度投影的泛投资组合选择策略SGP.将次梯度投影的思想应用到资产组合构建的过程中,得到策略的再平衡规则.理论上,本文分析了次梯度投影算法的竞争性能,证明了该策略是一个泛投资组合选择策略;并发现该算法具有线性时间复杂度.实证上,验证了SGP策略在美国与中国市场的表现.结果表明,SGP策略能够实现和最新的泛投资组合选择策略相当的收益率,而算法运行时间短于现有策略.参数敏感性分析表明SGP策略对参数选择不敏感;交易成本敏感性分析表明SGP策略能够承受合理的交易成本.     

Treatment Evaluation in the Presence of Sample Selection

Sample selection and attrition are inherent in a range of treatment evaluation problems such as the estimation of the returns to schooling or training. Conventional estimators tackling selection bias typically rely on restrictive functional form assumptions that are unlikely to hold in reality. This paper shows identification of average and quantile treatment effects in the presence of the double selection problem into (i) a selective subpopulation (e.g., working—selection on unobservables) and (ii) a binary treatment (e.g., training—selection on observables) based on weighting observations by the inverse of a nested propensity score that characterizes either selection probability. Weighting estimators based on parametric propensity score models are applied to female labor market data to estimate the returns to education.     

Composite quantile regression for massive datasets

Analysis of massive datasets is challenging owing to limitations of computer primary memory. Composite quantile regression (CQR) is a robust and efficient estimation method. In this paper, we extend CQR to massive datasets and propose a divide-and-conquer CQR method. The basic idea is to split the entire dataset into several blocks, applying the CQR method for data in each block, and finally combining these regression results via weighted average. The proposed approach significantly reduces the required amount of primary memory, and the resulting estimate will be as efficient as if the entire data set is analysed simultaneously. Moreover, to improve the efficiency of CQR, we propose a weighted CQR estimation approach. To achieve sparsity with high-dimensional covariates, we develop a variable selection procedure to select significant parametric components and prove the method possessing the oracle property. Both simulations and data analysis are conducted to illustrate the finite sample performance of the proposed methods.     

Reversible jump Markov chain Monte Carlo algorithms for Bayesian variable selection in logistic mixed models

In this article, to reduce computational load in performing Bayesian variable selection, we used a variant of reversible jump Markov chain Monte Carlo methods, and the Holmes and Held (HH) algorithm, to sample model index variables in logistic mixed models involving a large number of explanatory variables. Furthermore, we proposed a simple proposal distribution for model index variables, and used a simulation study and real example to compare the performance of the HH algorithm with our proposed and existing proposal distributions. The results show that the HH algorithm with our proposed proposal distribution is a computationally efficient and reliable selection method.     

Development of predictive signatures for treatment selection in precision medicine with survival outcomes

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker‐positive and biomarker‐negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood‐based change‐point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change‐point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.     

逆向选择条件下带奖惩金的两期保险契约模型及其Pareto改进性研究

为了更有效的规避影响保险市场交易效率的逆向选择问题，本文分投保人风险类型为两种和多种情形建立了带奖惩金的两期保险契约模型，首次提出可以用奖励金和惩罚金有效甄别投保人的风险类型。该模型根据投保人第一个保险期内的索赔情况在第二个保险期对其进行奖励或惩罚，高风险类型的投保人如果选择为低风险类型投保人设计的保险契约，则其在第二阶段受到惩罚的概率要远远大于得到奖励的概率，即风险越高的投保人越害怕惩罚金，因此所建模型满足斯彭斯-莫里斯分离条件。带奖惩金的两期保险契约模型中保险公司的期望利润仍然为0，并不会给投保人带来额外的经济负担，却能够实现对传统部分保险契约简单重复两次的严格帕累托改进。最后采用一个算例说明了该模型的有效性。