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.     

Mixtures of regression models with incomplete and noisy data

The estimation of the mixtures of regression models is usually based on the normal assumption of components and maximum likelihood estimation of the normal components is sensitive to noise, outliers, or high-leverage points. Missing values are inevitable in many situations and parameter estimates could be biased if the missing values are not handled properly. In this article, we propose the mixtures of regression models for contaminated incomplete heterogeneous data. The proposed models provide robust estimates of regression coefficients varying across latent subgroups even under the presence of missing values. The methodology is illustrated through simulation studies and a real data analysis.     

Likelihood ratio tests for multivariate normality

This paper presents some powerful omnibus tests for multivariate normality based on the likelihood ratio and the characterizations of the multivariate normal distribution. The power of the proposed tests is studied against various alternatives via Monte Carlo simulations. Simulation studies show our tests compare well with other powerful tests including multivariate versions of the Shapiro–Wilk test and the Anderson–Darling test.     

从教师专业实践能力发展反观高校与幼儿园的双向融合

当前，打通高校教师理论研究与基层一线教师专业发展瓶颈是总体提升我国教育质量的高效之举，教育的实践性本质也决定了高校教师的理论研究与一线教师的教育实践必须坚持双向融合的道路。实现校园双向融合既要积极构建教师发展共同体，还要积极探索有深度和广度的教研机制，并搭建开放、多元、创新的资源平台。     

公共卫生支出、健康结果与卫生投入政策——基于189个国家和地区的面板门槛分析（1995—2011年）

利用189个国家和地区1995—2011年的数据实证检验公共卫生支出规模对一个国家的健康结果（婴儿死亡率和出生时的平均期望寿命）的影响，结果表明，公共卫生支出占GDP的比重对婴儿死亡率和出生时的平均期望寿命的影响具有门槛效应，门槛值分别位于1.9%和6.62%。分段回归结果显示，公共卫生支出占GDP的比重低于1.9%时，公共卫生投入对出生时的平均期望寿命没有显著影响，对婴儿死亡率的影响较弱；超过1.9%后，公共卫生投入的规模效应开始体现，对健康结果指标的影响均显著增强；超过6.62%后，公共卫生投入对婴儿死亡率的影响不再显著，对出生时平均期望寿命的弹性系数不再变化，单位边际贡献不再增加。中国当前政府卫生投入规模仍然较低，需要继续增大公共资金投入、降低个人卫生支出比重。     

On nonparametric variogram estimation

A critical step for geostatistical prediction is estimation of variogram from the data. One of the popular methods estimating variogram is a smoothed version of classical nonparametric variogram estimator. In this paper we investigate its theoretical and empirical properties to provide useful information for using it. The main results are based on asymptotic theories (i.e., risk and central limit theorem) under nearly infill domain sampling. Simulation is also employed to make our points.     

Maternal Experiences of Parenting Girls who are Perceived as Overweight or at Risk for Becoming So: Narratives of Uncertainty,Ambivalence and Struggle

This work explored U.S. mothers’ experiences of parenting young adolescent girls who are perceived by their mothers as overweight or at risk for becoming so. Data were collected via in‐depth interviews with 13 mothers and were analysed using a constructivist grounded theory approach. Many mothers experienced socializing their daughters about issues of the body, weight, diet, and health as marked by uncertainty, ambivalence and struggle, particularly relative to four subthemes: mothers’ embodiment as challenge to ‘good mothering’, negotiating a dilemma of the ‘healthy mind’ versus the ‘healthy body’, managing discipline: how much to intervene?, and the challenge of interpersonal dynamics.     

Cramér Asymptotic Efficiency in a Semiparametric Model

This article is concerned with efficient estimation in a semiparametric model. We consider pseudo maximum likelihood estimation and prove that the proposed estimator is asymptotically efficient in the sense of Cramér; that is, the estimator has the smallest mean squared error.     

台湾对外直接投资与产业结构调整关系的实证研究

基于协整理论研究1978年-2009年期间台湾对外直接投资与产业结构调整之间的关系，并在此基础上建立误差修正模型。对外直接投资是引起台湾产业结构调整的重要原因，两者之间存在长期的协整关系：对外直接投资与工业比重调整的方向相反，与服务业比重调整的方向一致。结合中国大陆对外投资与产业结构的现状，应当完善对外投资管理，引导产业投资合理布局，明确投资产业选择基准。     

单因素约束下的坡市人口合理容量测算——以成都市为例

为制定城市发展规划，以实现城市可持续发展的目标，需对城市合理人口容量进行测算。考虑人口容量与经济、社会、自然的关系，选取国民生产总值、经济弹性系数、产业发展、耕地资源、水资源为制约人口容量的制约因素．测度各因素制约下的人口合理容量，最后应用“木桶原理”得到同时满足各个制约因素的人口合理容量。