Weighted quantile regression and testing for varying-coefficient models with randomly truncated data

This paper develops a varying-coefficient approach to the estimation and testing of regression quantiles under randomly truncated data. In order to handle the truncated data, the random weights are introduced and the weighted quantile regression (WQR) estimators for nonparametric functions are proposed. To achieve nice efficiency properties, we further develop a weighted composite quantile regression (WCQR) estimation method for nonparametric functions in varying-coefficient models. The asymptotic properties both for the proposed WQR and WCQR estimators are established. In addition, we propose a novel bootstrap-based test procedure to test whether the nonparametric functions in varying-coefficient quantile models can be specified by some function forms. The performance of the proposed estimators and test procedure are investigated through simulation studies and a real data example.     

Variance estimation using refitted cross-validation in ultrahigh dimensional regression

Variance estimation is a fundamental problem in statistical modelling. In ultrahigh dimensional linear regression where the dimensionality is much larger than the sample size, traditional variance estimation techniques are not applicable. Recent advances in variable selection in ultrahigh dimensional linear regression make this problem accessible. One of the major problems in ultrahigh dimensional regression is the high spurious correlation between the unobserved realized noise and some of the predictors. As a result, the realized noises are actually predicted when extra irrelevant variables are selected, leading to serious underestimate of the level of noise. We propose a two-stage refitted procedure via a data splitting technique, called refitted cross-validation, to attenuate the influence of irrelevant variables with high spurious correlations. Our asymptotic results show that the resulting procedure performs as well as the oracle estimator, which knows in advance the mean regression function. The simulation studies lend further support to our theoretical claims. The naive two-stage estimator and the plug-in one-stage estimators using the lasso and smoothly clipped absolute deviation are also studied and compared. Their performances can be improved by the reffitted cross-validation method proposed.     

黄宗羲的魂魄说及其与天主教思想之交涉——以《破邪论》与《天主实义》为中心

黄宗羲从其气一元的宇宙本体论和重视经世史学及社会教化的实学立场出发,在批判继承朱熹魂魄说的同时回归先秦儒学原典,批判了民间迷信及佛教、道教的魂魄说,指出人只有一魂一魄,主张人与物因气之差异而理亦相异,以人性与物性根本不同因而不能相互转化批判了轮回说,并对祭祀之感格及灵魂不朽给予了理性解释,维护了儒家传统信仰。利玛窦则基于天主赋魂说与天堂地狱赏罚论,批判了儒家的魂魄说、佛教的地狱轮回说,并给予儒家的祭祀以非宗教的解释。从黄宗羲信奉主宰世界的昊天上帝,认为圣贤灵魂不灭,以及承认儒家祭祀之非宗教世俗意义等方面来看,恰好可作为儒家主流学者对于利玛窦所倡导和推动的适应中国国情的传教策略的批判和反应。     

科技企业孵化器协同关系研究

选取3家科技企业孵化器作为案例,围绕科技企业孵化器协同关系中的主体、要素、服务进行分析,构建了协同关系网络下科技企业孵化器的主体互动、要素共享和服务提供的分析模型。提出了三个命题:多主体互动是科技企业孵化器协同关系产生的基础;多主体互动带来资源要素共享,产生主体间协同关系;孵化器基于协同关系为孵化企业提供差异化的服务,从而创造价值。     

A Monte Carlo comparison of GMM and QMLE estimators for short dynamic panel data models with spatial errors

We suggest a generalized spatial system GMM (SGMM) estimation for short dynamic panel data models with spatial errors and fixed effects when n is large and T is fixed (usually small). Monte Carlo studies are conducted to evaluate the finite sample properties with the quasi-maximum likelihood estimation (QMLE). The results show that, QMLE, with a proper approximation for initial observation, performs better than SGMM in general cases. However, it performs poorly when spatial dependence is large. QMLE and SGMM perform better for different parameters when there is unknown heteroscedasticity in the disturbances and the data are highly persistent. Both estimates are not sensitive to the treatment of initial values. Estimation of the spatial autoregressive parameter is generally biased when either the data are highly persistent or spatial dependence is large. Choices of spatial weights matrices and the sign of spatial dependence do affect the performance of the estimates, especially in the case of the heteroscedastic disturbance. We also give empirical guidelines for the model.     

Quantile regression for competing risks analysis under case-cohort design

The case-cohort design brings cost reduction in large cohort studies. In this paper, we consider a nonlinear quantile regression model for censored competing risks under the case-cohort design. Two different estimation equations are constructed with or without the covariates information of other risks included, respectively. The large sample properties of the estimators are obtained. The asymptotic covariances are estimated by using a fast resampling method, which is useful to consider further inferences. The finite sample performance of the proposed estimators is assessed by simulation studies. Also a real example is used to demonstrate the application of the proposed methods.     

解决认同难题的阿基米德杠杆——借鉴公民社会理论推进社会主义核心价值认同

多元利益格局是社会主义核心价值认同的现实难题,经济体制转型是多元利益格局形成的社会条件,而多元价值观冲突是多元利益格局的思想反映.由于公民社会是多元利益格局当中协调利益冲突和价值观冲突的阿基米德杠杆,因而可以借鉴公民社会理论来建构核心价值认同的"表达-导向"机制,即首先建构民间组织利益表达机制以推动多元价值观的彰显,然后建构多元价值观的融合机制以形成核心价值体系的大众化表述,最后建构核心价值体系的导向机制来引领价值认同.     

网络媒介条件下的信息公信力

网络媒介已成为当今信息传播的主要渠道之一,但因其与传统传媒的不同特征,其信息公信力面临着不同的认知和评价。通过对这一问题的几个方面的讨论,就网络信息的传播特征、网络信息公信力现状和我们的应对等,给出浅见。     

清代台湾土著妇女的日常生活

清代台湾社会经济的发展离不开广大妇女的积极参与,土著妇女在日常社会生活中扮演了重要角色,她们不仅是耕织的主要承担者,而且在公共性社会活动中也占有一席之地,并由此决定了她们在婚姻家庭中享受较大的权利。她们与汉族移民的联姻,又加快了不同族群间的融合步伐,促进了台湾社会的发展。     

“三鹿奶粉”事件引起的思考——以成本与利润问题分析为例

“三鹿奶粉”重大食品安全事故,已导致全国近5万名婴幼儿患病,并一度造成广大社会民众对食品安全的极度恐慌。“三鹿奶粉”事件不但使中国食品行业发展遭遇重创,引发了整个中国食品行业的安全信任危机,而且在世界范围内再次掀起对“中国制造”问题的热议。“三鹿奶粉”事件产生的原因是多方面的,但主要归结于奶粉监督管理部门的规章制度不完善和监管不力以及企业道德失范、责任不明确或责任心不强等原因。而笔者力求利用经济学原理,从成本与利润分析这一新的视角出发,探讨奶制品行业造假贩假的本源,探索揭示“三鹿奶粉”事件发生的内在必然性,并提出相应的对策。