局部平稳部分线性面板数据模型的统计推断

部分线性模型是一类非常重要的半参数回归模型,由于它既含有参数部分又含有非参数部分,与常规的线性模型相比具有更强的适应性和解释能力。文章研究带有局部平稳协变量的固定效应部分线性面板数据模型的统计推断。首先提出一个两阶段估计方法得到模型中未知参数和非参数函数的估计,并证明估计量的渐近性质,然后运用不变原理构造出非参数函数的一致置信带,最后通过数值模拟研究和实例分析验证了该方法的有效性。     

Multiple event times in the presence of informative censoring: modeling and analysis by copulas

Motivated by a breast cancer research program, this paper is concerned with the joint survivor function of multiple event times when their observations are subject to informative censoring caused by a terminating event. We formulate the correlation of the multiple event times together with the time to the terminating event by an Archimedean copula to account for the informative censoring. Adapting the widely used two-stage procedure under a copula model, we propose an easy-to-implement pseudo-likelihood based procedure for estimating the model parameters. The approach yields a new estimator for the marginal distribution of a single event time with semicompeting-risks data. We conduct both asymptotics and simulation studies to examine the proposed approach in consistency, efficiency, and robustness. Data from the breast cancer program are employed to illustrate this research.

     

理智与情感:为历史“正名”的方法——从刘保昌长篇小说《楚武王》谈起

历史书写中的“最炫流行风”在今天的文化市场上大行其道——用拆解、谐谑、碎片化、戏拟等方式拉平历史的深度,以“傲慢与偏见”来充抵叙述历史的自信,也经由这种时尚化的叙述使历史以“轻松一刻”的形式实现消费欲望的满足.刘保昌的《楚武王》则疏离“流行前线”,以史料为据,正本清源,守正而出新,为历史“正名”,以彰显历史意识、历史理性的写作姿态,以坚持文学之理想力量的叙事激情,以有文化承担的叙事态度显示出对当代历史小说叙事品格与尊严的坚守.     

A modified common factor model for modelling mortality jointly for both sexes

Journal of Population Research - There is an increasing attention on the joint modelling of multiple populations. Populations are related in several ways, such as neighbouring countries, females...     

1950年代:“本质—特征论”文学定义的形成及影响

1950年代形成了"本质—特征论"的文学定义,即先界定文学的意识形态本质,再界定文学是用形象的方式来反映社会生活、体现意识形态的特征。这一定义的思维方式是先界定事物的普遍性,再界定事物的特殊性,突出事物的普遍性关联,轻视事物的独特性质的揭示,使得文学研究本应着重研究文学的特殊性成为空话,不利于认识文学自身的规律。1980年代的文学审美论试图突破这种定义方式,但1990年代以来,反本质主义的文学语境论再次回到从事物的关联性定义事物的思维老路,文学自身本质问题受到质疑,弱化了文学与审美关联的重要性与必要性。     

医疗需求与中国医疗费用增长——基于城乡老年医疗支出差异的视角

在人口老龄化和老年医疗支出城乡差异凸显的背景下,中国医疗费用增长部分源于合理的健康需求。利用中国营养与健康调查1991—2011年的八轮数据,构造出生组跟踪样本,分别考察城乡居民医疗支出的年龄效应,估算城乡老年医疗需求导致的费用上涨,可以发现:城市居民的人均医疗支出随年龄显著增加,但农村居民的人均医疗支出随年龄增长的趋势并不明显。而忽略出生组效应,会低估城乡老年医疗支出的差距。城乡老年医疗支出差距缩小,将导致医疗费用在2010—2030年年均实际增长约5.2%。在控制医疗总费用上涨的同时,医疗保险制度需根据老年医疗需求,调整医疗资源配置结构。     

Environmental Justice in the Context of Scientifi Uncertainty: On the Safety of Genetically Modifi Organisms

应对风险特别是科学不确定性所导致的风险，实现和维护环境正义是当今时代的 一个前沿核心议题。转基因生物安全为代表的科学不确定性背景下的环境正义，需要 建立在相应的科学研究基础之上，但在本质上乃是一种社会构建。对转基因生物技术 及其产品等现代科技的研发、应用和推广等活动，应当维护其市场自由，与此同时， 为了公共安全以及社会弱势群体的最大利益，还应当对相关科技活动进行必要的政府 干预和法律规制。     

Partial Identification of Economic Mobility: With an Application to the United States

Abstract

The economic mobility of individuals and households is of fundamental interest. While many measures of economic mobility exist, reliance on transition matrices remains pervasive due to simplicity and ease of interpretation. However, estimation of transition matrices is complicated by the well-acknowledged problem of measurement error in self-reported and even administrative data. Existing methods of addressing measurement error are complex, rely on numerous strong assumptions, and often require data from more than two periods. In this article, we investigate what can be learned about economic mobility as measured via transition matrices while formally accounting for measurement error in a reasonably transparent manner. To do so, we develop a nonparametric partial identification approach to bound transition probabilities under various assumptions on the measurement error and mobility processes. This approach is applied to panel data from the United States to explore short-run mobility before and after the Great Recession.     

高含水期油藏层间压力结构差异量化方法研究

由于长期注水开发和层间非均质性影响，高含水期油藏各小层层间压力结构差异严重，掌握差异状况是合理调整的重要依据。针对现场分层测压资料少，单层压力资料获取困难的情况，应用渗流力学原理建立了高含水期地层层间压力结构差异量化数学模型，模型的关键参数可以通过生产数据计算获得。枣园油田的实例验证表明，高含水油藏层间压力存在明显差异，该方法能够确定各小层的压力差异状况。量化计算结果和实测资料相接近，有利于认清开发矛盾，为合理调整提供技术依据。     

Towards Uniformly Efficient Trend Estimation Under Weak/Strong Correlation and Non‐stationary Volatility

In this paper, we consider the deterministic trend model where the error process is allowed to be weakly or strongly correlated and subject to non‐stationary volatility. Extant estimators of the trend coefficient are analysed. We find that under heteroskedasticity, the Cochrane–Orcutt‐type estimator (with some initial condition) could be less efficient than Ordinary Least Squares (OLS) when the process is highly persistent, whereas it is asymptotically equivalent to OLS when the process is less persistent. An efficient non‐parametrically weighted Cochrane–Orcutt‐type estimator is then proposed. The efficiency is uniform over weak or strong serial correlation and non‐stationary volatility of unknown form. The feasible estimator relies on non‐parametric estimation of the volatility function, and the asymptotic theory is provided. We use the data‐dependent smoothing bandwidth that can automatically adjust for the strength of non‐stationarity in volatilities. The implementation does not require pretesting persistence of the process or specification of non‐stationary volatility. Finite‐sample evaluation via simulations and an empirical application demonstrates the good performance of proposed estimators.