基于施密特过程的变量筛选及其在森林覆盖率分析中的应用

在多元线性回归模型的实际应用中,因受分析人员主观判断的影响,初始选取的自变量集合往往会包含过多的变量。本文利用施密特过程提出一种新的变量筛选方法,可以选择对因变量有显著解释作用的自变量,并且将没有解释作用的信息及冗余信息有效地分解出来并排除掉。将该方法应用于森林覆盖率影响因素分析,有效地进行了变量筛选,并得到了解释性很强同时拟合优度较高的模型结果。     

Making Feminist Sense out of ‘Charlie Wilson's War’

Charlie Wilson's War (2007), Mike Nichols's film about the womanizing Congressman who engineered black funds for the CIA's proxy war in Soviet-occupied Afghanistan, is historically misleading but highly instructive, because in packaging dominant American masculine identity and war politics as popular entertainment for post-9/11 audiences, it reveals the sexed and gendered ‘politics of the visual’ in global affairs. This intertextual study of ‘Charlie Wilson's war’ as movie, constructed history and legacy examines Wilson as a prime exhibit of a needy masculinity that, like the film's emasculated CIA, bulks itself up through surrogate military selves. It also analyses modes of the imaginary and specularity in brother-bonding with the mujahidin, tracks the proxy system's loops of masculine identity-and-war-making between Stateside and South Asia in the post-Vietnam 1980s and interrogates the dynamics of imperial ‘un-seeing’ in this campaign and its long aftermath. While US proxy wars proliferate worldwide, the lack of useable political memory about the ground truths of ‘Charlie's war’ continues to matter because America's second ‘good’ war in Afghanistan, bound to the first by gendered causal links, has re-empowered the forces that still menace women's rights and lives.     

Identification of Marginal Effects in Nonseparable Models Without Monotonicity

Nonseparable models do not impose any type of additivity between the unobserved part and the observable regressors, and are therefore ideal for many economic applications. To identify these models using the entire joint distribution of the data as summarized in regression quantiles, monotonicity in unobservables has frequently been assumed. This paper establishes that in the absence of monotonicity, the quantiles identify local average structural derivatives of nonseparable models.     

一类新非线性常微分方程的可积判据

借助变量替换法及求导法则,给出一类新的非线性常微分方程的可积充分条件,并提供参数形式的通解,所得结论推广了相应文献的结果。     

二重八面体变几何桁架机器人工作空间解析

提出了二重八面体变几何桁架机器人工作空间的边界曲面分析方法。该方法以曲面分析及位置正解的解析方法为基础,得到了工作空间边界曲面解析的曲面方程,描绘出了工作空间边界曲面的精确图形。分析计算结果表明,二重八面体变几何桁架机器人的工作空间是由6张曲面片包围而成的闭包。     

A robust diagnostic plot for explanatory variables under model mis-specification

A typical added variable plot is a commonly used plot in assessing the accuracy of a normal linear model. This plot is often used to evaluate the effect of adding an explanatory variable into the model and to detect possibly high leverage points or influential observations on the added variable. However, this type of plot is generally in doubt, once the normal distributional assumptions are violated. In this article, we extend the robust likelihood technique introduced by Royall and Tsou [11] to propose a robust added variable plot. The validity of this diagnostic plot requires no knowledge of the true underlying distributions so long as their second moments exist. The usefulness of the robust graphical approach is demonstrated through a few illustrations and simulations.     

Exact confidence interval estimation for the difference in diagnostic accuracy with three ordinal diagnostic groups

In the cases with three ordinal diagnostic groups, the important measures of diagnostic accuracy are the volume under surface (VUS) and the partial volume under surface (PVUS) which are the extended forms of the area under curve (AUC) and the partial area under curve (PAUC). This article addresses confidence interval estimation of the difference in paired VUS s and the difference in paired PVUS s. To focus especially on studies with small to moderate sample sizes, we propose an approach based on the concepts of generalized inference. A Monte Carlo study demonstrates that the proposed approach generally can provide confidence intervals with reasonable coverage probabilities even at small sample sizes. The proposed approach is compared to a parametric bootstrap approach and a large sample approach through simulation. Finally, the proposed approach is illustrated via an application to a data set of blood test results of anemia patients.     

财富变量、不确定性与中国居民消费函数——基于PIH-LCH假说消费函数的扩展

自改革开放以来,虽然中国国内生产总值呈现不断增长的态势,但居民消费率总体上呈现逐步下降的趋势。借鉴西方消费理论,并结合中国金融市场不完全、城乡二元结构、居民收入来源多元化、由计划经济向市场经济转轨等特点构建和扩展了中国居民消费函数。研究结果表明,中国居民消费呈现出＂瞻前＂和＂顾后＂的双重特征,并据此提出发展和完善金融市场,大力发展第三产业与生产性服务业,提高教育投资的收益率以及增加政府在社会保障方面的支出,完善中国社会保障体系等提高居民消费的政策建议。     

Asymptotic Properties of Weighted M-estimators for variable probability samples

I provide a systematic treatment of the asymptotic properties of weighted M-estimators under variable probability stratified sampling. The characterization of the sampling scheme and representation of the objective function allow for a straightforward analysis. Simple, consistent asymptotic variance matrix estimators are proposed for a large class of problems. When stratification is based on exogenous variables, I show that the unweighted M-estimator is more efficient than the weighted estimator under a generalized conditional information matrix equality. When population frequencies are known, a more efficient weighting is possible. I also show how the results carry over to multinomial sampling.