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。     

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。     

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.     

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。     

马克思的资本有机构成理论与当代中国的经济发展

马克思在《资本论》中介绍,资本家为了获得更多的剩余价值和在竞争中取胜,必然不断改进技术,采用先进机器设备,提高劳动生产率,其结果是在全部资本中,不变资本所占比重相对增大,而可变资本所占比重相对缩小,从而导致资本有机构成提高。资本有机构成的提高,是资本积累、资本扩张发展的一个必然趋势。可在当今,随着产业结构调整和科学技术的发展,资本有机构成呈现下降的趋势,在发达国家尤其明显,资本有机构成对我国经济也有影响。     

Privacy protection measures for randomized response surveys on stigmatizing continuous variables

We consider the situation where sample surveys are to be undertaken on sensitive or stigmatizing issues. For such surveys, direct questioning methods usually lead to non-compliance or incorrect responses and so, the randomized response technique, where the responses are collected through some randomization device, is found to be useful. A majority of the literature on these techniques focus on dichotomous sensitive variables, while some techniques are also available for continuous sensitive variables. In this article, we focus on the extent of privacy protection available in sample surveys to respondents for continuous response variables. We also propose two measures of privacy protection. We demonstrate that the parameters of our randomization scheme can be so chosen as to achieve a pre-assigned level of privacy protection while at the same time yielding efficient estimates. We also show some numerical comparisons.     

Correlation curve estimation for multiplicative distortion measurement errors data

A correlation curve measures the strength of the association between two variables locally at different values of covariate. This paper studies how to estimate the correlation curve under the multiplicative distortion measurement errors setting. The unobservable variables are both distorted in a multiplicative fashion by an observed confounding variable. We obtain asymptotic normality results for the estimated correlation curve. We conduct Monte Carlo simulation experiments to examine the performance of the proposed estimator. The estimated correlation curve is applied to analyze a real dataset for an illustration.     

Using the EM algorithm for Bayesian variable selection in logistic regression models with related covariates

We develop a Bayesian variable selection method for logistic regression models that can simultaneously accommodate qualitative covariates and interaction terms under various heredity constraints. We use expectation-maximization variable selection (EMVS) with a deterministic annealing variant as the platform for our method, due to its proven flexibility and efficiency. We propose a variance adjustment of the priors for the coefficients of qualitative covariates, which controls false-positive rates, and a flexible parameterization for interaction terms, which accommodates user-specified heredity constraints. This method can handle all pairwise interaction terms as well as a subset of specific interactions. Using simulation, we show that this method selects associated covariates better than the grouped LASSO and the LASSO with heredity constraints in various exploratory research scenarios encountered in epidemiological studies. We apply our method to identify genetic and non-genetic risk factors associated with smoking experimentation in a cohort of Mexican-heritage adolescents.