球墨铸铁曲轴的气体软氮化

柴油机的珠墨铸铁曲轴系重要零件,气体软氮化应用到球墨铸铁领域还是一种新工艺,而且比中频淬火效果好,提高了曲轴的抗疲劳和抗磨损的能力。本文对气体软氮化的基本原理、设备、工艺参数的选择和催渗方法进行了探讨,对软氮化后曲轴的尺寸形状误差、光洁度和机械性能的提高等进行了较详细的试验、分析和比较,结果,经气体软氮化后的曲轴不但尺寸形站误差符合图纸要求外,而且抛去轻微氧化层后光洁度还略有提高,曲轴安全系数由1.3～1.6提高到1.70～1.77,并且一般不再发生疲劳断(?),使用寿命比原来寿命增加3.32倍以上。     

股权分置改革对沪港市场联动性影响的研究——基于小波多分辨率的分析

运用小波多分辨率方法,通过对两个时期日对数收益率数据的分析,发现股权分置改革后中国内地股市与香港股市间的联动效应明显增强,两者的联系日益密切;同时长期趋势因素对市场的影响加强,说明中国内地股市的有效性有所改善;短期波动能量有所下降,但不明显,表明市场运行格局在短期内还未有较大改变,需继续加强市场制度建设,强化培育理性机构投资者,优化投资者结构,引导与完善市场投资理念,推动市场向更成熟方向发展.     

知识共享对供应链信用风险传染的影响研究

将企业间知识共享这一因素引入供应链上下游企业博弈过程,在完全信息条件下研究三级供应链中零售商分别与分销商、制造商进行知识共享时知识共享量与企业信用风险水平和供应链信用风险传染强度之间的关系,并结合算例进行了数值分析.基于Stackelberg博弈模型的研究结果表明,供应链上的知识共享有助于降低供应链成员企业的信用风险;进行知识共享的企业间信用风险传染强度随着知识共享量的增加而增大;企业进行知识共享比不进行知识共享时受到其它企业信用风险传染的影响大.     

Three-part model for fractional response variables with application to Chinese household health insurance coverage

A survey on health insurance was conducted in July and August of 2011 in three major cities in China. In this study, we analyze the household coverage rate, which is an important index of the quality of health insurance. The coverage rate is restricted to the unit interval [0, 1], and it may differ from other rate data in that the “two corners” are nonzero. That is, there are nonzero probabilities of zero and full coverage. Such data may also be encountered in economics, finance, medicine, and many other areas. The existing approaches may not be able to properly accommodate such data. In this study, we develop a three-part model that properly describes fractional response variables with non-ignorable zeros and ones. We investigate estimation and inference under two proportional constraints on the regression parameters. Such constraints may lead to more lucid interpretations and fewer unknown parameters and hence more accurate estimation. A simulation study is conducted to compare the performance of constrained and unconstrained models and show that estimation under constraint can be more efficient. The analysis of household health insurance coverage data suggests that household size, income, expense, and presence of chronic disease are associated with insurance coverage.     

Generation of multivariate distributions by vertical density representation

Troutt (1991,1993) proposed the idea of the vertical density representation (VDR) based on Box-Millar method. Kotz, Fang and Liang (1997) provided a systematic study on the multivariate vertical density representation (MVDR). Suppose that we want to generate a random vector X[d]Rⁿthat has a density function ?(x). The key point of using the MVDR is to generate the uniform distribution on [D]_?(v) = {x :?(x) = v} for any v > 0 which is the surface in RⁿIn this paper we use the conditional distribution method to generate the uniform distribution on a domain or on some surface and based on it we proposed an alternative version of the MVDR(type 2 MVDR), by which one can transfer the problem of generating a random vector X with given density f to one of generating (X, X_n+i) that follows the uniform distribution on a region in Rⁿ⁺¹defined by ?. Several examples indicate that the proposed method is quite practical.     

Statistical inference on asymptotic properties of two estimators for the partially linear single-index models

The outer product of gradients (OPG) estimation procedure based on least squares (LS) approach has been presented by Xia et al. [An adaptive estimation of dimension reduction space. J Roy Statist Soc Ser B. 2002;64:363–410] to estimate the single-index parameter in partially linear single-index models (PLSIM). However, its asymptotic property has not been established yet and the efficiency of LS-based method can be significantly affected by outliers and heavy-tailed distributions. In this paper, we firstly derive the asymptotic property of OPG estimator developed by Xia et al. [An adaptive estimation of dimension reduction space. J Roy Statist Soc Ser B. 2002;64:363–410] in theory, and a novel robust estimation procedure combining the ideas of OPG and local rank (LR) inference is further developed for PLSIM along with its theoretical property. Then, we theoretically derive the asymptotic relative efficiency (ARE) of the proposed LR-based procedure with respect to LS-based method, which is shown to possess an expression that is closely related to that of the signed-rank Wilcoxon test in comparison with the t-test. Moreover, we demonstrate that the new proposed estimator has a great efficiency gain across a wide spectrum of non-normal error distributions and almost not lose any efficiency for the normal error. Even in the worst case scenarios, the ARE owns a lower bound equalling to 0.864 for estimating the single-index parameter and a lower bound being 0.8896 for estimating the nonparametric function respectively, versus the LS-based estimators. Finally, some Monte Carlo simulations and a real data analysis are conducted to illustrate the finite sample performance of the estimators.     

Empirical Likelihood Inference for the Parameter in Additive Partially Linear EV Models

In this article, we consider empirical likelihood inference for the parameter in the additive partially linear models when the linear covariate is measured with error. By correcting for attenuation, a corrected-attenuation empirical log-likelihood ratio statistic for the unknown parameter β, which is of primary interest, is suggested. We show that the proposed statistic is asymptotically standard chi-square distribution without requiring the undersmoothing of the nonparametric components, and hence it can be directly used to construct the confidence region for the parameter β. Some simulations indicate that, in terms of comparison between coverage probabilities and average lengths of the confidence intervals, the proposed method performs better than the profile-based least-squares method. We also give the maximum empirical likelihood estimator (MELE) for the unknown parameter β, and prove the MELE is asymptotically normal under some mild conditions.     

Estimation Bias in Complete-Case Analysis in Crossover Studies with Missing Data

Crossover designs are used often in clinical trials. It is not uncommon that subjects discontinue before completing all treatment periods in a crossover study. Despite availability of statistical methodologies utilizing all available data and software for obtaining valid inferences under the assumption of missing at random (MAR), naïve approaches, such as the complete case (CC) analysis, which is only valid with a strong assumption of missing completely at random are still widely used in practice. In this article, we obtain the analytical form of the estimation bias of treatment effects with CC for linear mixed models. We use simulation studies to examine the inflation of Type I error and efficiency loss in the inferences with CC under MAR. Invalidity and inefficiency of two other commonly used approaches for defining analyzed data in the presence of missing data, including data from at least two periods in three period crossover and available cases for a specific comparison of interest, are also demonstrated through simulation studies.     

Uniform Design for Experiments with Mixtures

The goal of uniform mixture design is to scatter the design points in the experimental region uniformly. The commonly used criteria, such as mean square distance, are based on the Euclidean distance. Based on the Lee distance, a new criterion is proposed in this article. And an algorithm, called NTLBG, is also proposed to refine the randomly generated design for the experimental design with mixtures. Some examples show that the design generated by the NTLBG algorithm has a lower criteria value.