NONPARAMETRIC QUANTILE ESTIMATION FROM RECORD-BREAKING DATA

Sometimes, in industrial quality control experiments and destructive stress testing, only values smaller than all previous ones are observed. Here we consider nonparametric quantile estimation, both the ‘sample quantile function’ and kernel-type estimators, from such record-breaking data. For a single record-breaking sample, consistent estimation is not possible except in the extreme tails of the distribution. Hence replication is required, and for m. such independent record-breaking samples the quantile estimators are shown to be strongly consistent and asymptotically normal as m-→∞. Also, for small m, the mean-squared errors, biases and smoothing parameters (for the smoothed estimators) are investigated through computer simulations.     

Nonparametric density estimation from data with a mixture of Berkson and classical errors

The author considers density estimation from contaminated data where the measurement errors come from two very different sources. A first error, of Berkson type, is incurred before the experiment: the variable X of interest is unobservable and only a surrogate can be measured. A second error, of classical type, is incurred after the experiment: the surrogate can only be observed with measurement error. The author develops two nonparametric estimators of the density of X, valid whenever Berkson, classical or a mixture of both errors are present. Rates of convergence of the estimators are derived and a fully data‐driven procedure is proposed. Finite sample performance is investigated via simulations and on a real data example.     

毛泽东在中共领导集体核心地位的确立过程与原因探析

毛泽东在中共领导集体核心地位的确立有一个历史过程 ,大致可分为五个阶段。毛泽东在中共领导集体核心地位的确立是由众多因素决定的 :第一 ,毛泽东具有深厚的马列主义理论修养和传统文化素养 ;第二 ,毛泽东是将马克思主义中国化的旗手 ;第三 ,毛泽东具有追求真理的顽强革命精神 ;第四 ,毛泽东善于争取团结多数 ,赢得众人的支持 ;第五 ,共产国际对毛泽东的赞同 ;第六 ,毛泽东具有卓越的人格魅力     

意义、原则与路径——关于社会主义核心价值体系建设的思考

在建设社会主义核心价值体系实践中,应当坚持核心价值体系建设与尊重差异、包容多样的有机统一,坚持追求理想目标与从现实出发的有机统一,坚持共同价值目标与个体价值目标的有机统一,坚持继承与发展、借鉴与创新的有机统一。要把社会主义核心价值体系融入思想教育活动之中,融入舆论宣传之中,融入文化活动和文化产品消费过程之中,融入群众性精神文明创建活动之中,融入党的执政能力和先进性建设之中。     

DO NOT WEIGHT FOR HETEROSCEDASTICITY IN NONPARAMETRIC REGRESSION

The potential role of weighting in kernel regression is examined. The concept that weighting has something to do with heteroscedastic errors is shown to be false. However, weighting does affect bias, and ways in which this might be exploited are indicated.     

Semiparametric Time-Varying Coefficients Regression Model for Longitudinal Data

Abstract.  In this paper, we consider a semiparametric time-varying coefficients regression model where the influences of some covariates vary non-parametrically with time while the effects of the remaining covariates follow certain parametric functions of time. The weighted least squares type estimators for the unknown parameters of the parametric coefficient functions as well as the estimators for the non-parametric coefficient functions are developed. We show that the kernel smoothing that avoids modelling of the sampling times is asymptotically more efficient than a single nearest neighbour smoothing that depends on the estimation of the sampling model. The asymptotic optimal bandwidth is also derived. A hypothesis testing procedure is proposed to test whether some covariate effects follow certain parametric forms. Simulation studies are conducted to compare the finite sample performances of the kernel neighbourhood smoothing and the single nearest neighbour smoothing and to check the empirical sizes and powers of the proposed testing procedures. An application to a data set from an AIDS clinical trial study is provided for illustration.     

安全关键系统中防危策略的设计技术研究

为保障安全关键系统的正常运行,防止由用户对安全关键设备的误操作引起的系统故障,该文提出了一种通用的安全关键系统防危体系结构和防危策略模型,并以核能发电控制系统为原型研究了一种基于有限自动机的防危策略设计技术,该技术可推广到其他任何安全关键系统设备中．     

论高校创新人才培养的全面质量管理

随着时代的进步和社会需求的变迁,高校创新人才的教育质量已成为市场关注的焦点,为此,高等教育开始重新审视自身的创新人才培养目标及其理念。应用现代管理思想,促进高校创新人才培养成为世界教育管理的一种发展趋势;厘清高校的产品、消费者等概念,明确高校创新人才培养全面质量管理的核心价值在于突出其全面性、全员性、全程性。     

Estimation of non-parametric regression for dasometric measures

The aim of this paper is to describe a simulation procedure to compare parametric regression against a non-parametric regression method, for different functions and sets of information. The proposed methodology improves lack of fit at the edges of the regression curves, and an acceptable result is obtained for the no-parametric estimation in all studied cases. Larger differences appear at the edges of the estimation. The results are applied to the study of dasometric variables, which do not fulfil the normality hypothesis needed for parametric estimation. The kernel regression shows the relationship between the studied variables, which would not be detected with more rigid parametric models.     

A class of partially linear single‐index survival models

The authors define a class of “partially linear single‐index” survival models that are more flexible than the classical proportional hazards regression models in their treatment of covariates. The latter enter the proposed model either via a parametric linear form or a nonparametric single‐index form. It is then possible to model both linear and functional effects of covariates on the logarithm of the hazard function and if necessary, to reduce the dimensionality of multiple covariates via the single‐index component. The partially linear hazards model and the single‐index hazards model are special cases of the proposed model. The authors develop a likelihood‐based inference to estimate the model components via an iterative algorithm. They establish an asymptotic distribution theory for the proposed estimators, examine their finite‐sample behaviour through simulation, and use a set of real data to illustrate their approach.