Consistent estimation of the bispectral density function of a harmonizable process

In this note, the consistency of a bispectral density estimator of strongly harmonizable processes is obtained. A resampling procedure is used consisting of an -mixing sequence of sample paths of the process.     

Estimation in inverse Gaussian regression: comparison of asymptotic and bootstrap distributions

This paper presents a brief review of the asymptotic properties of the pseudo-maximum likelihood estimator in the regression model where the reciprocal of the mean of the dependent variable is considered to be a linear function of the regressor variables, and the observations on the dependent variable are assumed to have an inverse Gaussian distribution. The large sample theory for the pseudo-maximum likelihood estimator presented in Babu and Chaubey (1996) is highlighted and a simulation study is carried out to compare the approximation yielded by the bootstrap distribution to that of the asymptotic distribution.     

试论刘少奇对制定和贯彻八大路线所做出的卓越贡献

党的八大是一次具有历史意义的成功大会 ,它制定的路线是正确的 ,为社会主义事业的进一步发展指明了正确方向。刘少奇作为第一代中共领导核心的重要成员 ,为党的八大正确路线的形成和贯彻做出了卓越贡献 :呕心沥血起草八大政治报告 ,他的一些理论、观点 ,被八大政治报告所收纳 ,极大地丰富了八大路线 ,并为贯彻和维护八大正确路线而艰辛地探索。     

Variance Estimation in Spatial Regression Using a Non-parametric Semivariogram Based on Residuals

Abstract.  The empirical semivariogram of residuals from a regression model with stationary errors may be used to estimate the covariance structure of the underlying process. For prediction (kriging) the bias of the semivariogram estimate induced by using residuals instead of errors has only a minor effect because the bias is small for small lags. However, for estimating the variance of estimated regression coefficients and of predictions, the bias due to using residuals can be quite substantial. Thus we propose a method for reducing this bias. The adjusted empirical semivariogram is then isotonized and made conditionally negative-definite and used to estimate the variance of estimated regression coefficients in a general estimating equations setup. Simulation results for least squares and robust regression show that the proposed method works well in linear models with stationary correlated errors.     

柳永词别离场面析论

通过四个方面，即离别场面设置、离别场面中的离别意象、离别的过程、柳永的自我意识，对柳永五首专写离别场面的词作了探讨，力求找到柳词对别离学的贡献。     

A Lack-of-Fit Test for Heteroscedastic Regression Models via Cosine-Series Smoothers

In this paper, a test is derived to assess the validity of heteroscedastic nonlinear regression models by a non‐parametric cosine regression method. For order selection, the paper proposes a data‐driven method that uses the parametric null model optimal order. This method yields a test that is asymptotically normally distributed under the null hypothesis and is consistent against any fixed alternative. Simulation studies that test the lack of fit of a generalized linear model are conducted to compare the performance of the proposed test with that of an existing non‐parametric kernel test. A dataset of esterase levels is used to demonstrate the proposed method in practice.     

中国工人运动的先驱——刘少奇与安源路矿工人运动

刘少奇是中国工人运动的先驱,本文论述了他在早期革命时,由毛泽东派往安源路矿从事工人运动的历史。开始,他与李立三一起,领导和组织了安源路矿工人大罢工,并取得了胜利;接着,在京汉铁路“二七”惨案后全国工人运动转入低潮的情况下,他把党的基本原则和安源路矿的实际相结合,利用敌人内部矛盾,采取灵活的斗争策略,进一步巩固和发展了工人运动。     

刘少奇与党的群众路线

群众路线是我们党的根本路线,刘少奇对群众路线理论的形成、完善和发展,作出了重要贡献。集中表现在:揭示了党的群众路线的深刻内涵,提出群众路线是党的根本路线;强调中国共产党代表最广大人民群众的利益,始终保持党与人民群众的血肉联系;重视执政党坚持群众路线的问题,努力探索执政为民的方式。刘少奇的这些重要思想对新时期我们党坚持群众路线,做好群众工作,具有重要的指导意义。     

Double hierarchical generalized linear models (with discussion)

Summary.  We propose a class of double hierarchical generalized linear models in which random effects can be specified for both the mean and dispersion. Heteroscedasticity between clusters can be modelled by introducing random effects in the dispersion model, as is heterogeneity between clusters in the mean model. This class will, among other things, enable models with heavy-tailed distributions to be explored, providing robust estimation against outliers. The h -likelihood provides a unified framework for this new class of models and gives a single algorithm for fitting all members of the class. This algorithm does not require quadrature or prior probabilities.     

Design and analysis of computer experiments when the output is highly correlated over the input space

To build a predictor, the output of a deterministic computer model or “code” is often treated as a realization of a stochastic process indexed by the code's input variables. The authors consider an asymptotic form of the Gaussian correlation function for the stochastic process where the correlation tends to unity. They show that the limiting best linear unbiased predictor involves Lagrange interpolating polynomials; linear model terms are implicitly included. The authors then develop optimal designs based on minimizing the limiting integrated mean squared error of prediction. They show through several examples that these designs lead to good prediction accuracy.