Modified profile likelihood estimation for the Weibull regression models in survival analysis

In this study, adjustment of profile likelihood function of parameter of interest in presence of many nuisance parameters is investigated for survival regression models. Our objective is to extend the Barndorff–Nielsen’s technique to Weibull regression models for estimation of shape parameter in presence of many nuisance and regression parameters. We conducted Monte-Carlo simulation studies and a real data analysis, all of which demonstrate and suggest that the modified profile likelihood estimators outperform the profile likelihood estimators in terms of three comparison criterion: mean squared errors, bias and standard errors.     

基于VAR模型的四川经济增长与投资消费关系实证研究

选取1990-2010年四川省经济数据,运用格兰杰因果检验、VAR模型和脉冲响应分析等方法对四川省经济增长与投资消费之间的关系进行了实证研究,结果表明：不论时间长短,投资水平对经济增长和消费水平的影响相对显著,而经济增长和消费水平在短期内对其他两项的影响不显著。     

通货膨胀及紧缩与货币供应关系的实证分析

首先借用剑桥方程式建立了通货膨胀及紧缩与货币供应关系的理论模型,然后运用格兰杰因果检验的方法验证了我国超供货币供应是CPI物价指数的原因,而CPI物价指数作为超供货币供应的原因则被拒绝。说明了我国现阶段以货币供应量为中间目标调控经济的发展仍具较强实际意义。     

论大学英语词汇教学

英语语言学家威尔金斯对词汇的定义和最新颁布的《大学英语课程教学要求(》试行)对大学英语词汇教学的要求,都阐释了词汇在大学英语教学中的重要性。文章介绍了现阶段大学英语教学的情况,并通过调查分析,发现以大学英语四、六级考试为目的的大学英语词汇教学的弊病,提出科学的词汇教学方法,并对词汇教学与大学英语教学之间的关系进行了阐释。     

我国教育投入与经济增长关系的实证研究

利用协整分析和Granger因果检验方法研究1952-2002期间,我国教育投入和经济增长之间的相互关系,证实我国经济增长教育的投入,且教育投入与国内生产总值间互为因果关系,增加教育投入有助于促进我国实际经济总量的增长,同时,经济和增长和发展水平决定着教育投入的规模.     

Testing spatial randomness based on empirical distribution function: A study on lattice data

In this article, we extended the empirical distribution function based test statistic _Ik$I_k$ of Skaug and Tjostheim [1993. Nonparametric test of serial independence based on the empirical distribution function. Biometrika 80, 591–602] in the time series setting to _Dn$D_n$ for spatial lattice data and derived the asymptotic distribution of the proposed test statistic _Dn$D_n$ under the null hypothesis of spatial independence. The size and power of the proposed test statistic under conditional autoregressive model (CAR) were simulated. We applied _Dn$D_n$, Moran's I and Geary's c   to the transformed and well-studied sudden infant death syndrome data from North Carolina and found that _Dn$D_n$ produced a much smaller p$p$-value in testing spatial independence.     

Modified inference about the mean of the exponential distribution using moving extreme ranked set sampling

The maximum likelihood estimator (MLE) and the likelihood ratio test (LRT) will be considered for making inference about the scale parameter of the exponential distribution in case of moving extreme ranked set sampling (MERSS). The MLE and LRT can not be written in closed form. Therefore, a modification of the MLE using the technique suggested by Maharota and Nanda (Biometrika 61:601–606, 1974) will be considered and this modified estimator will be used to modify the LRT to get a test in closed form for testing a simple hypothesis against one sided alternatives. The same idea will be used to modify the most powerful test (MPT) for testing a simple hypothesis versus a simple hypothesis to get a test in closed form for testing a simple hypothesis against one sided alternatives. Then it appears that the modified estimator is a good competitor of the MLE and the modified tests are good competitors of the LRT using MERSS and simple random sampling (SRS).     

Extended scaled prediction variance optimality for modified central composite design

Robust parameter designs (RPDs) enable the experimenter to discover how to modify the design of the product to minimize the effect due to variation from noise sources. The aim of this article is to show how this amount of work can be reduced under modified central composite design (MCCD). We propose a measure of extended scaled prediction variance (ESPV) for evaluation of RPDs on MCCD. Using these measures, we show that we can check the error or bias associated with estimating the model parameters and suggest the values of α recommended for MCCS under minimum ESPV.     

Tolerance limits under Poisson regression based on small-sample asymptotic methodology

This article explores the calculation of tolerance limits for the Poisson regression model based on the profile likelihood methodology and small-sample asymptotic corrections to improve the coverage probability performance. The data consist of n counts, where the mean or expected rate depends upon covariates via the log regression function. This article evaluated upper tolerance limits as a function of covariates. The upper tolerance limits are obtained from upper confidence limits of the mean. To compute upper confidence limits the following methodologies were considered: likelihood based asymptotic methods, small-sample asymptotic methods to improve the likelihood based methodology, and the delta method. Two applications are discussed: one application relating to defects in semiconductor wafers due to plasma etching and the other examining the number of surface faults in upper seams of coal mines. All three methodologies are illustrated for the two applications.