基于一般速率模型的面板计数数据统计分析

在复发事件数据存在删失的情况下,对基准速率函数进行一般化调整之后建立了一般速率模型。同时,给出该模型中未知参数的极大似然估计,并且证明了这些参数估计的大样本性质,即相合性和渐近正态性。利用大量数值模拟验证了文中所提出的模型及参数估计方法的合理性。最后,通过对一组膀胱癌治疗的临床试验数据进行实例分析,刻画协变量对膀胱癌复发率的影响。     

体育锻炼与高血压的相关性研究

了解成年居民体育锻炼人群与非体育锻炼人群之间的血压差异．在山西省人群中随机征集21-60岁健康的男女性样本1642个,对他们的体育锻炼状况进行询问,用标准方法测量样本的血压．应用协方差分析在校正其他因素的影响后分别比较男女性中体育锻炼组与非体育锻炼组收缩压（SBP）和舒张压（DBP）之间的差异．结果表明：男女性体育锻炼组与非体育锻炼组的SBP存在极显著差异（P〈0．01）,男性体育锻炼组与非体育锻炼组的DBP存在极显著差异（P〈0．01）,女性体育锻炼组与非体育锻炼组的DBP存在显著差异（P〈0．05）．体育锻炼是影响血压的有效指标,进行体育锻炼可有效的预防和控制高血压．     

一般生长曲线模型岭估计的影响分析

讨论协方差阵扰动对一般生长曲线模型岭估计的影响分析,建立了协方差阵扰动生长曲线模型与原模型间岭估计的一些关系式,给出了度量影响大小的距离测度和计算公式.     

Cox模型中共线协变量的分层处理

协变量间的共线关系导致不准确的估计及检验,现有的几种方法都存在不同程度的缺陷。作者给出一种分层处理方法,从模拟试验的拟合效果看,优于主尬发方法,似然比检验及单变量分析方法。将该方法用于一个实际例子,得到较好的结果。     

利用设备响应状态信息的运行可靠性评估

为了保障关键设备的运行安全可靠,以克服经典的可靠性分析方法难以评估单台运行设备可靠性的不足,提出了一种利用设备响应状态信息的运行可靠性评估方法.该方法利用设备响应状态信息,应用小波包分析和距离评估技术,提取并优选反映设备运行状态的敏感特征指标,然后结合比例协变量模型,给出基本响应协变量函数的定量计算方法,从而建立了设备的可靠性评估模型,实现了设备的运行可靠性评估.该方法通过特征优选将敏感响应协变量特征指标引入到设备可靠性评估中,实现了基本响应协变量函数的准确定量计算及设备运行可靠性的有效评估.将该方法应用到数控机床刀具的可靠性分析中,有效地评估了数控机床刀具的运行可靠性,并验证了提出方法的合理性和有效性.     

CONTINUOUS AUXILIARY COVARIATE IN ADDITIVE HAZARDS REGRESSION FOR SURVIVAL DATA

This paper considers the additive hazards iliary covariate information to improve the efficiency regression analysis by utilizing continuous aux- of the statistical inference when the primary covariate is ascertained only for a randomly selected subsample. The authors construct a martingale based estimating equation for the regression parameter and establish the asymptotic consistency and normality of the resultant estimators. Simulation study shows that the proposed method can greatly improve the efficiency compared with the estimator which discards the auxiliary covariate information in a variety of settings. A real example is also provided as an illustration.     

VARIABLE SELECTION FOR COVARIATE ADJUSTED REGRESSION MODEL

This paper employs the SCAD-penalized least squares method to simultaneously select variables and estimate the coefficients for high-dimensional covariate adjusted linear regression models. The distorted variables are assumed to be contaminated with a multiplicative factor that is determined by the value of an unknown function of an observable covariate. The authors show that under some appropriate conditions, the SCAD-penalized least squares estimator has the so called ＂oracle property＂. In addition, the authors also suggest a BIC criterion to select the tuning parameter, and show that BIC criterion is able to identify the true model consistently for the covariate adjusted linear regression models. Simulation studies and a real data are used to illustrate the efficiency of the proposed estimation algorithm.     

Bankruptcy prediction using a discrete‐time duration model incorporating temporal and macroeconomic dependencies

The purpose of this paper is to build an alternative method of bankruptcy prediction that accounts for some deficiencies in previous approaches that resulted in poor out‐of‐sample performances. Most of the traditional approaches suffer from restrictive presumptions and structural limitations and fail to reflect the panel properties of financial statements and/or the common macroeconomic influence. Extending the work of Shumway (2001), we present a duration model with time‐varying covariates and a baseline hazard function incorporating macroeconomic dependencies. Using the proposed model, we investigate how the hazard rates of listed companies in the Korea Stock Exchange (KSE) are affected by changes in the macroeconomic environment and by time‐varying covariate vectors that show unique financial characteristics of each company. We also investigate out‐of‐sample forecasting performances of the suggested model and demonstrate improvements produced by allowing temporal and macroeconomic dependencies. Copyright © 2008 John Wiley & Sons, Ltd.     

ESTIMATION AND INFERENCE FOR VARYING-COEFFICIENT REGRESSION MODELS WITH ERROR-PRONE COVARIATES

This paper studies the estimation and inference for a class of varying-coefficient regression models with error-prone covariates. The authors focus on the situation where the covariates are unobserved, there are no repeated measurements and the covariance matrix of the measurement errors is unknown, but some auxiliary information is available. The authors propose an instrumental variable type local polynomial estimator for the unknown varying-coefficient functions, and show that the estimator achieves the optimal nonparametric convergence rate, is asymptotically normal, and avoids using undersmoothing to allow the bandwidths to be selected using data-driven methods. A simulation is carried out to study the finite sample performance of the proposed estimator, and a real date set is analyzed to illustrate the usefulness of the developed methodology.