A new discrete distribution: properties and applications in medical care

This paper proposes a simple and flexible count data regression model which is able to incorporate overdispersion (the variance is greater than the mean) and which can be considered a competitor to the Poisson model. As is well known, this classical model imposes the restriction that the conditional mean of each count variable must equal the conditional variance. Nevertheless, for the common case of well-dispersed counts the Poisson regression may not be appropriate, while the count regression model proposed here is potentially useful. We consider an application to model counts of medical care utilization by the elderly in the USA using a well-known data set from the National Medical Expenditure Survey (1987), where the dependent variable is the number of stays after hospital admission, and where 10 explanatory variables are analysed.     

Exact predictors for a generalized ar(1) process with an ar(1) parameter

The generalized AR(1) process y _t = a _t y _t-1+ v _t is considered, where the parameter a _t follows the AR(1) process a _t = Ga _t-1+ w _t.Assuming that V _t and w _t are Gaussian and independent, the first six exact predictors for future values of y _t are derived. These exact predictors are compared with Box-Jenkins -type approximations. MACSYMA, a computer algebra program, is utilized in the derivation of the predictors.     

A modified Liu-type estimator with an intercept term under mixture experiments

We consider ridge regression with an intercept term under mixture experiments. We propose a new estimator which is shown to be a modified version of the Liu-type estimator. The so-called compound covariate estimator is applied to modify the Liu-type estimator. We then derive a formula of the total mean squared error (TMSE) of the proposed estimator. It is shown that the new estimator improves upon existing estimators in terms of the TMSE, and the performance of the new estimator is invariant under the change of the intercept term. We demonstrate the new estimator using a real dataset on mixture experiments.     

An index of local sensitivity to non-ignorability for parametric survival models with potential non-random missing covariate: an application to the SEER cancer registry data

Several survival regression models have been developed to assess the effects of covariates on failure times. In various settings, including surveys, clinical trials and epidemiological studies, missing data may often occur due to incomplete covariate data. Most existing methods for lifetime data are based on the assumption of missing at random (MAR) covariates. However, in many substantive applications, it is important to assess the sensitivity of key model inferences to the MAR assumption. The index of sensitivity to non-ignorability (ISNI) is a local sensitivity tool to measure the potential sensitivity of key model parameters to small departures from the ignorability assumption, needless of estimating a complicated non-ignorable model. We extend this sensitivity index to evaluate the impact of a covariate that is potentially missing, not at random in survival analysis, using parametric survival models. The approach will be applied to investigate the impact of missing tumor grade on post-surgical mortality outcomes in individuals with pancreas-head cancer in the Surveillance, Epidemiology, and End Results data set. For patients suffering from cancer, tumor grade is an important risk factor. Many individuals in these data with pancreas-head cancer have missing tumor grade information. Our ISNI analysis shows that the magnitude of effect for most covariates (with significant effect on the survival time distribution), specifically surgery and tumor grade as some important risk factors in cancer studies, highly depends on the missing mechanism assumption of the tumor grade. Also a simulation study is conducted to evaluate the performance of the proposed index in detecting sensitivity of key model parameters.     

Information borrowing methods for covariate‐adjusted ROC curve

In medical diagnostic testing problems, the covariate adjusted receiver operating characteristic (ROC) curves have been discussed recently for achieving the best separation between disease and control. Due to various restrictions such as cost, the availability of patients, and ethical issues quite frequently only limited information is available. As a result, we are unlikely to have a large enough overall sample size to support reliable direct estimations of ROCs for all the underlying covariates of interest. For example, some genetic factors are less commonly observable compared with others. To get an accurate covariate adjusted ROC estimation, novel statistical methods are needed to effectively utilize the limited information. Therefore, it is desirable to use indirect estimates that borrow strength by employing values of the variables of interest from neighbouring covariates. In this paper we discuss two semiparametric exponential tilting models, where the density functions from different covariate levels share a common baseline density, and the parameters in the exponential tilting component reflect the difference among the covariates. With the proposed models, the estimated covariate adjusted ROC is much smoother and more efficient than the nonparametric counterpart without borrowing information from neighbouring covariates. A simulation study and a real data application are reported. The Canadian Journal of Statistics 40: 569–587; 2012 © 2012 Statistical Society of Canada     

一类证券组合投资问题的H∞状态反馈投资策略

为研究兼顾实际系统离散性和参数时变性的证券组合投资问题,提出了一个衡量风险大小的二次型性能指标,并建立了证券组合投资的离散时变状态空间模型.运用离散时变系统的H_∞控制理论,得到了证券组合投资的H_∞状态反馈投资策略.仿真证明使用该投资策略可使总收益实现其最小的不确定性.     

Estimation of the Average Survival Function Using a Censored Data Regression Model

In the presence of covariates information, assuming the linear relationship between a transformation of survival time and covariates, we propose a new estimator of survival function and show its consistency. In addition, a comparison of the proposed estimator with the product-limit estimator introduced by Kaplan and Meier (1958) is performed through Monte Carlo simulation studies. We illustrate the proposed estimator with the updated Stanford heart transplant data.     

存货影响销售率且理论需求为线性时变函数时的EOQ模型

本文建立了理论需求率为线性时变递增函数且无短缺量拖后和短缺量完全拖后时的EOQ模型,并对无短缺量拖后时模型最优解的存在及唯一性进行了讨论,为库存系统的管理决策提供了理论依据。     

基于动态模型平均的大豆期货价格预测研究

针对大豆期货价格波动的复杂性及影响因素的多元性，本文将动态模型平均理论引入大豆期货价格分析与预测研究中，通过动态选择解释变量和系数时变程度，在有效控制模型和系数不确定性的同时，最大限度综合利用大豆期货市场内外部信息，以提高大豆期货价格预测准确度。具体的，本文提出一套基于动态模型平均理论的大豆期货价格影响因素与预测分析框架，从期货市场和经济环境等两方面准确地识别出大豆期货价格影响因素的时变特征，进而构建大豆期货价格预测模型，并通过预测误差指标和Diebold-Mariano检验法评估其与基准模型的预测能力。研究结果表明，动态模型平均理论在有效剖析大豆期货价格影响因素的时变特征的同时，能明显提升大豆期货价格预测准确度。     

欧债危机环境中资产组合ES模型比较研究

本文以中国上证综指、德国法兰克福DAX指数、美国S&P500指数为研究对象,分别运用DCC-GARCH及时变Copula-EVT模型建模,探讨了欧债危机爆发后股市间相依关系的变化状况。在此基础上,将三个股指收益两两组合,分别建立了各类模型假定下的资产组合预期损失ES模型,并通过后验分析方法,探讨了危机爆发后,各类ES风险模型测度精度的变化状况及对比结果。实证研究表明：欧债危机爆发后,时变Copula-EVT-ES的风险测度准确度明显高于DCC-GARCH-ES模型;边缘分布模型的选择对于时变Copula-EVT-ES模型的测度精度具有重要影响。综合对比分析发现,在金融市场极端波动的状况下,能够捕捉杠杆效应且善于刻画厚尾特征的时变t-Copula-AR（1）-GJR（1,1）-EVT-ES模型能够取得相对较好的风险测度效果。