Cox regression analysis in presence of collinearity: an application to assessment of health risks associated with occupational radiation exposure

This paper considers the analysis of time to event data in the presence of collinearity between covariates. In linear and logistic regression models, the ridge regression estimator has been applied as an alternative to the maximum likelihood estimator in the presence of collinearity. The advantage of the ridge regression estimator over the usual maximum likelihood estimator is that the former often has a smaller total mean square error and is thus more precise. In this paper, we generalized this approach for addressing collinearity to the Cox proportional hazards model. Simulation studies were conducted to evaluate the performance of the ridge regression estimator. Our approach was motivated by an occupational radiation study conducted at Oak Ridge National Laboratory to evaluate health risks associated with occupational radiation exposure in which the exposure tends to be correlated with possible confounders such as years of exposure and attained age. We applied the proposed methods to this study to evaluate the association of radiation exposure with all-cause mortality.     

On the Breslow–Holubkov estimator

Breslow and Holubkov (J Roy Stat Soc B 59:447–461 1997a) developed semiparametric maximum likelihood estimation for two-phase studies with a case–control first phase under a logistic regression model and noted that, apart for the overall intercept term, it was the same as the semiparametric estimator for two-phase studies with a prospective first phase developed in Scott and Wild (Biometrica 84:57–71 1997). In this paper we extend the Breslow–Holubkov result to general binary regression models and show that it has a very simple relationship with its prospective first-phase counterpart. We also explore why the design of the first phase only affects the intercept of a logistic model, simplify the calculation of standard errors, establish the semiparametric efficiency of the Breslow–Holubkov estimator and derive its asymptotic distribution in the general case.     

Semiparametric Bayesian classification with longitudinal markers

Summary.  We analyse data from a study involving 173 pregnant women. The data are observed values of the β human chorionic gonadotropin hormone measured during the first 80 days of gestational age, including from one up to six longitudinal responses for each woman. The main objective in this study is to predict normal versus abnormal pregnancy outcomes from data that are available at the early stages of pregnancy. We achieve the desired classification with a semiparametric hierarchical model. Specifically, we consider a Dirichlet process mixture prior for the distribution of the random effects in each group. The unknown random-effects distributions are allowed to vary across groups but are made dependent by using a design vector to select different features of a single underlying random probability measure. The resulting model is an extension of the dependent Dirichlet process model, with an additional probability model for group classification. The model is shown to perform better than an alternative model which is based on independent Dirichlet processes for the groups. Relevant posterior distributions are summarized by using Markov chain Monte Carlo methods.     

Bayesian analysis of human movement curves

Summary.  We consider the Bayesian analysis of human movement data, where the subjects perform various reaching tasks. A set of markers is placed on each subject and a system of cameras records the three-dimensional Cartesian co-ordinates of the markers during the reaching movement. It is of interest to describe the mean and variability of the curves that are traced by the markers during one reaching movement, and to identify any differences due to covariates. We propose a methodology based on a hierarchical Bayesian model for the curves. An important part of the method is to obtain identifiable features of the movement so that different curves can be compared after temporal warping. We consider four landmarks and a set of equally spaced pseudolandmarks are located in between. We demonstrate that the algorithm works well in locating the landmarks, and shape analysis techniques are used to describe the posterior distribution of the mean curve. A feature of this type of data is that some parts of the movement data may be missing—the Bayesian methodology is easily adapted to cope with this situation.     

Comparison of different estimation techniques for portfolio selection

The main problem in applying the mean-variance portfolio selection consists of the fact that the first two moments of the asset returns are unknown. In practice the optimal portfolio weights have to be estimated. This is usually done by replacing the moments by the classical unbiased sample estimators. We provide a comparison of the exact and the asymptotic distributions of the estimated portfolio weights as well as a sensitivity analysis to shifts in the moments of the asset returns. Furthermore we consider several types of shrinkage estimators for the moments. The corresponding estimators of the portfolio weights are compared with each other and with the portfolio weights based on the sample estimators of the moments. We show how the uncertainty about the portfolio weights can be introduced into the performance measurement of trading strategies. The methodology explains the bad out-of-sample performance of the classical Markowitz procedures.     

基于RS分析的中国债券市场分形特征研究

文章详细讨论了RS分析方法、概率空间分形维的测度,通过对上证国债指数和上证企债指数的RS分析,得到了两序列赫斯特指数和非周期循环长度,进一步得出中国债券市场为分形市场的结论.     

中国基础设施投资对经济增长波动的冲击效应分析

基础设施是经济发展的重要影响因素，而“基础设施投资促进经济发展”这一主流观点已得到广泛共识，但人们对经济增长与基础设施的探讨仅局限于总量的分析框架，并未对基础设施的类型加以区分，更没发现经济发展的不同阶段对基础设施的需求结构也是不同的。而以“基础设施投资促进经济发展”为出发点，将基础设施投资细分为生产性、生活性、社会性基础设施投资，并分别讨论其对经济的影响。通过邹检验发现：自1994年以来，基础设施投资冲击改变了中国经济增长的特征。其结论是生产性、生活性基础设施对经济增长的影响是明确的、同步的，但社会性基础设施对经济增长的作用是不确定的。因此，加强社会性基础设施投资是一个渐进地逐步推进的过程。     

我国商业银行无形资产统计问题研究

摘　　要:商业银行无形资产是形成商业银行核心竞争力的基础和产生超额收益的源泉,加强商业银行无形资产统计工作,有利于充分揭示商业银行无形资产基本内容和对核心竞争力的影响。笔者认为,目前我国商业银行无形资产统计指标体系设置不完整,统计制度不健全,统计内容不全面,统计分析不深入,影响经济决策。笔者认为,完善商业银行无形资产统计制度时,应从组织结构类、客户关系类、知识创新类和人力资本类等四个方面对无形资产进行统计,逐步细化统计指标,明确具体的统计内容,建立、健全无形资产价值信息系统,深化无形资产统计分析工作,以此为基础,提出提高商业银行核心竞争力的基本策略。     

统计代理制的理性探索

基层统计是中国政府统计的基础,提高警惕基层统计质量是提高中国统计数据质量的关键,而建立与社会经济体制相适应的基层统计管理制度又是提高基层统计质量的根本保证.在社会主义市场经济体制下,我国基层统计环境发生了全新变化,为了适应新的变化,必须建立基层统计代理制,基层统计代理制是统计经济成分多元化,社会分工细化,统计利益市场化和统计工作规范化的必然要求 .在我国建立基层统计代理制具有法律可行性,技术档行性和经济可行性.随着统计代理制的实施,我国基层统计的数据来源将会更有保障,数据质量将会稳步提升,工作效率将会大大提高,从而促进我国统计事业兴旺发达.