Least-squares estimation of distribution functions in johnson's translation system

To summarize a set of data by a distribution function in Johnson's translation system, we use a least-squares approach to parameter estimation wherein we seek to minimize the distance between the vector of "uniformized" oeder statistics and the corresponding vector of expected values. We use the software package FITTRI to apply this technique to three problems arising respectively in medicine, applied statistics, and civil engineering. Compared to traditional methods of distribution fitting based on moment matching, percentile matchingL ₁ estimation, and L _? estimation, the least-squares technique is seen to yield fits of similar accuracy and to converge more rapidly and reliably to a set of acceptable parametre estimates.     

A non-iterative sampling Bayesian method for linear mixed models with normal independent distributions

In this paper, we utilize normal/independent (NI) distributions as a tool for robust modeling of linear mixed models (LMM) under a Bayesian paradigm. The purpose is to develop a non-iterative sampling method to obtain i.i.d. samples approximately from the observed posterior distribution by combining the inverse Bayes formulae, sampling/importance resampling and posterior mode estimates from the expectation maximization algorithm to LMMs with NI distributions, as suggested by Tan et al. [33 Tan, M., Tian, G. and Ng, K. 2003. A noniterative sampling method for computing posteriors in the structure of EM-type algorithms. Statist. Sinica, 13(3): 625–640. [Web of Science ®] , [Google Scholar]]. The proposed algorithm provides a novel alternative to perfect sampling and eliminates the convergence problems of Markov chain Monte Carlo methods. In order to examine the robust aspects of the NI class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback–Leibler divergence. Further, some discussions on model selection criteria are given. The new methodologies are exemplified through a real data set, illustrating the usefulness of the proposed methodology.     

Likelihood‐based and marginal inference methods for recurrent event data with covariate measurement error

Recurrent event data arise commonly in medical and public health studies. The analysis of such data has received extensive research attention and various methods have been developed in the literature. Depending on the focus of scientific interest, the methods may be broadly classified as intensity‐based counting process methods, mean function‐based estimating equation methods, and the analysis of times to events or times between events. These methods and models cover a wide variety of practical applications. However, there is a critical assumption underlying those methods–variables need to be correctly measured. Unfortunately, this assumption is frequently violated in practice. It is quite common that some covariates are subject to measurement error. It is well known that covariate measurement error can substantially distort inference results if it is not properly taken into account. In the literature, there has been extensive research concerning measurement error problems in various settings. However, with recurrent events, there is little discussion on this topic. It is the objective of this paper to address this important issue. In this paper, we develop inferential methods which account for measurement error in covariates for models with multiplicative intensity functions or rate functions. Both likelihood‐based inference and robust inference based on estimating equations are discussed. The Canadian Journal of Statistics 40: 530–549; 2012 © 2012 Statistical Society of Canada     

Influence diagnostics in heteroscedastic and/or autoregressive nonlinear elliptical models for correlated data

In this paper, we propose nonlinear elliptical models for correlated data with heteroscedastic and/or autoregressive structures. Our aim is to extend the models proposed by Russo et al. 22 by considering a more sophisticated scale structure to deal with variations in data dispersion and/or a possible autocorrelation among measurements taken throughout the same experimental unit. Moreover, to avoid the possible influence of outlying observations or to take into account the non-normal symmetric tails of the data, we assume elliptical contours for the joint distribution of random effects and errors, which allows us to attribute different weights to the observations. We propose an iterative algorithm to obtain the maximum-likelihood estimates for the parameters and derive the local influence curvatures for some specific perturbation schemes. The motivation for this work comes from a pharmacokinetic indomethacin data set, which was analysed previously by Bocheng and Xuping 1 under normality.     

Reference priors for linear models with general covariance structures

We develop a new class of reference priors for linear models with general covariance structures. A general Markov chain Monte Carlo algorithm is also proposed for implementing the computation. We present several examples to demonstrate the results: Bayesian penalized spline smoothing, a Bayesian approach to bivariate smoothing for a spatial model, and prior specification for structural equation models.     

Likelihood-based approaches for multivariate linear models under inequality constraints for incomplete data

In this paper, we consider a multivariate linear model with complete/incomplete data, where the regression coefficients are subject to a set of linear inequality restrictions. We first develop an expectation/conditional maximization (ECM) algorithm for calculating restricted maximum likelihood estimates of parameters of interest. We then establish the corresponding convergence properties for the proposed ECM algorithm. Applications to growth curve models and linear mixed models are presented. Confidence interval construction via the double-bootstrap method is provided. Some simulation studies are performed and a real example is used to illustrate the proposed methods.     

Decision‐Making Fairness and Consensus Building in Multisector Community Health Alliances: A Mixed‐Methods Analysis

Given their inherently diverse composition and potentially competing interests, a foundational activity of community health alliances is establishing consensus on the vision and strategies for achieving its goals. Using an organizational justice framework, we examined whether member perceptions of fairness in alliances' decision‐making processes are associated with the perceived level of consensus among members regarding the alliance vision and strategies. We used a mixed‐methods design to examine the relationship between perceptions of fairness and consensus within fourteen multisector community health alliances. Quantitative analysis found the perceived level of consensus to be positively associated with decision‐making transparency (procedural fairness), inclusiveness (procedural fairness), and benefits relative to costs (distributive fairness). Qualitative analysis indicated that the consensus‐building process is facilitated by using formal decision‐making frameworks and engaging alliance members in decision‐making processes early. Alliance leaders may be more successful at building consensus when they recognize the need to appeal to a member's sense of procedural and distributive fairness, and, perhaps equally important, recognize when one rather than the other is called for and draw upon decision‐making processes that most clearly evoke that sense of fairness. Our findings reinforce the importance of fairness in building and sustaining capacity for improving community health.     

基于线性规划模型的政府绩效评价与资源优化配置研究

政府绩效管理是利用绩效信息协助设定同意的绩效目标,进行资源配置与优先顺序的安排,以告知管理者维持或改变既定目标计划的管理过程。线性规划方法从政府资源配置绩效分析的角度为政府绩效科学评价与政府资源优化配置提供了分析方法和工具。线性规划的偶线性规划的经济意义给出了政府各种资源最优配置的经济估价,结合线性规划的“松紧定理”可进一步对政府机构改革与政府管理资源进行分析,从而达到提高政府管理绩效的目标。     

类混合所有制靠近混合所有制路径研究——我国商业银行推行混合所有制的解决途径

商业银行作为特殊企业,仅有混合所有制之名,尚无混合所有制之实,其国有企业的性质及国有企业的管理特征仍然存在,因此,必须根据商业银行目前经营中存在的问题,结合商业银行内在金融属性与经营运作特点,进行定向“爆破”改革,从而进一步激发商业银行发展的活力。文章主要观点就是通过混合所有制改革,加强市场化机制建设,扫清商业银行传统弊病,真正建立现代企业制度,以增强商业银行发展的动力和能力。而这一过程需要内部冲动和外部政策改革同时进行。在对改革候选标的行业地位、公司规模和股权结构等指标全面衡量的基础上,遵循“先小后大”、“先易后难”、“先条件接近后条件远隔”的原则,从试点先行到全面推进,各家银行也要将本次改革作为一次机会,不为改革而改革的真正激发活力。     

金融混业与完善我国金融监管体系问题研究

全球经济一体化、金融自由化和金融创新的发展,带来了世界范围内的金融混业经营.中国也在分业经营的法律框架内,实现了实质上的混业经营格局.混业经营在促进金融业发展的同时,也促使各国进行金融监管体制的变革.在吸取国际经验的基础上,根据中国国情,从长远来看,我们应当建立一种高效的统一监管模式,过渡期间应当着力加强法制基础建设,加强现有监管机构的协调合作,建立多元化监管体系,督促金融机构强化内部控制制度.