Multiple Imputation in Multivariate Problems When the Imputation and Analysis Models Differ

Bayesian multiple imputation (MI) has become a highly useful paradigm for handling missing values in many settings. In this paper, I compare Bayesian MI with other methods – maximum likelihood, in particular—and point out some of its unique features. One key aspect of MI, the separation of the imputation phase from the analysis phase, can be advantageous in settings where the models underlying the two phases do not agree.     

The Multilevel Approach to Repeated Measures for Complete and Incomplete Data

Repeated measurements often are analyzed by multivariate analysis of variance (MANOVA). An alternative approach is provided by multilevel analysis, also called the hierarchical linear model (HLM), which makes use of random coefficient models. This paper is a tutorial which indicates that the HLM can be specified in many different ways, corresponding to different sets of assumptions about the covariance matrix of the repeated measurements. The possible assumptions range from the very restrictive compound symmetry model to the unrestricted multivariate model. Thus, the HLM can be used to steer a useful middle road between the two traditional methods for analyzing repeated measurements. Another important advantage of the multilevel approach to analyzing repeated measures is the fact that it can be easily used also if the data are incomplete. Thus it provides a way to achieve a fully multivariate analysis of repeated measures with incomplete data. This revised version was published online in June 2006 with corrections to the Cover Date.     

Simulation driven inferences for multiply imputed longitudinal datasets*

In this article, we demonstrate by simulations that rich imputation models for incomplete longitudinal datasets produce more calibrated estimates in terms of reduced bias and higher coverage rates without duly deflating the efficiency. We argue that the use of supplementary variables that are thought to be potential causes or correlates of missingness or outcomes in the imputation process may lead to better inferential results in comparison to simpler imputation models. The liberal use of these variables is recommended as opposed to the conservative strategy.     

ML– and semiparametric estimation in logistic models with incomplete covariate data

ML–estimation of regression parameters with incomplete covariate information usually requires a distributional assumption regarding the concerned covariates that implies a source of misspecification. Semiparametric procedures avoid such assumptions at the expense of efficiency. In this paper a simulation study with small sample size is carried out to get an idea of the performance of the ML–estimator under misspecification and to compare it with the semiparametric procedures when the former is based on a correct assumption. The results show that there is only a little gain by correct parametric assumptions, which does not justify the possibly large bias when the assumptions are not met. Additionally, a simple modification of the complete case estimator appears to be nearly semiparametric efficient.     

以政府职能转变推动收入分配改革

收入和财富分配的不平等一直是中国改革发展中的重要问题。针对中国经济的特点,可以从政府改革的角度来研究如何更好地进行收入分配改革。地方政府主导推动地方经济的做法对收入不平等的扩大起了重要作用,加快政府职能转换,是使收入分配改革真正落到实处的破局之处,也是实现收入分配合理化可行的第一步。要改变地方政府的经济职能,降低它直接参与基础设施的程度,改革地方土地财政,建立垂直管理机构,以分散它的一部分经济管理职能,为收入分配改革打开空间。     

Reliable Inference in Categorical Regression Analysis for Non‐randomly Coarsened Observations

In most surveys, one is confronted with missing or, more generally, coarse data. Traditional methods dealing with these data require strong, untestable and often doubtful assumptions, for example, coarsening at random. But due to the resulting, potentially severe bias, there is a growing interest in approaches that only include tenable knowledge about the coarsening process, leading to imprecise but reliable results. In this spirit, we study regression analysis with a coarse categorical‐dependent variable and precisely observed categorical covariates. Our (profile) likelihood‐based approach can incorporate weak knowledge about the coarsening process and thus offers a synthesis of traditional methods and cautious strategies refraining from any coarsening assumptions. This also allows a discussion of the uncertainty about the coarsening process, besides sampling uncertainty and model uncertainty. Our procedure is illustrated with data of the panel study ‘Labour market and social security' conducted by the Institute for Employment Research, whose questionnaire design produces coarse data.     

富水粉细砂地层隧道综合降水设计与施工技术

本文通过对实例进行分析论述,解决了围岩受施工扰动和水长期浸润后开挖时不稳定问题,保证了隧道结构和施工安全,为同等或类似地质条件下的地下工程研究及施工提供借鉴和参考。     

重复扰动下富含水地层的加固技术

随着城市地铁、地下空间、铁路和公路隧道的大规模建设和发展,工程中要面对的各种复杂地层的地基处理及松软地层改良问题越来越多。文章结合广州市轨道交通5号线(大坦沙～中山八盾构区间)2#联络通道塌方后,在重复扰动的富含水地层进行加固处理的成功经验,详细地介绍了采用无收缩双液二重管注浆技术在重复扰动下富含水地层中的注浆加固方法。     

转型期我国贫富差距扩大现象分析

目前我国的基尼系数已经超过了国际警戒线,最突出的表现就是贫富差距不断扩大,过大的贫富差距会给社会带来动荡,因此,我们不能忽视贫富差距扩大的问题,应正确的看待贫富差距扩大的问题产生的原因,现状以及它所带来的影响,并提出相应的解决方案。