The analysis of putative environmental pollution gradients in spatially correlated epidemiological data

The analysis of putative sources of health hazard has recently received considerable attention. Most analyses, however, assume that the observations - such as counts in census tracts or exact locations of cases - are not spatially correlated. This assumption, if violated, could have serious consequences for inferences made from such data. This paper outlines a number of simple Monte Carlo test procedures for this situation.     

Selection of multiple deferred (dependent) state sampling plans

This paper presents procedures and tables for the selection of minimum sample size multiple deferred (dependent) state sampling plan of type MDS and MDS - 1 for various entry conditions such as (AQL, LQL), (AQL, AOQL) etc.     

A new random permutation test in ANOVA models

This paper developed an exact method of random permutations when testing both interaction and main effects in the two-way ANOVA model. The method of this paper can be regarded as a much improved model when compared with those of the previous studies such as Still and White (1981) and ter Braak (1992). We further conducted a simulation experiment in order to check the statistical performance of the proposed method. The proposed method works relatively well for small sample sizes compare with the existing methods. This work was supported by Korea Science and Engineering Foundation Grant (R14-2003-002-0100)     

Using multiparametric auxiliary information at the estimation stage

Difference type estimators use auxiliary information based on an auxiliary parameter (specifically the parameter of interest), associated with the auxiliary variable. In practice, however, several parameters for auxiliary variables are available. This paper discusses how such estimators can be modified to improve the usual methods if information related to other parameters associated with an auxiliary variable or variables is available. Some applications estimating several such parameters are described. A proper set of simulation-based comparisons is made. Research partially supported by MCYT (Spain) contract n. BFM2001-3190     

A Joint estimation approach to sparse additive ordinary differential equations

Statistics and Computing - Analyzing massive spatial datasets using a Gaussian process model poses computational challenges. This is a problem prevailing heavily in applications such as...     

Modelling Trends and Inequality in Small Area Mortality

This paper considers the modelling of mortality rates classified by age, time, and small area with a view to developing life table parameters relevant to assessing trends in inequalities in life chances. In particular, using a fully Bayes perspective, one may assess the stochastic variation in small area life table parameters, such as life expectancies, and also formally assess whether trends in indices of inequality in mortality are significant. Modelling questions include choice between random walk priors for age and time effects as against non-linear regression functions, questions of identifiability when several random effects are present in the death rates model, and the choice of model when both within and out-of-sample performance may be important. A case study application involves 44 small areas in North East London and mortality in five sub-periods (1986-88, 1989-91, 1992-94, 1995-97, 1998-2000) between 1986 and 2000, with the final period used for assessing out-of-sample performance.     

在不可忽略抽样机制下目标变量的估计问题

抽样调查是通过对有限总体的重复抽样,用样本数据对总体的目标变量进行估计,但是若样本的抽样过程与目标变量有关,则样本分布不能代表总体分布,此时用样本数据来估计总体会产生很大的偏差。针对这种在不可忽略的抽样机制下如何进行目标变量的估计问题展开讨论,详细介绍了三种处理该问题的方法并对这三种方法进行了比较,得出第三种概率密度函数的方法是处理该问题比较好的一种方法。     

益贫式增长与社会机会分配

 目前的益贫式增长测度方法局限于从收入或支出角度考察增长是否益贫,没有足够重视教育、医疗、保险等社会服务机会分配的益贫性。本文用基于社会机会函数（SOF）构造机会指数（OI）和机会益贫指数（PIO）的方法测度中国教育和医疗服务机会分配的益贫性及其动态变化,采用CHNS住户调查数据（2000-2009年）进行了实证研究,以此作为考察增长益贫性以及制订和执行更具针对性的益贫策略的有力依据。     

Random Weighting Estimation of Confidence Intervals for Quantiles

This paper presents a new random weighting method for confidence interval estimation for the sample ‐quantile. A theory is established to extend ordinary random weighting estimation from a non‐smoothed function to a smoothed function, such as a kernel function. Based on this theory, a confidence interval is derived using the concept of backward critical points. The resultant confidence interval has the same length as that derived by ordinary random weighting estimation, but is distribution‐free, and thus it is much more suitable for practical applications. Simulation results demonstrate that the proposed random weighting method has higher accuracy than the Bootstrap method for confidence interval estimation.     

Meta-Analysis Software: A Comparative Review

DSTAT, Version 1.10: Available from Lawrence Erlbaum Associates, Inc., 10 Industrial Ave., Mahwah, NJ 07430-2262; phone: 800-926-6579

TRUE EPISTAT, Version 4.0: Available from Epistat Services, 2011 Cap Rock Circle, Richardson, TX 75080; phone: 214-680-1376; fax: 214-680-1303.

FAST*PRO, Version 1.0: Available from Academic Press, Inc., 955 Massachusetts Avenue, Cambridge, MA 02139; phone: 800-321-5068; fax: 800-336-7377.

Meta-analysts conduct studies in which the responses are analytic summary measurements, such as risk differences, effect sizes, p values, or z statistics, obtained from a series of independent studies. The motivation for conducting a meta-analysis is to integrate research findings over studies in order to summarize the evidence about treatment efficacy or risk factors. This article presents a comparative review of three meta-analytic software packages: DSTAT, TRUE EPISTAT, and FAST*PRO.     

An extended random-effects approach to modeling repeated, overdispersed count data

Non-Gaussian outcomes are often modeled using members of the so-called exponential family. The Poisson model for count data falls within this tradition. The family in general, and the Poisson model in particular, are at the same time convenient since mathematically elegant, but in need of extension since often somewhat restrictive. Two of the main rationales for existing extensions are (1) the occurrence of overdispersion, in the sense that the variability in the data is not adequately captured by the model's prescribed mean-variance link, and (2) the accommodation of data hierarchies owing to, for example, repeatedly measuring the outcome on the same subject, recording information from various members of the same family, etc. There is a variety of overdispersion models for count data, such as, for example, the negative-binomial model. Hierarchies are often accommodated through the inclusion of subject-specific, random effects. Though not always, one conventionally assumes such random effects to be normally distributed. While both of these issues may occur simultaneously, models accommodating them at once are less than common. This paper proposes a generalized linear model, accommodating overdispersion and clustering through two separate sets of random effects, of gamma and normal type, respectively. This is in line with the proposal by Booth et al. (Stat Model 3:179-181, 2003). The model extends both classical overdispersion models for count data (Breslow, Appl Stat 33:38-44, 1984), in particular the negative binomial model, as well as the generalized linear mixed model (Breslow and Clayton, J Am Stat Assoc 88:9-25, 1993). Apart from model formulation, we briefly discuss several estimation options, and then settle for maximum likelihood estimation with both fully analytic integration as well as hybrid between analytic and numerical integration. The latter is implemented in the SAS procedure NLMIXED. The methodology is applied to data from a study in epileptic seizures.     

E-optimal design in irregular BIBD settings

When the necessary conditions for a BIBD are satisfied, but no BIBD exists, there is no simple answer for the optimal design problem. This paper identifies the E-optimal information matrices for any such irregular BIBD setting when the number of treatments is no larger than 100. A- and D-optimal designs are typically not E-optimal. An E-optimal design for 15 treatments in 21 blocks of size 5 is found.     

Estimation of tail parameters under type i censoring

This paper is a continuation of previous work concerning the estimation of tail-parameters under Type II censoring (Weissman 1978). The same estimation problem is considered here, this truip under Type I censoring. A sample of size n is censored below aE a given level x₀it is assumed that che underlying distriibution .function (df)belogs to the domain of attraction of a known extreme-value distribution and that K - K(x_o) , the number of observed values, remains finite as on - ∞ . We offer here estimators, which are asymptotically maximum likelihood estimators (MLE's), for quantiles associated with the tail of F such as location and scale parameters, quantiles and F(x) itself (for x in the tail). The results are applied to two illustrative examples.     

Generalized bartlett correction

This paper provides a general method of modifying a statistic of interest in such a way that the distribution of the modified statistic can be approximated by an arbitrary reference distribution to an order of accuracy of O(n ^-1/2) or even O(n ^-1). The reference distribution is usually the asymptotic distribution of the original statistic. We prove that the multiplication of the statistic by a suitable stochastic correction improves the asymptotic approximation to its distribution. This paper extends the results of the closely related paper by Cordeiro and Ferrari (1991) to cope with several other statistical tests. The resulting expression for the adjustment factor requires knowledge of the Edgeworth-type expansion to order O(n^-1) for the distribution of the unmodified statistic. In practice its functional form involves some derivatives of the reference distribution. Certain difference between the cumulants of appropriate order in n of the unmodified statistic and those of its first-order approximation, and the unmodified statistic itself. Some applications are discussed.     

Combining information from multiple surveys for assessing health disparities

Despite advances in public health practice and medical technology, the disparities in health among the various racial/ethnic and socioeconomic groups remain a concern which has prompted the Department of Human and Health Services to designate the elimination of disparities in health as an overarching goal of Healthy People 2010. To assess the progress towards this goal, suitable measures are needed at the population level that can be tracked over time; Statistical inferential procedures have to be developed for these population level measures; and the data sources have to be identified to allow for such inferences to be conducted. Popular data sources for health disparities research are large surveys such the National Health and Interview Survey (NHIS) or the Behavior Risk Factor Surveillance System (BRFSS). The self-report disease status collected in these surveys may be inaccurate and the errors may be correlated with variables used in defining the groups. This article uses the National Health and Nutritional Examination Survey (NHANES) 99-00 to assess the extent of error in the self-report disease status; uses a Bayesian framework develop corrections for the self-report disease status in the National Health Interview Survey (NHIS) 99-00; and compares inferences about various measures of health disparities, with and without correcting for measurement error. The methodology is illustrated using the disease outcome hypertension, a common risk factor for cardiovascular disease.     

New critical values for the generalized t and generalized rank-sum procedures

Student's t test as well as Wilcoxon's rank-sum test may be inefficient in situations where treatments bring about changes in both location and scale. In order to rectify this situation, O'Brien (1988, Journal of the American Statistical Association 83, 52-61) has proposed two new statistics, the generalized t and generalized rank-sum procedures, which may be much more powerful than their traditional counterparts in such situations. Recently, however, Blair and Morel (1991, Statistics in Medicine in press) have shown that referencing these new statistics to standard F tables as recommended by O'Brien results in inflations of Type I errors. This paper provides tables of critical values which do not produce such inflations. Use of these new critical values results in Type I error rates near nominal levels for the generalized t statistic and slightly conservative rates for the generalized rank-sum test. In addition to the critical values, some new power results are given for the generalized tests.     

Algorithmic and analytical construction of efficient designs in small blocks for comparing consecutive pairs of treatments

Optimal block designs in small blocks are explored under the A-, E- and D-criteria when the treatments have a natural ordering and interest lies in comparing consecutive pairs of treatments. We first formulate the problem via approximate theory which leads to a convenient multiplicative algorithm for obtaining A-optimal design measures. This, in turn, yields highly efficient exact designs, under the A-criterion, even when the number of blocks is rather small. Moreover, our approach is seen to allow nesting of such efficient exact designs which is an advantage when the resources for the experiment are available in possibly several stages. Illustrative examples are given and tables of A-optimal design measures are provided. Approximate theory is also seen to yield analytical results on E- and D-optimal design measures.     

居民储蓄的长期均衡和短期波动——中国：1991-2000年的实证分析

一、引言改革开放以来中国的收入流程出现了由政府主导型向居民主导型的巨大转化 ,居民已成为我国总储蓄的最重要主体。居民储蓄向投资转化的巨大潜力已成为中国经济增长的重要源泉之一 ,进入“储蓄推进”的经济增长阶段是我国的必然[1] 。因此近年许多学者对中国居民储蓄进行了大量研究 ,形成了许多重要成果。但现有文献在两个方面值得注意。一是研究的样本区间。现有研究文献大多是基于改革开放以来甚至是建国以来的。但在 90年代影响中国居民储蓄的经济环境发生了巨大变化 ,如资本市场迅速发展、社会保障体系改革加快、消费信贷措施出台…