An Optimal Procedure for Sample Coordination Using Multiple Objective Functions and Nearest Proportional to IPPS Size Sampling Designs

The sample coordination problem involves maximization or minimization of overlap of sampling units in different/repeated surveys. Several optimal techniques using transportation theory, controlled rounding, and controlled selection have been suggested in literature to solve the sample coordination problem. In this article, using the multiple objective programming, we propose a method for sample coordination which facilitates variance estimation using the Horvitz–Thompson estimator. The proposed procedure can be applied to any two-sample surveys having identical universe and stratification. Some examples are discussed to demonstrate the utility of the proposed procedure.     

Asymptotic Expansions in Multi-Group Analysis of Moment Structures with an Application to Linearized Estimators

Asymptotic expansions of the joint distributions of functions of sample means and central moments up to an arbitrary order in multiple populations are given by Edgeworth expansions. The asymptotic distributions of the parameter estimators in moment structures under null/fixed alternative hypotheses and the chi-square statistics based on asymptotically distribution-free theory under fixed alternatives are given as applications of the above results. Asymptotic expansions of the null distributions of the chi-square statistics are also derived. For parameter estimators with the chi-square statistic, the linearized estimators are dealt with as well as fully iterated estimators.     

The Extent of Gross Errors Eliminated by Robust Multiple Linear Regressions

Robust estimation methods can effectively eliminate the influence of gross errors on parameter estimation. However, the extent of gross errors eliminated (EGEE) by robust estimation methods is far-reaching. This article presents a new approach to determine EGEE by robust estimation method. Taking multiple linear regressions (2–5) as examples, simulation experiments were conducted to compare the EGEE of 14 frequently used robust estimation methods. This article confirms several additional efficient robust estimation methods for dealing with multiple linear regressions, as well as the minimum number of observations needed to eliminate gross errors in certain ranges completely.     

On simultaneous confidence intervals based on rank-estimates with application to analysis of gene expression data

Abstract

Inferential methods based on ranks present robust and powerful alternative methodology for testing and estimation. In this article, two objectives are followed. First, develop a general method of simultaneous confidence intervals based on the rank estimates of the parameters of a general linear model and derive the asymptotic distribution of the pivotal quantity. Second, extend the method to high dimensional data such as gene expression data for which the usual large sample approximation does not apply. It is common in practice to use the asymptotic distribution to make inference for small samples. The empirical investigation in this article shows that for methods based on the rank-estimates, this approach does not produce a viable inference and should be avoided. A method based on the bootstrap is outlined and it is shown to provide a reliable and accurate method of constructing simultaneous confidence intervals based on rank estimates. In particular it is shown that commonly applied methods of normal or t-approximation are not satisfactory, particularly for large-scale inferences. Methods based on ranks are uniquely suitable for analysis of microarray gene expression data since they often involve large scale inferences based on small samples containing a large number of outliers and violate the assumption of normality. A real microarray data is analyzed using the rank-estimate simultaneous confidence intervals. Viability of the proposed method is assessed through a Monte Carlo simulation study under varied assumptions.     

Multidimensional Poverty: Measurement,Estimation, and Inference

Calibration in macroeconomics involves choosing fre parameters by matching certain moments of simulted models with those of data. We formally examine this method by treating the process of calibration as an econometric estimator. A numerical version of the Mehra-Prescott (1985) economy is the setting for an evaluation of calibration estimators via Monte Carlo methods. While these estimators sometimes have reasonable finite-sample properties they are not robust to mistakes in setting non-free parameters. In contrast, generalized method-of-moments (GMM) estimators have satisfactory finite-sample characteristics, quick convergence, and informational requirements less stringent than those of calibration estimators. In dynamic equilibrium models in which GMM is infeasible we offer some suggestions for improving estimates based on calibration methodology.     

多元复合认同——以境外俄罗斯人为例

苏联解体后,分布在独联体境内的2500万俄罗斯人,面临民族认同的重新选择现象,新的认同形塑是一个重要问题,“祖国”的消失,心里上的“多元认同”和实体上的“复合身份”,以及该族群的居住国政策和俄罗斯联邦的同胞政策都对其造成影响,多样的认同的存在,不只单是平行的现象,而是一种同一的多层次结构,彼此并不必然会在认同选择的过程中出现冲突。本文试图通过分析这种影响的多重因子,探寻族群属性是否是一种复合体,寻求民族认同的多元复合性。     

Imputing missing quality of life data as covariate in survival analysis of the International Breast Cancer Study Group Trials VI and VII

Quality of life (QoL) was an important endpoint in the adjuvant breast cancer trials International Breast Cancer Study Group (IBCSG) Trial VI and VII. Here, QoL was considered as a time-dependent effect. The hypothesis explored is that poorer QoL throughout the trial is associated with poorer disease-free survival (DFS) and vice-versa. Potential bias in the parameter estimates is an important concern associated with missing observations. Standard simple and multiple imputation methods were applied to missing QoL assessments before analysis in a time-dependent Cox model. There was no evidence that the patient's QoL is related to the patient's DFS.     

Orthogonal polynomials,repeated measures,and SPSS

We discuss the construction of discrete orthonormal polynomials, using MAPLE procedures. We also study two important applications of these polynomials in statistics: in multiple linear regression and in repeated measures analysis. In particular, it is argued that the tests given by SPSS for linear and other trends in a within-subject factor are inefficient. Examples are given, including two (from psychology and medicine, respectively) which involve repeated measures and SPSS. Extensive tables of discrete orthornormal polynomials are given in the Appendix.     

Robust multi-criteria sorting with the outranking preference model and characteristic profiles

We present a new multiple criteria sorting approach that uses characteristic profiles for defining the classes and outranking relation as the preference model, similarly to the Electre Tri-C method. We reformulate the conditions for the worst and best class assignments of Electre Tri-C to increase comprehensibility of the method and interpretability of the results it delivers. Then, we present a disaggregation procedure for inferring the set of outranking models compatible with the given preference information, and use the set in deriving, for each decision alternative, the necessary and possible assignments. Furthermore, we introduce simplified assignment procedures and prove that they maintain a no class jumps-property in the possible assignments. Application of the proposed approach is demonstrated by classifying 40 land zones in 4 classes representing different risk levels.