Microdata Access and Labor Market Research: The U. S. Experience

Summary: The paper illustrates the value of broad researcher access to survey and administrative microdata using examples drawn from the U. S. experience, outlining how analyses of trends in earnings inequality, poverty and employment dynamics using such data have benefited policy makers and contributed to improvements in statistical agency data products. Methods of facilitating researcher access, including the release of public use files, the use of licensing agreements, and the establishment of research data centers, are discussed. * The author thanks Anne Polivka, Marilyn Seastrom and two anonymous referees for helpful comments on an earlier draft of the paper.     

The Hausdorff Space and Applied Statistics: A View from the U.S.S.R.

The article focuses on the debate in the pages of the Soviet statistical journal Vestnik Statistiki as to the existence and relevance of applied statistics as a separate scientific discipline. The contents of four letters to the editor and relevant editorial comments that appeared in this journal between October 1985 and July 1987 are analyzed.     

Krishnaiah,P.R. and C.R. Rao (eds):Sampling. Handbook of Statistics,vol. 6

In a recent paper Yager (1988) introduced a special type of aggregation operators which he called OWA-operators and applied it to multicriteria decisionmaking. In our paper we give a more general definition of such operators and investigate in some detail the structural properties of them. In particular we introduce a well motivated ordering relation on weight systems which is different but compatible with Yager's degree of “orness”. Our results have immediate applications to the theory of generalized quantifiers as also discussed by Yager. Details concerning this topic will appear in a future paper.     

Extrapolation of subsampling distribution estimators: The i.i.d. and strong mixing cases

Politis & Romano (1994) proposed a general subsampling methodology for the construction of large‐sample confidence regions for an arbitrary parameter under minimal conditions. Nevertheless, the subsampling distribution estimators may sometimes be inefficient (in the case of the sample mean of i.i.d. data, for instance) as compared to alternative estimators such as the bootstrap and/or the asymptotic normal distribution (with estimated variance). The authors investigate here the extent to which the performance of subsampling distribution estimators can be improved by interpolation and extrapolation techniques, while at the same time retaining the robustness property of consistent distribution estimation even in nonregular cases; both i.i.d. and weakly dependent (mixing) observations are considered.