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In Politis and Romano (1993), different block resampling estimators of variance of general linear statistics, e.g. a sample mean, were proposed under the assumption of stationarity. In the present paper such estimators of variance of sample means, computed from nonstationary spatially indexed data {Xi : i E A}, where A is a finite subset of the integer lattice Z2, are studied. Consistency of estimators of variance will be shown for the following kind of data: Observations taken from different lattice points are allowed to come from different distributions, and the dependence structure is allowed to differ over the lattice. We assume that all observed values are from distributions with the same expected value, or with expected values that decompose additively into directional components. Furthermore, it will be assumed that observations separated by a certain distance are independent.

This article presents a way of dealing scientifically with the problem of comparison among dynamically evolving units of analysis in social science, namely by using methods from theoretical, computational & mathematical biology & phylogeny (evolutionary tree) analysis. First, the standard political science & static method of comparative analysis is criticized. Second, some important aspects of the dynamic comparison are presented from its multidisciplinary & historic context, commenting on the compatibility with evolutionary institutional as well as universal Darwinian theoretical perspectives. Third, dynamic comparison is approached using first techniques from standard social science statistical software (SPSS) & then from molecular genetics (MEGA, Molecular Evolutionary Analysis). Examples of evolutionary tree analyses of the European nations are presented & discussed. Similarities & dissimilarities between social science & biological applications of the dynamic comparison techniques are discussed, emphasizing the importance of openness to new techniques & application of panel data for elaboration of new methods of dynamic comparative analyses in social sciences. 2 Tables, 9 Figures, 68 References. Adapted from the source document.