From finite sample to asymptotic methods in statistics /

People are inundated with massive amounts of data due to the proliferation of computer technology and computer usage. Analyzing such data with precision requires more accurate approximations; consequently, there is a need for more profound mathematical theory/analysis in this area. Up to now, discussions on this topic have been largely limited to specialized articles and books that are not generally accessible to a large group of potential users. In an earlier book, Large Sample Methods in Statistics (1993), Sen (Univ. of North Carolina, Chapel Hill) and Singer (Univ. of Sao Paulo, Brazil) were able to bridge this gap. In the current work, Sen and Singer along with Pedroso de Lima (Univ. of Sao Paulo, Brazil) successfully continue this effort by examining the theory concerning exact statistical inference versus approximations through discussions of finite to large samples and asymptotic statistical inference. Each of the work's 11 chapters includes exercises, and all but the first chapter contains a "Concluding Notes" section. Readers must have a solid background in mathematics and statistics to understand the text. Summing Up: Highly recommended. Graduate students and researchers/faculty. D. J. Gougeon University of Scranton