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A variety of biological and social science data come in the form of cross-classified tables of counts, commonly referred to as contingency tables. Until recent years the statistical and computational techniques available for the analysis of cross-classified data were quite limited. This book presents some of the recent work on the statistical analysis of cross-classified data using longlinear models, especially in the multidimensional situation.
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Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
From the reviews of the second edition:
"This is a great book on the analysis of cross-classified categorical data, particularly for non-statistical readers. The need for analyzing cross-classified categorical data has been growing rapidly ... . The loglinear models and maximum-likelihood estimates discussed in this book are very helpful for practitioners to get hands on categorical data and perform sound analyses. This book is comprised of 8 chapters. ... is a useful book and I recommend it to data analysts in practical and academic fields." (Zhen Mei, Zentralblatt MATH, Vol. 1134 (12), 2008)
"This Springer edition is a reprint of the first edition published by MIT Press in 1977. ... this a useful resource book for applied statisticians with good theoretical background." (Technometrics, Vol. 50 (4), November, 2008)
"This is a great book on the analysis of cross-classified categorical data, particularly for non-statistical readers. The need for analyzing cross-classified categorical data has been growing rapidly ... . The loglinear models and maximum-likelihood estimates discussed in this book are very helpful for practitioners to get hands on categorical data and perform sound analyses. This book is comprised of 8 chapters. ... is a useful book and I recommend it to data analysts in practical and academic fields." (Zhen Mei, Zentralblatt MATH, Vol. 1134 (12), 2008)
"This Springer edition is a reprint of the first edition published by MIT Press in 1977. ... this a useful resource book for applied statisticians with good theoretical background." (Technometrics, Vol. 50 (4), November, 2008)