Excerpt from Identifying Rank-Influential Groups of Observations in Linear Regression Modeling
Of course, it may be that while the matrix X is rank deficient, the regression (1) is still useful for its estimates of contrasts (b; E . How ever, we are particularly concerned about those situations when the sole purpose of the analysis is to infer about contrasts which are ines timable or would be inestimable without the presence of a particular case group.
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Excerpt from Identifying Rank-Influential Groups of Observations in Linear Regression Modeling
Of course, it may be that while the matrix X is rank deficient, the regression (1) is still useful for its estimates of contrasts (b; E . How ever, we are particularly concerned about those situations when the sole purpose of the analysis is to infer about contrasts which are ines timable or would be inestimable without the presence of a particular case group.
About the Publisher
Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com
This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
"About this title" may belong to another edition of this title.
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Paperback. Condition: New. Print on Demand. This book explores the identification of groups of observations that exert a strong influence on the results of linear regression modeling. The author presents mathematical characterizations of these groups and proposes methods using factor-analytic rotation algorithms and projection pursuit techniques. One method focuses on finding directions in which many cases are extreme inliers, while the other examines directions in which most cases are outlying. Both methods avoid a combinatorial search through possible subsets of cases, making them computationally feasible for data sets of various sizes. The author provides examples to illustrate how the methods perform, and the book concludes with a discussion of related diagnostic methods. The insights provided by this book are significant for researchers and practitioners who use linear regression modeling to analyze data, as they highlight the importance of considering the impact of influential groups of observations on the reliability and validity of their results. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book. print-on-demand item. Seller Inventory # 9781332264612_0
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # LW-9781332264612
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # LW-9781332264612
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Condition: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar. Seller Inventory # 26072839/2