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analysis (MDA). Unlike similar texts, it treats the MDA problems as optimization problems on matrix manifolds defined by the MDA model parameters;
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and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis. The book begins by explaining;
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researchers who are not familiar with matrix-intensive formulations of multivariate data analysis. The book begins by explaining fundamental matrix;
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Covering statistical analysis on the two special manifolds, the Stiefel manifold and the Grassmann manifold, this book is designed as a;
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, the authors show how the analysis of the data is performed using Stata(R), SAS(R), and SPSS(R). The discussed approaches are also applicable;
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multivariate techniques An extensive survey of the world literature on multivariate analysis An in-depth review of matrix theory A disk including;
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-oriented introduction to multivariate data analysis for the nonstatistician by focusing on the fundamental concepts that affect the use of specific;
Vergelijkbare producten zoals Multivariate Data Analysis
-oriented introduction to multivariate data analysis for the nonstatistician by focusing on the fundamental concepts that affect the use of specific;
Vergelijkbare producten zoals Multivariate Data Analysis
-oriented introduction to multivariate data analysis for the nonstatistician by focusing on the fundamental concepts that affect the use of specific;
Vergelijkbare producten zoals Multivariate Data Analysis
covariance matrices Matrix Analysis for Statistics, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses on matrix;
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analysis and for developing new statistical methods. Most books on the subject have historically discussed univariate, multivariate, and mixed;
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. Prerequisites include a course on factorial analysis of variance. It does not assume a working knowledge of matrix algebra.;
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methods. Until now, however, most of the material on random matrices could only be found scattered in various statistical journals. Matrix Variate;
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eighth edition of Multivariate Data Analysis provides an updated perspective on the analysis of all types of data as well as introducing some new;
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Many problems in the sciences and engineering can be rephrased as optimization problems on matrix search spaces endowed with a so-called;
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This book integrates methods and data based on interpretations relevant to multivariate analysis. It uses an applications approach and;
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understanding multivariate methods."Mathematical Tools for Applied Multivariate Analysis, Revised Edition illustrates major concepts in matrix;
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This volume is a tribute to Professor Dietrich von Rosen on the occasion of his 65th birthday. It contains a collection of twenty original;
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* Structural Graph Theory Downunder * Tropical Geometry and Mirror Symmetry * Early Career Researchers Workshop on Geometric Analysis;
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Geared toward upper-level undergraduates and graduate students, this two-part treatment deals with the foundations of multivariate analysis;
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jargon. With extensive exercises following every chapter, 'Applied Multivariate Data Analysis' is a valuable resource for students on applied;
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This book on multivariate models, statistical inference, and data analysis contains deep coverage of multivariate non-normal distributions;
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applications of applied statistics, statistical testing, and multivariate data analysis. Drawing on practical examples from the business world, it;
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applications of applied statistics, statistical testing, and multivariate data analysis. Drawing on practical examples from the business world, it;
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Data Analysis provides one of the first thorough treatments of the theory and methodology for analyzing data on manifolds. It also presents;
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Object Data Analysis provides one of the first thorough treatments of the theory and methodology for analyzing data on manifolds. It also;
Vergelijkbare producten zoals Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis
For graduate-level courses in Marketing Research, Research Design and Data Analysis . Well-suited for the non-statistician, this;
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