Bilinear Regression Analysis
Bilinear Regression Analysis

Author: Dietrich von Rosen

Publisher: Springer

Published: 2018-08-02

Total Pages: 473

ISBN-13: 3319787845

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This book expands on the classical statistical multivariate analysis theory by focusing on bilinear regression models, a class of models comprising the classical growth curve model and its extensions. In order to analyze the bilinear regression models in an interpretable way, concepts from linear models are extended and applied to tensor spaces. Further, the book considers decompositions of tensor products into natural subspaces, and addresses maximum likelihood estimation, residual analysis, influential observation analysis and testing hypotheses, where properties of estimators such as moments, asymptotic distributions or approximations of distributions are also studied. Throughout the text, examples and several analyzed data sets illustrate the different approaches, and fresh insights into classical multivariate analysis are provided. This monograph is of interest to researchers and Ph.D. students in mathematical statistics, signal processing and other fields where statistical multivariate analysis is utilized. It can also be used as a text for second graduate-level courses on multivariate analysis.

Bilinear and Trilinear Regression Models with Structured Covariance Matrices
Bilinear and Trilinear Regression Models with Structured Covariance Matrices

Author: Joseph Nzabanita

Publisher: Linköping University Electronic Press

Published: 2015-05-21

Total Pages: 51

ISBN-13: 9175190702

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This thesis focuses on the problem of estimating parameters in bilinear and trilinear regression models in which random errors are normally distributed. In these models the covariance matrix has a Kronecker product structure and some factor matrices may be linearly structured. The interest of considering various structures for the covariance matrices in different statistical models is partly driven by the idea that altering the covariance structure of a parametric model alters the variances of the model’s estimated mean parameters. Firstly, the extended growth curve model with a linearly structured covariance matrix is considered. The main theme is to find explicit estimators for the mean and for the linearly structured covariance matrix. We show how to decompose the residual space, the orthogonal complement to the mean space, into appropriate orthogonal subspaces and how to derive explicit estimators of the covariance matrix from the sum of squared residuals obtained by projecting observations on those subspaces. Also an explicit estimator of the mean is derived and some properties of the proposed estimators are studied. Secondly, we study a bilinear regression model with matrix normally distributed random errors. For those models, the dispersion matrix follows a Kronecker product structure and it can be used, for example, to model data with spatio-temporal relationships. The aim is to estimate the parameters of the model when, in addition, one covariance matrix is assumed to be linearly structured. On the basis of n independent observations from a matrix normal distribution, estimating equations, a flip-flop relation, are established. At last, the models based on normally distributed random third order tensors are studied. These models are useful in analyzing 3-dimensional data arrays. In some studies the analysis is done using the tensor normal model, where the focus is on the estimation of the variance-covariance matrix which has a Kronecker structure. Little attention is paid to the structure of the mean, however, there is a potential to improve the analysis by assuming a structured mean. We formally introduce a 2-fold growth curve model by assuming a trilinear structure for the mean in the tensor normal model and propose an estimation algorithm for parameters. Also some extensions are discussed.

Linear Models in Statistics
Linear Models in Statistics

Author: Alvin C. Rencher

Publisher: John Wiley & Sons

Published: 2008-01-07

Total Pages: 690

ISBN-13: 0470192607

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The essential introduction to the theory and application of linear models—now in a valuable new edition Since most advanced statistical tools are generalizations of the linear model, it is neces-sary to first master the linear model in order to move forward to more advanced concepts. The linear model remains the main tool of the applied statistician and is central to the training of any statistician regardless of whether the focus is applied or theoretical. This completely revised and updated new edition successfully develops the basic theory of linear models for regression, analysis of variance, analysis of covariance, and linear mixed models. Recent advances in the methodology related to linear mixed models, generalized linear models, and the Bayesian linear model are also addressed. Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. This modern Second Edition features: New chapters on Bayesian linear models as well as random and mixed linear models Expanded discussion of two-way models with empty cells Additional sections on the geometry of least squares Updated coverage of simultaneous inference The book is complemented with easy-to-read proofs, real data sets, and an extensive bibliography. A thorough review of the requisite matrix algebra has been addedfor transitional purposes, and numerous theoretical and applied problems have been incorporated with selected answers provided at the end of the book. A related Web site includes additional data sets and SAS® code for all numerical examples. Linear Model in Statistics, Second Edition is a must-have book for courses in statistics, biostatistics, and mathematics at the upper-undergraduate and graduate levels. It is also an invaluable reference for researchers who need to gain a better understanding of regression and analysis of variance.

Artificial Neural Networks and Machine Learning – ICANN 2020
Artificial Neural Networks and Machine Learning – ICANN 2020

Author: Igor Farkaš

Publisher: Springer Nature

Published: 2020-10-19

Total Pages: 891

ISBN-13: 3030616096

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The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks, ICANN 2020, held in Bratislava, Slovakia, in September 2020.* The total of 139 full papers presented in these proceedings was carefully reviewed and selected from 249 submissions. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics and biosignal analysis, cognitive models, neural network theory and information theoretic learning, and robotics and neural models of perception and action. *The conference was postponed to 2021 due to the COVID-19 pandemic.

Designing for Product Sound Quality
Designing for Product Sound Quality

Author: Richard Lyon

Publisher: CRC Press

Published: 2000-06-06

Total Pages: 232

ISBN-13: 1482270412

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"Provides previously unavailable material in sound quality crucial for a more effective design process. Presents all aspects of product sound quality, such as ""rules of thumb"" and design formulas and charts. Covers sound radiation and targeting, resolving, and testing design features."

Correspondence Analysis in Practice, Third Edition
Correspondence Analysis in Practice, Third Edition

Author: Michael Greenacre

Publisher: CRC Press

Published: 2017-01-20

Total Pages: 572

ISBN-13: 1315352958

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Drawing on the author’s 45 years of experience in multivariate analysis, Correspondence Analysis in Practice, Third Edition, shows how the versatile method of correspondence analysis (CA) can be used for data visualization in a wide variety of situations. CA and its variants, subset CA, multiple CA and joint CA, translate two-way and multi-way tables into more readable graphical forms — ideal for applications in the social, environmental and health sciences, as well as marketing, economics, linguistics, archaeology, and more. Michael Greenacre is Professor of Statistics at the Universitat Pompeu Fabra, Barcelona, Spain, where he teaches a course, amongst others, on Data Visualization. He has authored and co-edited nine books and 80 journal articles and book chapters, mostly on correspondence analysis, the latest being Visualization and Verbalization of Data in 2015. He has given short courses in fifteen countries to environmental scientists, sociologists, data scientists and marketing professionals, and has specialized in statistics in ecology and social science.

A Biostatistics Toolbox for Data Analysis
A Biostatistics Toolbox for Data Analysis

Author: S. Selvin

Publisher: Cambridge University Press

Published: 2015-10-20

Total Pages: 579

ISBN-13: 1107113083

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A Biostatistics Toolbox for Data Analysis delivers a sophisticated package of statistical methods for advanced master's (MPH) and PhD students in public health and epidemiology who are involved in the analysis of data. The book's statistical tools are organized into sections with similar objectives, each of which is accompanied by complete instructions, explanations, detailed examples, and advice on relevant issues and potential pitfalls.

Principles of Plant Genetics and Breeding
Principles of Plant Genetics and Breeding

Author: George Acquaah

Publisher: John Wiley & Sons

Published: 2020-09-28

Total Pages: 855

ISBN-13: 1119626692

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The revised edition of the bestselling textbook, covering both classical and molecular plant breeding Principles of Plant Genetics and Breeding integrates theory and practice to provide an insightful examination of the fundamental principles and advanced techniques of modern plant breeding. Combining both classical and molecular tools, this comprehensive textbook describes the multidisciplinary strategies used to produce new varieties of crops and plants, particularly in response to the increasing demands to of growing populations. Illustrated chapters cover a wide range of topics, including plant reproductive systems, germplasm for breeding, molecular breeding, the common objectives of plant breeders, marketing and societal issues, and more. Now in its third edition, this essential textbook contains extensively revised content that reflects recent advances and current practices. Substantial updates have been made to its molecular genetics and breeding sections, including discussions of new breeding techniques such as zinc finger nuclease, oligonucleotide directed mutagenesis, RNA-dependent DNA methylation, reverse breeding, genome editing, and others. A new table enables efficient comparison of an expanded list of molecular markers, including Allozyme, RFLPs, RAPD, SSR, ISSR, DAMD, AFLP, SNPs and ESTs. Also, new and updated “Industry Highlights” sections provide examples of the practical application of plant breeding methods to real-world problems. This new edition: Organizes topics to reflect the stages of an actual breeding project Incorporates the most recent technologies in the field, such as CRSPR genome edition and grafting on GM stock Includes numerous illustrations and end-of-chapter self-assessment questions, key references, suggested readings, and links to relevant websites Features a companion website containing additional artwork and instructor resources Principles of Plant Genetics and Breeding offers researchers and professionals an invaluable resource and remains the ideal textbook for advanced undergraduates and graduates in plant science, particularly those studying plant breeding, biotechnology, and genetics.

Machine Learning and Knowledge Discovery in Databases, Part II
Machine Learning and Knowledge Discovery in Databases, Part II

Author: Dimitrios Gunopulos

Publisher: Springer Science & Business Media

Published: 2011-09-06

Total Pages: 702

ISBN-13: 3642237827

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This three-volume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. The 121 revised full papers presented together with 10 invited talks and 11 demos in the three volumes, were carefully reviewed and selected from about 600 paper submissions. The papers address all areas related to machine learning and knowledge discovery in databases as well as other innovative application domains such as supervised and unsupervised learning with some innovative contributions in fundamental issues; dimensionality reduction, distance and similarity learning, model learning and matrix/tensor analysis; graph mining, graphical models, hidden markov models, kernel methods, active and ensemble learning, semi-supervised and transductive learning, mining sparse representations, model learning, inductive logic programming, and statistical learning. a significant part of the papers covers novel and timely applications of data mining and machine learning in industrial domains.

The Analysis of Sports Forecasting
The Analysis of Sports Forecasting

Author: William S. Mallios

Publisher: Springer Science & Business Media

Published: 2013-03-09

Total Pages: 301

ISBN-13: 1475767137

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Given the magnitude of currency speculation and sports gambling, it is surprising that the literature contains mostly negative forecasting results. Majority opinion still holds that short term fluctuations in financial markets follow random walk. In this non-random walk through financial and sports gambling markets, parallels are drawn between modeling short term currency movements and modeling outcomes of athletic encounters. The forecasting concepts and methodologies are identical; only the variables change names. If, in fact, these markets are driven by mechanisms of non-random walk, there must be some explanation for the negative forecasting results. The Analysis of Sports Forecasting: Modeling Parallels Between Sports Gambling and Financial Markets examines this issue.