Recent Developments in Multivariate and Random Matrix Analysis: Festschrift in Honour of Dietrich von Rosen, Holgersson Thomas, Singull Martin
Автор: Gregory R. Hancock, Jeffrey R. Harring, George B. Macready Название: Advances in Latent Class Analysis: A Festschrift in Honor of C. Mitchell Dayton ISBN: 164113562X ISBN-13(EAN): 9781641135627 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 14276.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: What is latent class analysis? If you asked that question thirty or forty years ago you would have gotten a different answer than you would today. Closer to its time of inception, latent class analysis was viewed primarily as a categorical data analysis technique, often framed as a factor analysis model where both the measured variable indicators and underlying latent variables are categorical. Today, however, it rests within much broader mixture and diagnostic modeling framework, integrating measured and latent variables that may be categorical and/or continuous, and where latent classes serve to define the subpopulations for whom many aspects of the focal measured and latent variable model may differ. For latent class analysis to take these developmental leaps required contributions that were methodological, certainly, as well as didactic. Among the leaders on both fronts was C. Mitchell “Chan” Dayton, at the University of Maryland, whose work in latent class analysis spanning several decades helped the method to expand and reach its current potential. The current volume in the Center for Integrated Latent Variable Research (CILVR) series reflects the diversity that is latent class analysis today, celebrating work related to, made possible by, and inspired by Chan’s noted contributions, and signaling the even more exciting future yet to come.
Автор: Gregory R. Hancock, Jeffrey R. Harring, George B. Macready Название: Advances in Latent Class Analysis: A Festschrift in Honor of C. Mitchell Dayton ISBN: 1641135611 ISBN-13(EAN): 9781641135610 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 7623.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: What is latent class analysis? If you asked that question thirty or forty years ago you would have gotten a different answer than you would today. Closer to its time of inception, latent class analysis was viewed primarily as a categorical data analysis technique, often framed as a factor analysis model where both the measured variable indicators and underlying latent variables are categorical. Today, however, it rests within much broader mixture and diagnostic modeling framework, integrating measured and latent variables that may be categorical and/or continuous, and where latent classes serve to define the subpopulations for whom many aspects of the focal measured and latent variable model may differ. For latent class analysis to take these developmental leaps required contributions that were methodological, certainly, as well as didactic. Among the leaders on both fronts was C. Mitchell “Chan” Dayton, at the University of Maryland, whose work in latent class analysis spanning several decades helped the method to expand and reach its current potential. The current volume in the Center for Integrated Latent Variable Research (CILVR) series reflects the diversity that is latent class analysis today, celebrating work related to, made possible by, and inspired by Chan’s noted contributions, and signaling the even more exciting future yet to come.
Описание: The contents of the papers evolve around multivariate analysis and random matrices with topics such as high-dimensional analysis, goodness-of-fit measures, variable selection and information criteria, inference of covariance structures, the Wishart distribution and growth curve models.
Описание: The collection and analysis of data play an important role in many fields of science and technology, such as computational biology, quantitative finance, information engineering, machine learning, neuroscience, medicine, and the social sciences.
Описание: The collection and analysis of data play an important role in many fields of science and technology, such as computational biology, quantitative finance, information engineering, machine learning, neuroscience, medicine, and the social sciences.
Описание: The articles in this collection are a sampling of some of the research presented during the conference "Stochastic Analysis and Related Topics", held in May of 2015 at Purdue University in honor of the 60th birthday of Rodrigo Banuelos.
Описание: Preface.- Contents.- List of Contributors.- On Nearly Linear Recurrence Sequences, Shigeki Akiyama, Jan-Hendrik Evertse and Attila Pethц.- Risk Theory with Affine Dividend Payment Strategies, Hansjцrg Albrecher and Arian Cani.- A Discrepancy Problem: Balancing Infinite Dimensional Vectors, Jуzsef Beck.- Squares with Three Nonzero Digits, Michael A. Bennett and Adrian-Maria Scheerer.- On the density of coprime tuples of the form (n; bf1(n)c; : bfk(n)c), where f1; : fk are functions from a Hardy field, Vitaly Bergelson and Florian Karl Richter.- On the uniform theory of lacunary series, Istvбn Berkes.- Diversity in Parametric Families of Number Fields, Yuri Bilu and Florian Luca.- Local Oscillations in Moderately Dense Sequences of Primes, Jцrg Brьdern and Christian Elsholtz.- Sums of the digits in bases 2 and 3, Jean-Marc Deshouillers, Laurent Habsieger, Shanta Laishram and Bernard Landreau.- On the Discrepancy of Halton-Kronecker Sequences, Michael Drmota, Roswitha Hofer and Gerhard Larcher.- More on Diophantine Sextuples, Andrej Dujella and Matija Kazalicki.- Effective Results for Discrimant Equations Over Finitely Generated Integral Domains, Jan-Hendrik Evertse and Kбlmбn Gyцry.- Quasi-equivalence of Heights and Runge's Theorem, Philipp Habegger.- On the Monoid Generated by a Lucas Sequence, Clemens Heuberger and Stephan Wagner.- Measures of pseudorandomness: Arithmetic Autocorrelation and correlation Measure, Richard Hofer, Lбszlу Mйrai and Arne Winterhof.- On Multiplicative Independent Bases for Canonical Number Systems in Cyclotomic Number Fields, Manfred G. Madritsch, Paul Surer and Volker Ziegler.- Refined Estimates for Exponential Sums and a Problem Concerning the Product of Three L-Series, Werner Georg Nowak.- Orbits of Algebraic Dynamical Systems in Subgroups and Subfields, Alina Ostafe and Igor E. Shparlinski.- Patterns of Primes in Arithmetic Progressions, Jбnos Pintz.- On Simple Linear Recurrences, Andrzej Schinzel.- Equivalence of the Logarithmically Averaged Chowla and Sarnak Conjectures, Terence Tao.- Discrepancy Bounds for -Adic Halton Sequences, Jцrg M. Thuswaldner.
Автор: Filipiak Katarzyna, Markiewicz Augustyn, Von Rosen Dietrich Название: Multivariate, Multilinear and Mixed Linear Models ISBN: 3030754936 ISBN-13(EAN): 9783030754938 Издательство: Springer Цена: 23757.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Preface.- Holonomic gradient method for multivariate distribution theory (Akimichi Takemura).- From normality to skewed multivariate distributions: a personal view (Tхnu Kollo).- Multivariate moments in multivariate analysis (Jolanta Pielaszkiewicz and Dietrich von Rosen).- Regularized estimation of covariance structure through quadratic loss function (Defei Zhang, Xiangzhao Cui, Chun Li, Jine Zhao, Li Zeng, and Jianxin Pan).- Separable covariance structure identification for doubly multivariate data (Katarzyna Filipiak, Daniel Klein, and Monika Mokrzycka).- Estimation and testing of the covariance structure of doubly multivariate data (Katarzyna Filipiak and Daniel Klein).- Testing equality of mean vectors with block-circular and block compound-symmetric covariance matrices (Carlos A. Coelho).- Estimation and testing hypotheses in two-level and three-level multivariate data with block compound symmetric covariance structure (Arkadiusz Koziol, Anuradha Roy, Roman Zmyślony, Ivan Zezula, and Miguel Fonseca).- Testing of multivariate repeated measures data with block exchangeable covariance structure (Ivan Zezula, Daniel Klein, and Anuradha Roy).- On a simplified approach to estimation in experiments with orthogonal block structure (Radoslaw Kala).- A review of the linear sufficiency and linear prediction sufficiency in the linear model with new observations (Stephen J. Haslett, Jarkko Isotalo, Radoslaw Kala, Augustyn Markiewicz, and Simo Puntanen).- Linear mixed-effects model using penalized spline based on data transformation methods (Syed Ejaz Ahmed, Dursun Aydın and Ersin Yılmaz).- MMLM meetings - List of Publications.- Index.
Автор: Adachi Kohei Название: Matrix-Based Introduction to Multivariate Data Analysis ISBN: 9811095957 ISBN-13(EAN): 9789811095955 Издательство: Springer Рейтинг: Цена: 11179.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Part 1. Elementary Statistics with Matrices.- 1 Introduction to Matrix Operations.- 2 Intra-variable Statistics.- 3 Inter-variable Statistics.- Part 2. Least Squares Procedures.- 4 Regression Analysis.- 5 Principal Component Analysis (Part 1).- 6 Principal Component Analysis 2 (Part 2).- 7 Cluster Analysis.- Part 3. Maximum Likelihood Procedures.- 8 Maximum Likelihood and Normal Distributions.- 9 Path Analysis.- 10 Confirmatory Factor Analysis.- 11 Structural Equation Modeling.- 12 Exploratory Factor Analysis.- Part 4. Miscellaneous Procedures.- 13 Rotation Techniques.- 14 Canonical Correlation and Multiple Correspondence Analyses.- 15 Discriminant Analysis.- 16 Multidimensional Scaling.- Appendices.- A1 Geometric Understanding of Matrices and Vectors.- A2 Decomposition of Sums of Squares.- A3 Singular Value Decomposition (SVD).- A4 Matrix Computation Using SVD.- A5 Supplements for Probability Densities and Likelihoods.- A6 Iterative Algorithms.- References.- Index.
Автор: Adachi Kohei Название: Matrix-Based Introduction to Multivariate Data Analysis ISBN: 9811541051 ISBN-13(EAN): 9789811541056 Издательство: Springer Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
This is the first textbook that allows readers who may be unfamiliar with matrices to understand a variety of multivariate analysis procedures in matrix forms. By explaining which models underlie particular procedures and what objective function is optimized to fit the model to the data, it enables readers to rapidly comprehend multivariate data analysis. Arranged so that readers can intuitively grasp the purposes for which multivariate analysis procedures are used, the book also offers clear explanations of those purposes, with numerical examples preceding the mathematical descriptions.
Supporting the modern matrix formulations by highlighting singular value decomposition among theorems in matrix algebra, this book is useful for undergraduate students who have already learned introductory statistics, as well as for graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis.
The book begins by explaining fundamental matrix operations and the matrix expressions of elementary statistics. Then, it offers an introduction to popular multivariate procedures, with each chapter featuring increasing advanced levels of matrix algebra.
Further the book includes in six chapters on advanced procedures, covering advanced matrix operations and recently proposed multivariate procedures, such as sparse estimation, together with a clear explication of the differences between principal components and factor analyses solutions. In a nutshell, this book allows readers to gain an understanding of the latest developments in multivariate data science.
Автор: Adachi Kohei Название: Matrix-Based Introduction to Multivariate Data Analysis ISBN: 9811541027 ISBN-13(EAN): 9789811541025 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Elementary matrix operations.- Intravariable statistics.- Inter-variable statistics.- Regression analysis.- Principal component analysis.- Principal component.
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