Innovations In Multivariate Statistical Modeling

Author : Andriëtte Bekker,Johannes T. Ferreira,Mohammad Arashi,Ding-Geng Chen
Publisher : Springer Nature
Page : 434 pages
File Size : 48,5 Mb
Release : 2022-12-15
Category : Mathematics
ISBN : 9783031139710

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Innovations in Multivariate Statistical Modeling by Andriëtte Bekker,Johannes T. Ferreira,Mohammad Arashi,Ding-Geng Chen Pdf

Multivariate statistical analysis has undergone a rich and varied evolution during the latter half of the 20th century. Academics and practitioners have produced much literature with diverse interests and with varying multidisciplinary knowledge on different topics within the multivariate domain. Due to multivariate algebra being of sustained interest and being a continuously developing field, its appeal breaches laterally across multiple disciplines to act as a catalyst for contemporary advances, with its core inferential genesis remaining in that of statistics. It is exactly this varied evolution caused by an influx in data production, diffusion, and understanding in scientific fields that has blurred many lines between disciplines. The cross-pollination between statistics and biology, engineering, medical science, computer science, and even art, has accelerated the vast amount of questions that statistical methodology has to answer and report on. These questions are often multivariate in nature, hoping to elucidate uncertainty on more than one aspect at the same time, and it is here where statistical thinking merges mathematical design with real life interpretation for understanding this uncertainty. Statistical advances benefit from these algebraic inventions and expansions in the multivariate paradigm. This contributed volume aims to usher novel research emanating from a multivariate statistical foundation into the spotlight, with particular significance in multidisciplinary settings. The overarching spirit of this volume is to highlight current trends, stimulate a focus on, and connect multidisciplinary dots from and within multivariate statistical analysis. Guided by these thoughts, a collection of research at the forefront of multivariate statistical thinking is presented here which has been authored by globally recognized subject matter experts.

Author : H. Bozdogan,Arjun K. Gupta
Publisher : Springer Science & Business Media
Page : 193 pages
File Size : 45,9 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9789400939776

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Multivariate Statistical Modeling and Data Analysis by H. Bozdogan,Arjun K. Gupta Pdf

This volume contains the Proceedings of the Advanced Symposium on Multivariate Modeling and Data Analysis held at the 64th Annual Heeting of the Virginia Academy of Sciences (VAS)--American Statistical Association's Vir ginia Chapter at James Madison University in Harrisonburg. Virginia during Hay 15-16. 1986. This symposium was sponsored by financial support from the Center for Advanced Studies at the University of Virginia to promote new and modern information-theoretic statist ical modeling procedures and to blend these new techniques within the classical theory. Multivariate statistical analysis has come a long way and currently it is in an evolutionary stage in the era of high-speed computation and computer technology. The Advanced Symposium was the first to address the new innovative approaches in multi variate analysis to develop modern analytical and yet practical procedures to meet the needs of researchers and the societal need of statistics. vii viii PREFACE Papers presented at the Symposium by e1l11lJinent researchers in the field were geared not Just for specialists in statistics, but an attempt has been made to achieve a well balanced and uniform coverage of different areas in multi variate modeling and data analysis. The areas covered included topics in the analysis of repeated measurements, cluster analysis, discriminant analysis, canonical cor relations, distribution theory and testing, bivariate densi ty estimation, factor analysis, principle component analysis, multidimensional scaling, multivariate linear models, nonparametric regression, etc.

Author : Risto D.H. Heijmans,D.S.G. Pollock,Albert Satorra
Publisher : Springer Science & Business Media
Page : 302 pages
File Size : 51,9 Mb
Release : 2012-12-06
Category : Business & Economics
ISBN : 9781461546030

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Innovations in Multivariate Statistical Analysis by Risto D.H. Heijmans,D.S.G. Pollock,Albert Satorra Pdf

The three decades which have followed the publication of Heinz Neudecker's seminal paper `Some Theorems on Matrix Differentiation with Special Reference to Kronecker Products' in the Journal of the American Statistical Association (1969) have witnessed the growing influence of matrix analysis in many scientific disciplines. Amongst these are the disciplines to which Neudecker has contributed directly - namely econometrics, economics, psychometrics and multivariate analysis. This book aims to illustrate how powerful the tools of matrix analysis have become as weapons in the statistician's armoury. The majority of its chapters are concerned primarily with theoretical innovations, but all of them have applications in view, and some of them contain extensive illustrations of the applied techniques. This book will provide research workers and graduate students with a cross-section of innovative work in the fields of matrix methods and multivariate statistical analysis. It should be of interest to students and practitioners in a wide range of subjects which rely upon modern methods of statistical analysis. The contributors to the book are themselves practitioners of a wide range of subjects including econometrics, psychometrics, educational statistics, computation methods and electrical engineering, but they find a common ground in the methods which are represented in the book. It is envisaged that the book will serve as an important work of reference and as a source of inspiration for some years to come.

Author : Ronald Christensen,Ronald A. Christensen
Publisher : Entropy, Limited
Page : 814 pages
File Size : 43,9 Mb
Release : 1983
Category : Mathematical statistics
ISBN : UCAL:B3844631

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Multivariate Statistical Modeling by Ronald Christensen,Ronald A. Christensen Pdf

Author : L. Fahrmeir
Publisher : Copernicus
Page : 425 pages
File Size : 50,7 Mb
Release : 1994
Category : Mathematics
ISBN : 0387942335

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Multivariate Statistical Modelling Based on Generalized Linear Models by L. Fahrmeir Pdf

Author : H. Bozdogan
Publisher : Springer Science & Business Media
Page : 421 pages
File Size : 49,8 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9789401108003

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Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach by H. Bozdogan Pdf

Often a statistical analysis involves use of a set of alternative models for the data. A "model-selection criterion" is a formula which provides a figure-of merit for the alternative models. Generally the alternative models will involve different numhers of parameters. Model-selection criteria take into account hoth the goodness-or-fit of a model and the numher of parameters used to achieve that fit. 1.1. SETS OF ALTERNATIVE MODELS Thus the focus in this paper is on data-analytic situations ill which there is consideration of a set of alternative models. Choice of a suhset of explanatory variahles in regression, the degree of a polynomial regression, the number of factors in factor analysis, or the numher of dusters in duster analysis are examples of such situations. 1.2. MODEL SELECTION VERSUS HYPOTHESIS TESTING In exploratory data analysis or in a preliminary phase of inference an approach hased on model-selection criteria can offer advantages over tests of hypotheses. The model-selection approach avoids the prohlem of specifying error rates for the tests. With model selection the focus can he on simultaneous competition between a hroad dass of competing models rather than on consideration of a sequence of simpler and simpler models.

Author : Ronald A. Christensen
Publisher : Unknown
Page : 726 pages
File Size : 50,5 Mb
Release : 1983
Category : Multivariate analysis
ISBN : 0938986147

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Multivariate Statistical Modeling by Ronald A. Christensen Pdf

Author : H. Bozdogan
Publisher : Springer Science & Business Media
Page : 356 pages
File Size : 53,9 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9789401108546

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Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach by H. Bozdogan Pdf

These three volumes comprise the proceedings of the US/Japan Conference, held in honour of Professor H. Akaike, on the `Frontiers of Statistical Modeling: an Informational Approach'. The major theme of the conference was the implementation of statistical modeling through an informational approach to complex, real-world problems. Volume 1 contains papers which deal with the Theory and Methodology of Time Series Analysis. Volume 1 also contains the text of the Banquet talk by E. Parzen and the keynote lecture of H. Akaike. Volume 2 is devoted to the general topic of Multivariate Statistical Modeling, and Volume 3 contains the papers relating to Engineering and Scientific Applications. For all scientists whose work involves statistics.

Author : Gilbert MacKenzie,Defen Peng
Publisher : Springer Science & Business Media
Page : 244 pages
File Size : 50,6 Mb
Release : 2014-05-08
Category : Mathematics
ISBN : 9783319045795

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Statistical Modelling in Biostatistics and Bioinformatics by Gilbert MacKenzie,Defen Peng Pdf

This book presents selected papers on statistical model development related mainly to the fields of Biostatistics and Bioinformatics. The coverage of the material falls squarely into the following categories: (a) Survival analysis and multivariate survival analysis, (b) Time series and longitudinal data analysis, (c) Statistical model development and (d) Applied statistical modelling. Innovations in statistical modelling are presented throughout each of the four areas, with some intriguing new ideas on hierarchical generalized non-linear models and on frailty models with structural dispersion, just to mention two examples. The contributors include distinguished international statisticians such as Philip Hougaard, John Hinde, Il Do Ha, Roger Payne and Alessandra Durio, among others, as well as promising newcomers. Some of the contributions have come from researchers working in the BIO-SI research programme on Biostatistics and Bioinformatics, centred on the Universities of Limerick and Galway in Ireland and funded by the Science Foundation Ireland under its Mathematics Initiative.

Author : Wenqing He,Liqun Wang,Jiahua Chen,Chunfang Devon Lin
Publisher : Springer Nature
Page : 339 pages
File Size : 44,9 Mb
Release : 2022-10-27
Category : Science
ISBN : 9783031083297

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Advances and Innovations in Statistics and Data Science by Wenqing He,Liqun Wang,Jiahua Chen,Chunfang Devon Lin Pdf

This book highlights selected papers from the 4th ICSA-Canada Chapter Symposium, as well as invited articles from established researchers in the areas of statistics and data science. It covers a variety of topics, including methodology development in data science, such as methodology in the analysis of high dimensional data, feature screening in ultra-high dimensional data and natural language ranking; statistical analysis challenges in sampling, multivariate survival models and contaminated data, as well as applications of statistical methods. With this book, readers can make use of frontier research methods to tackle their problems in research, education, training and consultation.

Author : L. Fahrmeir,Gerhard Tutz
Publisher : Unknown
Page : 425 pages
File Size : 44,6 Mb
Release : 1994
Category : Linear models (Statistics)
ISBN : 7506238241

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Multivariate Statistical Modelling Basedon Generalized Linear Models by L. Fahrmeir,Gerhard Tutz Pdf

Author : Simo Puntanen,George P. H. Styan,Jarkko Isotalo
Publisher : Springer Science & Business Media
Page : 486 pages
File Size : 52,9 Mb
Release : 2011-08-24
Category : Mathematics
ISBN : 9783642104732

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Matrix Tricks for Linear Statistical Models by Simo Puntanen,George P. H. Styan,Jarkko Isotalo Pdf

In teaching linear statistical models to first-year graduate students or to final-year undergraduate students there is no way to proceed smoothly without matrices and related concepts of linear algebra; their use is really essential. Our experience is that making some particular matrix tricks very familiar to students can substantially increase their insight into linear statistical models (and also multivariate statistical analysis). In matrix algebra, there are handy, sometimes even very simple “tricks” which simplify and clarify the treatment of a problem—both for the student and for the professor. Of course, the concept of a trick is not uniquely defined—by a trick we simply mean here a useful important handy result. In this book we collect together our Top Twenty favourite matrix tricks for linear statistical models.

Author : Michel Jambu
Publisher : Unknown
Page : 474 pages
File Size : 55,9 Mb
Release : 1989
Category : Electronic
ISBN : OCLC:1097726154

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Exploratory and multivariate data analysis by Michel Jambu Pdf

Author : Marc S. Paolella
Publisher : John Wiley & Sons
Page : 896 pages
File Size : 45,8 Mb
Release : 2018-10-10
Category : Mathematics
ISBN : 9781119431855

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Linear Models and Time-Series Analysis by Marc S. Paolella Pdf

A comprehensive and timely edition on an emerging new trend in time series Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH sets a strong foundation, in terms of distribution theory, for the linear model (regression and ANOVA), univariate time series analysis (ARMAX and GARCH), and some multivariate models associated primarily with modeling financial asset returns (copula-based structures and the discrete mixed normal and Laplace). It builds on the author's previous book, Fundamental Statistical Inference: A Computational Approach, which introduced the major concepts of statistical inference. Attention is explicitly paid to application and numeric computation, with examples of Matlab code throughout. The code offers a framework for discussion and illustration of numerics, and shows the mapping from theory to computation. The topic of time series analysis is on firm footing, with numerous textbooks and research journals dedicated to it. With respect to the subject/technology, many chapters in Linear Models and Time-Series Analysis cover firmly entrenched topics (regression and ARMA). Several others are dedicated to very modern methods, as used in empirical finance, asset pricing, risk management, and portfolio optimization, in order to address the severe change in performance of many pension funds, and changes in how fund managers work. Covers traditional time series analysis with new guidelines Provides access to cutting edge topics that are at the forefront of financial econometrics and industry Includes latest developments and topics such as financial returns data, notably also in a multivariate context Written by a leading expert in time series analysis Extensively classroom tested Includes a tutorial on SAS Supplemented with a companion website containing numerous Matlab programs Solutions to most exercises are provided in the book Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH is suitable for advanced masters students in statistics and quantitative finance, as well as doctoral students in economics and finance. It is also useful for quantitative financial practitioners in large financial institutions and smaller finance outlets.

Author : Mark E. Johnson
Publisher : John Wiley & Sons
Page : 248 pages
File Size : 53,6 Mb
Release : 2013-11-11
Category : Mathematics
ISBN : 9781118150733

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Multivariate Statistical Simulation by Mark E. Johnson Pdf

Provides state-of-the-art coverage for the researcher confronted with designing and executing a simulation study using continuous multivariate distributions. Concise writing style makes the book accessible to a wide audience. Well-known multivariate distributions are described, emphasizing a few representative cases from each distribution. Coverage includes Pearson Types II and VII elliptically contoured distributions, Khintchine distributions, and the unifying class for the Burr, Pareto, and logistic distributions. Extensively illustrated--the figures are unique, attractive, and reveal very nicely what distributions ``look like.'' Contains an extensive and up-to-date bibliography culled from journals in statistics, operations research, mathematics, and computer science.