Information And Complexity In Statistical Modeling

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Information and Complexity in Statistical Modeling

Author : Jorma Rissanen
Publisher : Springer Science & Business Media
ISBN 13 : 0387688129
Total Pages : 145 pages
Book Rating : 4.3/5 (876 download)

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Book Synopsis Information and Complexity in Statistical Modeling by : Jorma Rissanen

Download or read book Information and Complexity in Statistical Modeling written by Jorma Rissanen and published by Springer Science & Business Media. This book was released on 2007-12-15 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: No statistical model is "true" or "false," "right" or "wrong"; the models just have varying performance, which can be assessed. The main theme in this book is to teach modeling based on the principle that the objective is to extract the information from data that can be learned with suggested classes of probability models. The intuitive and fundamental concepts of complexity, learnable information, and noise are formalized, which provides a firm information theoretic foundation for statistical modeling. Although the prerequisites include only basic probability calculus and statistics, a moderate level of mathematical proficiency would be beneficial.

Stochastic Complexity In Statistical Inquiry

Author : Jorma Rissanen
Publisher : World Scientific
ISBN 13 : 9814507407
Total Pages : 191 pages
Book Rating : 4.8/5 (145 download)

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Book Synopsis Stochastic Complexity In Statistical Inquiry by : Jorma Rissanen

Download or read book Stochastic Complexity In Statistical Inquiry written by Jorma Rissanen and published by World Scientific. This book was released on 1998-10-07 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes how model selection and statistical inference can be founded on the shortest code length for the observed data, called the stochastic complexity. This generalization of the algorithmic complexity not only offers an objective view of statistics, where no prejudiced assumptions of 'true' data generating distributions are needed, but it also in one stroke leads to calculable expressions in a range of situations of practical interest and links very closely with mainstream statistical theory. The search for the smallest stochastic complexity extends the classical maximum likelihood technique to a new global one, in which models can be compared regardless of their numbers of parameters. The result is a natural and far reaching extension of the traditional theory of estimation, where the Fisher information is replaced by the stochastic complexity and the Cramer-Rao inequality by an extension of the Shannon-Kullback inequality. Ideas are illustrated with applications from parametric and non-parametric regression, density and spectrum estimation, time series, hypothesis testing, contingency tables, and data compression.

Statistical Modeling and Analysis for Complex Data Problems

Author : Pierre Duchesne
Publisher : Springer Science & Business Media
ISBN 13 : 9780387245546
Total Pages : 354 pages
Book Rating : 4.2/5 (455 download)

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Book Synopsis Statistical Modeling and Analysis for Complex Data Problems by : Pierre Duchesne

Download or read book Statistical Modeling and Analysis for Complex Data Problems written by Pierre Duchesne and published by Springer Science & Business Media. This book was released on 2005-04-12 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: STATISTICAL MODELING AND ANALYSIS FOR COMPLEX DATA PROBLEMS treats some of today’s more complex problems and it reflects some of the important research directions in the field. Twenty-nine authors—largely from Montreal’s GERAD Multi-University Research Center and who work in areas of theoretical statistics, applied statistics, probability theory, and stochastic processes—present survey chapters on various theoretical and applied problems of importance and interest to researchers and students across a number of academic domains. Some of the areas and topics examined in the volume are: an analysis of complex survey data, the 2000 American presidential election in Florida, data mining, estimation of uncertainty for machine learning algorithms, interacting stochastic processes, dependent data & copulas, Bayesian analysis of hazard rates, re-sampling methods in a periodic replacement problem, statistical testing in genetics and for dependent data, statistical analysis of time series analysis, theoretical and applied stochastic processes, and an efficient non linear filtering algorithm for the position detection of multiple targets. The book examines the methods and problems from a modeling perspective and surveys the state of current research on each topic and provides direction for further research exploration of the area.

Statistical Foundations of Data Science

Author : Jianqing Fan
Publisher : CRC Press
ISBN 13 : 1466510854
Total Pages : 752 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Statistical Foundations of Data Science by : Jianqing Fan

Download or read book Statistical Foundations of Data Science written by Jianqing Fan and published by CRC Press. This book was released on 2020-09-21 with total page 752 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.

Complex Data Modeling and Computationally Intensive Statistical Methods

Author : Pietro Mantovan
Publisher : Springer Science & Business Media
ISBN 13 : 8847013860
Total Pages : 164 pages
Book Rating : 4.8/5 (47 download)

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Book Synopsis Complex Data Modeling and Computationally Intensive Statistical Methods by : Pietro Mantovan

Download or read book Complex Data Modeling and Computationally Intensive Statistical Methods written by Pietro Mantovan and published by Springer Science & Business Media. This book was released on 2011-01-27 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Selected from the conference "S.Co.2009: Complex Data Modeling and Computationally Intensive Methods for Estimation and Prediction," these 20 papers cover the latest in statistical methods and computational techniques for complex and high dimensional datasets.

Models of Science Dynamics

Author : Andrea Scharnhorst
Publisher : Springer Science & Business Media
ISBN 13 : 3642230687
Total Pages : 270 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Models of Science Dynamics by : Andrea Scharnhorst

Download or read book Models of Science Dynamics written by Andrea Scharnhorst and published by Springer Science & Business Media. This book was released on 2012-01-25 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Models of Science Dynamics aims to capture the structure and evolution of science, the emerging arena in which scholars, science and the communication of science become themselves the basic objects of research. In order to capture the essence of phenomena as diverse as the structure of co-authorship networks or the evolution of citation diffusion patterns, such models can be represented by conceptual models based on historical and ethnographic observations, mathematical descriptions of measurable phenomena, or computational algorithms. Despite its evident importance, the mathematical modeling of science still lacks a unifying framework and a comprehensive study of the topic. This volume fills this gap, reviewing and describing major threads in the mathematical modeling of science dynamics for a wider academic and professional audience. The model classes presented cover stochastic and statistical models, system-dynamics approaches, agent-based simulations, population-dynamics models, and complex-network models. The book comprises an introduction and a foundational chapter that defines and operationalizes terminology used in the study of science, as well as a review chapter that discusses the history of mathematical approaches to modeling science from an algorithmic-historiography perspective. It concludes with a survey of remaining challenges for future science models and their relevance for science and science policy.

Big and Complex Data Analysis

Author : S. Ejaz Ahmed
Publisher : Springer
ISBN 13 : 3319415735
Total Pages : 386 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Big and Complex Data Analysis by : S. Ejaz Ahmed

Download or read book Big and Complex Data Analysis written by S. Ejaz Ahmed and published by Springer. This book was released on 2017-03-21 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume conveys some of the surprises, puzzles and success stories in high-dimensional and complex data analysis and related fields. Its peer-reviewed contributions showcase recent advances in variable selection, estimation and prediction strategies for a host of useful models, as well as essential new developments in the field. The continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and complexity. Examples include epigenomic data, genomic data, proteomic data, high-resolution image data, high-frequency financial data, functional and longitudinal data, and network data. Simultaneous variable selection and estimation is one of the key statistical problems involved in analyzing such big and complex data. The purpose of this book is to stimulate research and foster interaction between researchers in the area of high-dimensional data analysis. More concretely, its goals are to: 1) highlight and expand the breadth of existing methods in big data and high-dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2) identify important directions for future research in the theory of regularization methods, in algorithmic development, and in methodologies for different application areas; and 3) facilitate collaboration between theoretical and subject-specific researchers.

Information Criteria and Statistical Modeling

Author : Sadanori Konishi
Publisher : Springer Science & Business Media
ISBN 13 : 9780387718873
Total Pages : 276 pages
Book Rating : 4.7/5 (188 download)

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Book Synopsis Information Criteria and Statistical Modeling by : Sadanori Konishi

Download or read book Information Criteria and Statistical Modeling written by Sadanori Konishi and published by Springer Science & Business Media. This book was released on 2007-09-12 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical modeling is a critical tool in scientific research. This book provides comprehensive explanations of the concepts and philosophy of statistical modeling, together with a wide range of practical and numerical examples. The authors expect this work to be of great value not just to statisticians but also to researchers and practitioners in various fields of research such as information science, computer science, engineering, bioinformatics, economics, marketing and environmental science. It’s a crucial area of study, as statistical models are used to understand phenomena with uncertainty and to determine the structure of complex systems. They’re also used to control such systems, as well as to make reliable predictions in various natural and social science fields.

Statistical Complexity

Author : K.D. Sen
Publisher : Springer Science & Business Media
ISBN 13 : 9048138906
Total Pages : 304 pages
Book Rating : 4.0/5 (481 download)

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Book Synopsis Statistical Complexity by : K.D. Sen

Download or read book Statistical Complexity written by K.D. Sen and published by Springer Science & Business Media. This book was released on 2011-08-27 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: The understanding of electron density as the carrier of all the information of a multielectronic system is implicit in the theorems of density functional theory. Information theoretical based measures giving a quantitative understanding of statistical complexity of such systems is shaping up as a new area of research in chemical physics. This book is the first monograph of its kind covering the aspects of complexity measure in atoms and molecules.

Advances in Complex Data Modeling and Computational Methods in Statistics

Author : Anna Maria Paganoni
Publisher : Springer
ISBN 13 : 3319111493
Total Pages : 209 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Advances in Complex Data Modeling and Computational Methods in Statistics by : Anna Maria Paganoni

Download or read book Advances in Complex Data Modeling and Computational Methods in Statistics written by Anna Maria Paganoni and published by Springer. This book was released on 2014-11-04 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed.

Handbook of Statistical Analysis and Data Mining Applications

Author : Robert Nisbet
Publisher : Elsevier
ISBN 13 : 0124166458
Total Pages : 822 pages
Book Rating : 4.1/5 (241 download)

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Book Synopsis Handbook of Statistical Analysis and Data Mining Applications by : Robert Nisbet

Download or read book Handbook of Statistical Analysis and Data Mining Applications written by Robert Nisbet and published by Elsevier. This book was released on 2017-11-09 with total page 822 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. Includes input by practitioners for practitioners Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models Contains practical advice from successful real-world implementations Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

Introduction to Statistical Modelling

Author : Annette J. Dobson
Publisher : Springer
ISBN 13 : 1489931740
Total Pages : 133 pages
Book Rating : 4.4/5 (899 download)

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Book Synopsis Introduction to Statistical Modelling by : Annette J. Dobson

Download or read book Introduction to Statistical Modelling written by Annette J. Dobson and published by Springer. This book was released on 2013-11-11 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about generalized linear models as described by NeIder and Wedderburn (1972). This approach provides a unified theoretical and computational framework for the most commonly used statistical methods: regression, analysis of variance and covariance, logistic regression, log-linear models for contingency tables and several more specialized techniques. More advanced expositions of the subject are given by McCullagh and NeIder (1983) and Andersen (1980). The emphasis is on the use of statistical models to investigate substantive questions rather than to produce mathematical descriptions of the data. Therefore parameter estimation and hypothesis testing are stressed. I have assumed that the reader is familiar with the most commonly used statistical concepts and methods and has some basic knowledge of calculus and matrix algebra. Short numerical examples are used to illustrate the main points. In writing this book I have been helped greatly by the comments and criticism of my students and colleagues, especially Anne Young. However, the choice of material, and the obscurities and errors are my responsibility and I apologize to the reader for any irritation caused by them. For typing the manuscript under difficult conditions I am grateful to Anne McKim, Jan Garnsey, Cath Claydon and Julie Latimer.

Frontiers in Massive Data Analysis

Author : National Research Council
Publisher : National Academies Press
ISBN 13 : 0309287812
Total Pages : 190 pages
Book Rating : 4.3/5 (92 download)

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Book Synopsis Frontiers in Massive Data Analysis by : National Research Council

Download or read book Frontiers in Massive Data Analysis written by National Research Council and published by National Academies Press. This book was released on 2013-09-03 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale--terabytes and petabytes--is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge--from computer science, statistics, machine learning, and application disciplines--that must be brought to bear to make useful inferences from massive data.

Statistical Modeling for Naturalists

Author : Pedro F. Quintana Ascencio
Publisher : Cambridge Scholars Publishing
ISBN 13 : 1527579530
Total Pages : 210 pages
Book Rating : 4.5/5 (275 download)

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Book Synopsis Statistical Modeling for Naturalists by : Pedro F. Quintana Ascencio

Download or read book Statistical Modeling for Naturalists written by Pedro F. Quintana Ascencio and published by Cambridge Scholars Publishing. This book was released on 2022-01-31 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book will allow naturalists, nature stewards, and graduate students to appreciate and comprehend basic statistical concepts as a bridge to more complex themes relevant to their daily work. Although there are excellent sources on more specialized analytical topics relevant to naturalists, this introductory book makes a connection with the experience and needs of field practitioners. It uses aspects of the natural history of the Florida scrub relevant for conservation and management as examples of analytical issues pertinent to the naturalist in a broader context. Each chapter identifies important ecological questions and then provides approaches to evaluate data, focusing on the analytical decision-making process. The book guides the reader on frequently overlooked aspects such as the understanding of model assumptions, alternative model specifications, model output interpretation, and model limitations.

Complex Data Modeling and Computationally Intensive Statistical Methods

Author :
Publisher :
ISBN 13 : 9788847013926
Total Pages : 176 pages
Book Rating : 4.0/5 (139 download)

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Book Synopsis Complex Data Modeling and Computationally Intensive Statistical Methods by :

Download or read book Complex Data Modeling and Computationally Intensive Statistical Methods written by and published by . This book was released on 2011-08-14 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Knowing What Students Know

Author : National Research Council
Publisher : National Academies Press
ISBN 13 : 0309293227
Total Pages : 383 pages
Book Rating : 4.3/5 (92 download)

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Book Synopsis Knowing What Students Know by : National Research Council

Download or read book Knowing What Students Know written by National Research Council and published by National Academies Press. This book was released on 2001-10-27 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: Education is a hot topic. From the stage of presidential debates to tonight's dinner table, it is an issue that most Americans are deeply concerned about. While there are many strategies for improving the educational process, we need a way to find out what works and what doesn't work as well. Educational assessment seeks to determine just how well students are learning and is an integral part of our quest for improved education. The nation is pinning greater expectations on educational assessment than ever before. We look to these assessment tools when documenting whether students and institutions are truly meeting education goals. But we must stop and ask a crucial question: What kind of assessment is most effective? At a time when traditional testing is subject to increasing criticism, research suggests that new, exciting approaches to assessment may be on the horizon. Advances in the sciences of how people learn and how to measure such learning offer the hope of developing new kinds of assessments-assessments that help students succeed in school by making as clear as possible the nature of their accomplishments and the progress of their learning. Knowing What Students Know essentially explains how expanding knowledge in the scientific fields of human learning and educational measurement can form the foundations of an improved approach to assessment. These advances suggest ways that the targets of assessment-what students know and how well they know it-as well as the methods used to make inferences about student learning can be made more valid and instructionally useful. Principles for designing and using these new kinds of assessments are presented, and examples are used to illustrate the principles. Implications for policy, practice, and research are also explored. With the promise of a productive research-based approach to assessment of student learning, Knowing What Students Know will be important to education administrators, assessment designers, teachers and teacher educators, and education advocates.

Information Criteria and Statistical Modeling

Author : Sadanori Konishi
Publisher : Springer Science & Business Media
ISBN 13 : 0387718869
Total Pages : 282 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Information Criteria and Statistical Modeling by : Sadanori Konishi

Download or read book Information Criteria and Statistical Modeling written by Sadanori Konishi and published by Springer Science & Business Media. This book was released on 2008 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical modeling is a critical tool in scientific research. This book provides comprehensive explanations of the concepts and philosophy of statistical modeling, together with a wide range of practical and numerical examples. The authors expect this work to be of great value not just to statisticians but also to researchers and practitioners in various fields of research such as information science, computer science, engineering, bioinformatics, economics, marketing and environmental science. It’s a crucial area of study, as statistical models are used to understand phenomena with uncertainty and to determine the structure of complex systems. They’re also used to control such systems, as well as to make reliable predictions in various natural and social science fields.