Partially Linear Models

Partially Linear Models

Author: Wolfgang Härdle

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 210

ISBN-13: 3642577008

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In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing in statistics. Many methods and techniques have been proposed and studied. This monograph hopes to bring an up-to-date presentation of the state of the art of partially linear regression techniques. The emphasis is on methodologies rather than on the theory, with a particular focus on applications of partially linear regression techniques to various statistical problems. These problems include least squares regression, asymptotically efficient estimation, bootstrap resampling, censored data analysis, linear measurement error models, nonlinear measurement models, nonlinear and nonparametric time series models.


Handbook of Graphical Models

Handbook of Graphical Models

Author: Marloes Maathuis

Publisher: CRC Press

Published: 2018-11-12

Total Pages: 612

ISBN-13: 0429874235

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A graphical model is a statistical model that is represented by a graph. The factorization properties underlying graphical models facilitate tractable computation with multivariate distributions, making the models a valuable tool with a plethora of applications. Furthermore, directed graphical models allow intuitive causal interpretations and have become a cornerstone for causal inference. While there exist a number of excellent books on graphical models, the field has grown so much that individual authors can hardly cover its entire scope. Moreover, the field is interdisciplinary by nature. Through chapters by leading researchers from different areas, this handbook provides a broad and accessible overview of the state of the art. Key features: * Contributions by leading researchers from a range of disciplines * Structured in five parts, covering foundations, computational aspects, statistical inference, causal inference, and applications * Balanced coverage of concepts, theory, methods, examples, and applications * Chapters can be read mostly independently, while cross-references highlight connections The handbook is targeted at a wide audience, including graduate students, applied researchers, and experts in graphical models.


Applied Nonparametric Econometrics

Applied Nonparametric Econometrics

Author: Daniel J. Henderson

Publisher: Cambridge University Press

Published: 2015-01-19

Total Pages: 381

ISBN-13: 110701025X

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The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignores the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians. It discusses in depth, and in terms that someone with only one year of graduate econometrics can understand, basic to advanced nonparametric methods. The analysis starts with density estimation and motivates the procedures through methods that should be familiar to the reader. It then moves on to kernel regression, estimation with discrete data, and advanced methods such as estimation with panel data and instrumental variables models. The book pays close attention to the issues that arise with programming, computing speed, and application. In each chapter, the methods discussed are applied to actual data, paying attention to presentation of results and potential pitfalls.


High-Dimensional Covariance Matrix Estimation

High-Dimensional Covariance Matrix Estimation

Author: Aygul Zagidullina

Publisher: Springer Nature

Published: 2021-10-29

Total Pages: 123

ISBN-13: 3030800652

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This book presents covariance matrix estimation and related aspects of random matrix theory. It focuses on the sample covariance matrix estimator and provides a holistic description of its properties under two asymptotic regimes: the traditional one, and the high-dimensional regime that better fits the big data context. It draws attention to the deficiencies of standard statistical tools when used in the high-dimensional setting, and introduces the basic concepts and major results related to spectral statistics and random matrix theory under high-dimensional asymptotics in an understandable and reader-friendly way. The aim of this book is to inspire applied statisticians, econometricians, and machine learning practitioners who analyze high-dimensional data to apply the recent developments in their work.


Fundamentals of High-Dimensional Statistics

Fundamentals of High-Dimensional Statistics

Author: Johannes Lederer

Publisher: Springer Nature

Published: 2021-11-16

Total Pages: 355

ISBN-13: 3030737926

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This textbook provides a step-by-step introduction to the tools and principles of high-dimensional statistics. Each chapter is complemented by numerous exercises, many of them with detailed solutions, and computer labs in R that convey valuable practical insights. The book covers the theory and practice of high-dimensional linear regression, graphical models, and inference, ensuring readers have a smooth start in the field. It also offers suggestions for further reading. Given its scope, the textbook is intended for beginning graduate and advanced undergraduate students in statistics, biostatistics, and bioinformatics, though it will be equally useful to a broader audience.


Statistics for High-Dimensional Data

Statistics for High-Dimensional Data

Author: Peter Bühlmann

Publisher: Springer Science & Business Media

Published: 2011-06-08

Total Pages: 568

ISBN-13: 364220192X

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Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections. A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.


High-Dimensional Statistics

High-Dimensional Statistics

Author: Martin J. Wainwright

Publisher: Cambridge University Press

Published: 2019-02-21

Total Pages: 571

ISBN-13: 1108498027

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A coherent introductory text from a groundbreaking researcher, focusing on clarity and motivation to build intuition and understanding.


Large-dimensional Panel Data Econometrics: Testing, Estimation And Structural Changes

Large-dimensional Panel Data Econometrics: Testing, Estimation And Structural Changes

Author: Feng Qu

Publisher: World Scientific

Published: 2020-08-24

Total Pages: 167

ISBN-13: 9811220794

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This book aims to fill the gap between panel data econometrics textbooks, and the latest development on 'big data', especially large-dimensional panel data econometrics. It introduces important research questions in large panels, including testing for cross-sectional dependence, estimation of factor-augmented panel data models, structural breaks in panels and group patterns in panels. To tackle these high dimensional issues, some techniques used in Machine Learning approaches are also illustrated. Moreover, the Monte Carlo experiments, and empirical examples are also utilised to show how to implement these new inference methods. Large-Dimensional Panel Data Econometrics: Testing, Estimation and Structural Changes also introduces new research questions and results in recent literature in this field.


High-Dimensional Probability

High-Dimensional Probability

Author: Roman Vershynin

Publisher: Cambridge University Press

Published: 2018-09-27

Total Pages: 299

ISBN-13: 1108415199

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An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.


Large Dimensional Factor Analysis

Large Dimensional Factor Analysis

Author: Jushan Bai

Publisher: Now Publishers Inc

Published: 2008

Total Pages: 90

ISBN-13: 1601981449

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Large Dimensional Factor Analysis provides a survey of the main theoretical results for large dimensional factor models, emphasizing results that have implications for empirical work. The authors focus on the development of the static factor models and on the use of estimated factors in subsequent estimation and inference. Large Dimensional Factor Analysis discusses how to determine the number of factors, how to conduct inference when estimated factors are used in regressions, how to assess the adequacy pf observed variables as proxies for latent factors, how to exploit the estimated factors to test unit root tests and common trends, and how to estimate panel cointegration models.