Linear Models And Time Series Analysis

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Linear Models and Time-Series Analysis

Marc S. Paolella
Linear Models and Time Series Analysis Book PDF

Author : Marc S. Paolella
Publisher : John Wiley & Sons
Page : 896 pages
File Size : 46,5 Mb
Release : 2018-12-17
Category : Mathematics
ISBN : 9781119431909

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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.

Linear Models in Statistics

Alvin C. Rencher,G. Bruce Schaalje
Linear Models in Statistics Book PDF

Author : Alvin C. Rencher,G. Bruce Schaalje
Publisher : John Wiley & Sons
Page : 690 pages
File Size : 42,7 Mb
Release : 2008-01-07
Category : Mathematics
ISBN : 9780470192603

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Linear Models in Statistics by Alvin C. Rencher,G. Bruce Schaalje Pdf

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.

Threshold Models in Non-linear Time Series Analysis

H. Tong
Threshold Models in Non linear Time Series Analysis Book PDF

Author : H. Tong
Publisher : Springer Science & Business Media
Page : 333 pages
File Size : 50,9 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781468478884

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Threshold Models in Non-linear Time Series Analysis by H. Tong Pdf

In the last two years or so, I was most fortunate in being given opportunities of lecturing on a new methodology to a variety of audiences in Britain, China, Finland, France and Spain. Despite my almost Confucian attitude of preferring talking (i.e. a transient record) to writing (i.e. a permanent record), the warm encouragement of friends has led to the ensuing notes. I am also only too conscious of the infancy of the methodology introduced in these notes. However, it is my sincere hope that exposure to a wider audience will accelerate its maturity. Readers are assumed to be familiar with the basic theory of time series analysis. The book by Professor M.B. Priestley (1981) may be used as a general reference. Chapter One is addressed to the general question: "why do we need non-linear time series models?" After describing some significant advantages of linear models, it singles out several major limitations of linearity. Of course, the selection reflects my personal view on the subject, which is only at its very beginning, although there does seem to be a general agreement in the literature that time irr'eversibility and limit cycles are among the most obvious.

Advanced Linear Modeling

Ronald Christensen
Advanced Linear Modeling Book PDF

Author : Ronald Christensen
Publisher : Springer Science & Business Media
Page : 412 pages
File Size : 55,5 Mb
Release : 2013-03-14
Category : Mathematics
ISBN : 9781475738476

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Advanced Linear Modeling by Ronald Christensen Pdf

This book introduces several topics related to linear model theory, including: multivariate linear models, discriminant analysis, principal components, factor analysis, time series in both the frequency and time domains, and spatial data analysis. This second edition adds new material on nonparametric regression, response surface maximization, and longitudinal models. The book provides a unified approach to these disparate subjects and serves as a self-contained companion volume to the author's Plane Answers to Complex Questions: The Theory of Linear Models. Ronald Christensen is Professor of Statistics at the University of New Mexico. He is well known for his work on the theory and application of linear models having linear structure.

Geodetic Time Series Analysis in Earth Sciences

Jean-Philippe Montillet,Machiel S. Bos
Geodetic Time Series Analysis in Earth Sciences Book PDF

Author : Jean-Philippe Montillet,Machiel S. Bos
Publisher : Springer
Page : 422 pages
File Size : 49,7 Mb
Release : 2019-08-16
Category : Science
ISBN : 9783030217181

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Geodetic Time Series Analysis in Earth Sciences by Jean-Philippe Montillet,Machiel S. Bos Pdf

This book provides an essential appraisal of the recent advances in technologies, mathematical models and computational software used by those working with geodetic data. It explains the latest methods in processing and analyzing geodetic time series data from various space missions (i.e. GNSS, GRACE) and other technologies (i.e. tide gauges), using the most recent mathematical models. The book provides practical examples of how to apply these models to estimate seal level rise as well as rapid and evolving land motion changes due to gravity (ice sheet loss) and earthquakes respectively. It also provides a necessary overview of geodetic software and where to obtain them.

Linear Models for Multivariate, Time Series, and Spatial Data

Ronald Christensen
Linear Models for Multivariate Time Series and Spatial Data Book PDF

Author : Ronald Christensen
Publisher : Springer Science & Business Media
Page : 329 pages
File Size : 52,7 Mb
Release : 2013-11-11
Category : Mathematics
ISBN : 9781475741032

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Linear Models for Multivariate, Time Series, and Spatial Data by Ronald Christensen Pdf

This is a self-contained companion volume to the authors book "Plane Answers to Complex Questions: The Theory of Linear Models". It provides introductions to several topics related to linear model theory: multivariate linear models, discriminant analysis, principal components, factor analysis, time series in both the frequency and time domains, and spatial data analysis (geostatistics). The purpose of this volume is to use the three fundamental ideas of best linear prediction, projections, and Mahalanobis' distance to exploit their properties in examining multivariate, time series and spatial data. Ronald Christensen is Professor of Statistics at the University of New Mexico, and is recognised internationally as an expert in the theory and application of linear models.

Regression Analysis and Linear Models

Richard B. Darlington,Andrew F. Hayes
Regression Analysis and Linear Models Book PDF

Author : Richard B. Darlington,Andrew F. Hayes
Publisher : Guilford Publications
Page : 689 pages
File Size : 52,5 Mb
Release : 2016-08-22
Category : Social Science
ISBN : 9781462527984

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Regression Analysis and Linear Models by Richard B. Darlington,Andrew F. Hayes Pdf

Emphasizing conceptual understanding over mathematics, this user-friendly text introduces linear regression analysis to students and researchers across the social, behavioral, consumer, and health sciences. Coverage includes model construction and estimation, quantification and measurement of multivariate and partial associations, statistical control, group comparisons, moderation analysis, mediation and path analysis, and regression diagnostics, among other important topics. Engaging worked-through examples demonstrate each technique, accompanied by helpful advice and cautions. The use of SPSS, SAS, and STATA is emphasized, with an appendix on regression analysis using R. The companion website (www.afhayes.com) provides datasets for the book's examples as well as the RLM macro for SPSS and SAS. Pedagogical Features: *Chapters include SPSS, SAS, or STATA code pertinent to the analyses described, with each distinctively formatted for easy identification. *An appendix documents the RLM macro, which facilitates computations for estimating and probing interactions, dominance analysis, heteroscedasticity-consistent standard errors, and linear spline regression, among other analyses. *Students are guided to practice what they learn in each chapter using datasets provided online. *Addresses topics not usually covered, such as ways to measure a variable’s importance, coding systems for representing categorical variables, causation, and myths about testing interaction.

Forecasting: principles and practice

Rob J Hyndman,George Athanasopoulos
Forecasting principles and practice Book PDF

Author : Rob J Hyndman,George Athanasopoulos
Publisher : OTexts
Page : 380 pages
File Size : 45,8 Mb
Release : 2018-05-08
Category : Business & Economics
ISBN : 9780987507112

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Forecasting: principles and practice by Rob J Hyndman,George Athanasopoulos Pdf

Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.

Applied Regression Analysis and Generalized Linear Models

John Fox
Applied Regression Analysis and Generalized Linear Models Book PDF

Author : John Fox
Publisher : SAGE Publications
Page : 612 pages
File Size : 46,6 Mb
Release : 2015-03-18
Category : Social Science
ISBN : 9781483321318

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Applied Regression Analysis and Generalized Linear Models by John Fox Pdf

Combining a modern, data-analytic perspective with a focus on applications in the social sciences, the Third Edition of Applied Regression Analysis and Generalized Linear Models provides in-depth coverage of regression analysis, generalized linear models, and closely related methods, such as bootstrapping and missing data. Updated throughout, this Third Edition includes new chapters on mixed-effects models for hierarchical and longitudinal data. Although the text is largely accessible to readers with a modest background in statistics and mathematics, author John Fox also presents more advanced material in optional sections and chapters throughout the book. Accompanying website resources containing all answers to the end-of-chapter exercises. Answers to odd-numbered questions, as well as datasets and other student resources are available on the author′s website. NEW! Bonus chapter on Bayesian Estimation of Regression Models also available at the author′s website.

Time Series Analysis

Henrik Madsen
Time Series Analysis Book PDF

Author : Henrik Madsen
Publisher : CRC Press
Page : 390 pages
File Size : 54,8 Mb
Release : 2007-11-28
Category : Mathematics
ISBN : 9781420059687

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Time Series Analysis by Henrik Madsen Pdf

With a focus on analyzing and modeling linear dynamic systems using statistical methods, Time Series Analysis formulates various linear models, discusses their theoretical characteristics, and explores the connections among stochastic dynamic models. Emphasizing the time domain description, the author presents theorems to highlight the most

Nonlinear Time Series Analysis

Ruey S. Tsay,Rong Chen
Nonlinear Time Series Analysis Book PDF

Author : Ruey S. Tsay,Rong Chen
Publisher : John Wiley & Sons
Page : 512 pages
File Size : 41,8 Mb
Release : 2018-09-14
Category : Mathematics
ISBN : 9781119264071

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Nonlinear Time Series Analysis by Ruey S. Tsay,Rong Chen Pdf

A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. The authors—noted experts in the field—explore the advantages and limitations of the nonlinear models and methods and review the improvements upon linear time series models. The need for this book is based on the recent developments in nonlinear time series analysis, statistical learning, dynamic systems and advanced computational methods. Parametric and nonparametric methods and nonlinear and non-Gaussian state space models provide a much wider range of tools for time series analysis. In addition, advances in computing and data collection have made available large data sets and high-frequency data. These new data make it not only feasible, but also necessary to take into consideration the nonlinearity embedded in most real-world time series. This vital guide: • Offers research developed by leading scholars of time series analysis • Presents R commands making it possible to reproduce all the analyses included in the text • Contains real-world examples throughout the book • Recommends exercises to test understanding of material presented • Includes an instructor solutions manual and companion website Written for students, researchers, and practitioners who are interested in exploring nonlinearity in time series, Nonlinear Time Series Analysis offers a comprehensive text that explores the advantages and limitations of the nonlinear models and methods and demonstrates the improvements upon linear time series models.

Richly Parameterized Linear Models

James S. Hodges
Richly Parameterized Linear Models Book PDF

Author : James S. Hodges
Publisher : CRC Press
Page : 469 pages
File Size : 42,8 Mb
Release : 2016-04-19
Category : Mathematics
ISBN : 9781439866849

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Richly Parameterized Linear Models by James S. Hodges Pdf

A First Step toward a Unified Theory of Richly Parameterized Linear Models Using mixed linear models to analyze data often leads to results that are mysterious, inconvenient, or wrong. Further compounding the problem, statisticians lack a cohesive resource to acquire a systematic, theory-based understanding of models with random effects. Richly Parameterized Linear Models: Additive, Time Series, and Spatial Models Using Random Effects takes a first step in developing a full theory of richly parameterized models, which would allow statisticians to better understand their analysis results. The author examines what is known and unknown about mixed linear models and identifies research opportunities. The first two parts of the book cover an existing syntax for unifying models with random effects. The text explains how richly parameterized models can be expressed as mixed linear models and analyzed using conventional and Bayesian methods. In the last two parts, the author discusses oddities that can arise when analyzing data using these models. He presents ways to detect problems and, when possible, shows how to mitigate or avoid them. The book adapts ideas from linear model theory and then goes beyond that theory by examining the information in the data about the mixed linear model’s covariance matrices. Each chapter ends with two sets of exercises. Conventional problems encourage readers to practice with the algebraic methods and open questions motivate readers to research further. Supporting materials, including datasets for most of the examples analyzed, are available on the author’s website.

Linear Models

Brenton R. Clarke
Linear Models Book PDF

Author : Brenton R. Clarke
Publisher : John Wiley & Sons
Page : 288 pages
File Size : 51,7 Mb
Release : 2008-09-19
Category : Mathematics
ISBN : 0470377976

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Linear Models by Brenton R. Clarke Pdf

An insightful approach to the analysis of variance in the study of linear models Linear Models explores the theory of linear models and the dynamic relationships that these models have with Analysis of Variance (ANOVA), experimental design, and random and mixed-model effects. This one-of-a-kind book emphasizes an approach that clearly explains the distribution theory of linear models and experimental design starting from basic mathematical concepts in linear algebra. The author begins with a presentation of the classic fixed-effects linear model and goes on to illustrate eight common linear models, along with the value of their use in statistics. From this foundation, subsequent chapters introduce concepts pertaining to the linear model, starting with vector space theory and the theory of least-squares estimation. An outline of the Helmert matrix is also presented, along with a thorough explanation of how the ANOVA is created in both typical two-way and higher layout designs, ultimately revealing the distribution theory. Other important topics covered include: Vector space theory The theory of least squares estimation Gauss-Markov theorem Kronecker products Diagnostic and robust methods for linear models Likelihood approaches to estimation A discussion of Bayesian theory is also included for purposes of comparison and contrast, and numerous illustrative exercises assist the reader with uncovering the nature of the models, using both classic and new data sets. Requiring only a working knowledge of basic probability and statistical inference, Linear Models is a valuable book for courses on linear models at the upper-undergraduate and graduate levels. It is also an excellent reference for practitioners who use linear models to conduct research in the fields of econometrics, psychology, sociology, biology, and agriculture.

Regression Models for Time Series Analysis

Benjamin Kedem,Konstantinos Fokianos
Regression Models for Time Series Analysis Book PDF

Author : Benjamin Kedem,Konstantinos Fokianos
Publisher : Wiley-Interscience
Page : 360 pages
File Size : 42,9 Mb
Release : 2002-08-19
Category : Mathematics
ISBN : 0471363553

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Regression Models for Time Series Analysis by Benjamin Kedem,Konstantinos Fokianos Pdf

A thorough review of the most current regression methods in time series analysis Regression methods have been an integral part of time series analysis for over a century. Recently, new developments have made major strides in such areas as non-continuous data where a linear model is not appropriate. This book introduces the reader to newer developments and more diverse regression models and methods for time series analysis. Accessible to anyone who is familiar with the basic modern concepts of statistical inference, Regression Models for Time Series Analysis provides a much-needed examination of recent statistical developments. Primary among them is the important class of models known as generalized linear models (GLM) which provides, under some conditions, a unified regression theory suitable for continuous, categorical, and count data. The authors extend GLM methodology systematically to time series where the primary and covariate data are both random and stochastically dependent. They introduce readers to various regression models developed during the last thirty years or so and summarize classical and more recent results concerning state space models. To conclude, they present a Bayesian approach to prediction and interpolation in spatial data adapted to time series that may be short and/or observed irregularly. Real data applications and further results are presented throughout by means of chapter problems and complements. Notably, the book covers: * Important recent developments in Kalman filtering, dynamic GLMs, and state-space modeling * Associated computational issues such as Markov chain, Monte Carlo, and the EM-algorithm * Prediction and interpolation * Stationary processes

Essentials of Time Series for Financial Applications

Massimo Guidolin,Manuela Pedio
Essentials of Time Series for Financial Applications Book PDF

Author : Massimo Guidolin,Manuela Pedio
Publisher : Academic Press
Page : 435 pages
File Size : 47,8 Mb
Release : 2018-05-29
Category : Business & Economics
ISBN : 9780128134108

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Essentials of Time Series for Financial Applications by Massimo Guidolin,Manuela Pedio Pdf

Essentials of Time Series for Financial Applications serves as an agile reference for upper level students and practitioners who desire a formal, easy-to-follow introduction to the most important time series methods applied in financial applications (pricing, asset management, quant strategies, and risk management). Real-life data and examples developed with EViews illustrate the links between the formal apparatus and the applications. The examples either directly exploit the tools that EViews makes available or use programs that by employing EViews implement specific topics or techniques. The book balances a formal framework with as few proofs as possible against many examples that support its central ideas. Boxes are used throughout to remind readers of technical aspects and definitions and to present examples in a compact fashion, with full details (workout files) available in an on-line appendix. The more advanced chapters provide discussion sections that refer to more advanced textbooks or detailed proofs. Provides practical, hands-on examples in time-series econometrics Presents a more application-oriented, less technical book on financial econometrics Offers rigorous coverage, including technical aspects and references for the proofs, despite being an introduction Features examples worked out in EViews (9 or higher)