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Linear Models Time Series And Outliers 3 Stochastic Difference Equation Models
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Book Synopsis Linear Models, Time Series and Outliers. 3. Stochastic Difference Equation Models by : Bovas Abraham
Download or read book Linear Models, Time Series and Outliers. 3. Stochastic Difference Equation Models written by Bovas Abraham and published by . This book was released on 1975 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: There are clear cut cases in which models involving time are deterministic since they describe some known physical phenomenon which is a function of time. In other instances, there is properly speaking no fixed deterministic component but time series can be modeled by stochastic difference models. In this report it is shown how the need for a deterministic component may be exposed by the analysis itself. A suitably general framework is provided by stochastic difference equation models so that deterministic components are automatically accommodated.
Book Synopsis Linear Models, Time Series and Outliers by : Bovas Abraham
Download or read book Linear Models, Time Series and Outliers written by Bovas Abraham and published by . This book was released on 1975 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Technical Report by : University of Wisconsin--Madison. Department of Statistics
Download or read book Technical Report written by University of Wisconsin--Madison. Department of Statistics and published by . This book was released on 1972 with total page 732 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Linear Models for Multivariate, Time Series, and Spatial Data by : Ronald Christensen
Download or read book Linear Models for Multivariate, Time Series, and Spatial Data written by Ronald Christensen and published by Springer Science & Business Media. This book was released on 1991 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: A companion volume to Plane answers to complex questions: the theory of linear models (1987), presenting six chapters with shallow treatments of very broad topics showing how the properties of three fundamental ideas from standard linear model theory can be used to examine multivariate, time series,
Book Synopsis Studies in the Modelling of Discrete Time Series by : Greta Marianne Ljung
Download or read book Studies in the Modelling of Discrete Time Series written by Greta Marianne Ljung and published by . This book was released on 1976 with total page 542 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Extracting Knowledge From Time Series by : Boris P. Bezruchko
Download or read book Extracting Knowledge From Time Series written by Boris P. Bezruchko and published by Springer Science & Business Media. This book was released on 2010-09-03 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical modelling is ubiquitous. Almost every book in exact science touches on mathematical models of a certain class of phenomena, on more or less speci?c approaches to construction and investigation of models, on their applications, etc. As many textbooks with similar titles, Part I of our book is devoted to general qu- tions of modelling. Part II re?ects our professional interests as physicists who spent much time to investigations in the ?eld of non-linear dynamics and mathematical modelling from discrete sequences of experimental measurements (time series). The latter direction of research is known for a long time as “system identi?cation” in the framework of mathematical statistics and automatic control theory. It has its roots in the problem of approximating experimental data points on a plane with a smooth curve. Currently, researchers aim at the description of complex behaviour (irregular, chaotic, non-stationary and noise-corrupted signals which are typical of real-world objects and phenomena) with relatively simple non-linear differential or difference model equations rather than with cumbersome explicit functions of time. In the second half of the twentieth century, it has become clear that such equations of a s- ?ciently low order can exhibit non-trivial solutions that promise suf?ciently simple modelling of complex processes; according to the concepts of non-linear dynamics, chaotic regimes can be demonstrated already by a third-order non-linear ordinary differential equation, while complex behaviour in a linear model can be induced either by random in?uence (noise) or by a very high order of equations.
Book Synopsis Dynamic Linear Models with R by : Giovanni Petris
Download or read book Dynamic Linear Models with R written by Giovanni Petris and published by Springer Science & Business Media. This book was released on 2009-06-12 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.
Book Synopsis Computational Intelligence, Theory and Applications by : Bernd Reusch
Download or read book Computational Intelligence, Theory and Applications written by Bernd Reusch and published by Springer Science & Business Media. This book was released on 2006-08-18 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th Dortmund Fuzzy Days, held in Dortmund, Germany, 2004. The Fuzzy-Days conference has established itself as an international forum for the discussion of new results in the field of Computational Intelligence. All the papers had to undergo a thorough review guaranteeing a solid quality of the programme. The papers are devoted to foundational and practical issues in fuzzy systems, neural networks, evolutionary algorithms, and machine learning and thus cover the whole range of computational intelligence.
Book Synopsis Introduction to Linear Models and Statistical Inference by : Steven J. Janke
Download or read book Introduction to Linear Models and Statistical Inference written by Steven J. Janke and published by John Wiley & Sons. This book was released on 2005-09-15 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: A multidisciplinary approach that emphasizes learning by analyzing real-world data sets This book is the result of the authors' hands-on classroom experience and is tailored to reflect how students best learn to analyze linear relationships. The text begins with the introduction of four simple examples of actual data sets. These examples are developed and analyzed throughout the text, and more complicated examples of data sets are introduced along the way. Taking a multidisciplinary approach, the book traces the conclusion of the analyses of data sets taken from geology, biology, economics, psychology, education, sociology, and environmental science. As students learn to analyze the data sets, they master increasingly sophisticated linear modeling techniques, including: * Simple linear models * Multivariate models * Model building * Analysis of variance (ANOVA) * Analysis of covariance (ANCOVA) * Logistic regression * Total least squares The basics of statistical analysis are developed and emphasized, particularly in testing the assumptions and drawing inferences from linear models. Exercises are included at the end of each chapter to test students' skills before moving on to more advanced techniques and models. These exercises are marked to indicate whether calculus, linear algebra, or computer skills are needed. Unlike other texts in the field, the mathematics underlying the models is carefully explained and accessible to students who may not have any background in calculus or linear algebra. Most chapters include an optional final section on linear algebra for students interested in developing a deeper understanding. The many data sets that appear in the text are available on the book's Web site. The MINITAB(r) software program is used to illustrate many of the examples. For students unfamiliar with MINITAB(r), an appendix introduces the key features needed to study linear models. With its multidisciplinary approach and use of real-world data sets that bring the subject alive, this is an excellent introduction to linear models for students in any of the natural or social sciences.
Book Synopsis Richly Parameterized Linear Models by : James S. Hodges
Download or read book Richly Parameterized Linear Models written by James S. Hodges and published by CRC Press. This book was released on 2016-04-19 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: A First Step toward a Unified Theory of Richly Parameterized Linear ModelsUsing 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 Param
Book Synopsis Introduction to Statistical Time Series by : Wayne A. Fuller
Download or read book Introduction to Statistical Time Series written by Wayne A. Fuller and published by John Wiley & Sons. This book was released on 2009-09-25 with total page 734 pages. Available in PDF, EPUB and Kindle. Book excerpt: The subject of time series is of considerable interest, especiallyamong researchers in econometrics, engineering, and the naturalsciences. As part of the prestigious Wiley Series in Probabilityand Statistics, this book provides a lucid introduction to thefield and, in this new Second Edition, covers the importantadvances of recent years, including nonstationary models, nonlinearestimation, multivariate models, state space representations, andempirical model identification. New sections have also been addedon the Wold decomposition, partial autocorrelation, long memoryprocesses, and the Kalman filter. Major topics include: * Moving average and autoregressive processes * Introduction to Fourier analysis * Spectral theory and filtering * Large sample theory * Estimation of the mean and autocorrelations * Estimation of the spectrum * Parameter estimation * Regression, trend, and seasonality * Unit root and explosive time series To accommodate a wide variety of readers, review material,especially on elementary results in Fourier analysis, large samplestatistics, and difference equations, has been included.
Book Synopsis Linear Models, TIme Series and Outliers, 5: Outliers in Time Series by : Bovas Abraham
Download or read book Linear Models, TIme Series and Outliers, 5: Outliers in Time Series written by Bovas Abraham and published by . This book was released on 1975 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt: Assuming a linear time series model a Bayesian approach to the outlier problem is used in which with probability 1 alpha a probability is generated by a standard distribution and with probability alpha is generated by an alternative distribution with the same variance but with the mean displaced. The effect of estimation of parameters is studied and consideration is given to the selection of bad values.
Book Synopsis Contributions to Survey Sampling and Applied Statistics by : Herbert Aron David
Download or read book Contributions to Survey Sampling and Applied Statistics written by Herbert Aron David and published by . This book was released on 1978 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Contributions to Survey Sampling and Applied Statistics.
Book Synopsis Linear Models in Statistics by : Alvin C. Rencher
Download or read book Linear Models in Statistics written by Alvin C. Rencher and published by John Wiley & Sons. This book was released on 2008-01-07 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Linear Models written by Debasis Sengupta and published by World Scientific. This book was released on 2003 with total page 646 pages. Available in PDF, EPUB and Kindle. Book excerpt: Linear Models: An Integrated Approach aims to provide a clear and deep understanding of the general linear model using simple statistical ideas. Elegant geometric arguments are also invoked as needed and a review of vector spaces and matrices is provided to make the treatment self-contained. Complex, matrix-algebraic methods, such as those used in the rank-deficient case, are replaced by statistical proofs that are more transparent and that show the parallels with the simple linear model. This book has the following special features: Use of simple statistical ideas such as linear zero functions and covariance adjustment to explain the fundamental as well as advanced concepts Emphasis on the statistical interpretation of complex algebraic results A thorough treatment of the singular linear model, including the case of multivariate response A unified discussion on models with a partially unknown dispersion matrix, including mixed- effects/variance-components models and models for spatial,and time series data Insight into updates on the linear model and their connection with diagnostics, design, variable selection, the Kalman filter, etc. An extensive discussion on the foundations of linear inference, along with linear alternatives to least squares Coverage of other special topics, such as collinearity, stochastic and inequality constraints, misspecified models, etc. Simpler proofs of numerous known results Pointers to current research through examples and exercises
Book Synopsis Forecasting: principles and practice by : Rob J Hyndman
Download or read book Forecasting: principles and practice written by Rob J Hyndman and published by OTexts. This book was released on 2018-05-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Threshold Models in Non-linear Time Series Analysis by : H. Tong
Download or read book Threshold Models in Non-linear Time Series Analysis written by H. Tong and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 333 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
