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Estimation Of A Dynamic Linear Model With Unknown Starting Values
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Book Synopsis Estimation of a Dynamic Linear Model with Unknown Starting Values by : R. D. Snyder
Download or read book Estimation of a Dynamic Linear Model with Unknown Starting Values written by R. D. Snyder and published by . This book was released on 1986 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 Estimation in Linear Models by : Truman Orville Lewis
Download or read book Estimation in Linear Models written by Truman Orville Lewis and published by Prentice Hall. This book was released on 1971 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Applied Econometrics with R by : Christian Kleiber
Download or read book Applied Econometrics with R written by Christian Kleiber and published by Springer Science & Business Media. This book was released on 2008-12-10 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and also a variety of packages with canned routines. We believe that R has great potential in econometrics, both for research and for teaching. There are at least three reasons for this: (1) R is mostly platform independent and runs on Microsoft Windows, the Mac family of operating systems, and various ?avors of Unix/Linux, and also on some more exotic platforms. (2) R is free software that can be downloaded and installed at no cost from a family of mirror sites around the globe, the Comprehensive R Archive Network (CRAN); hence students can easily install it on their own machines. (3) R is open-source software, so that the full source code is available and can be inspected to understand what it really does, learn from it, and modify and extend it. We also like to think that platform independence and the open-source philosophy make R an ideal environment for reproducible econometric research.
Book Synopsis Optimal Estimation of Dynamic Systems by : John L. Crassidis
Download or read book Optimal Estimation of Dynamic Systems written by John L. Crassidis and published by CRC Press. This book was released on 2011-10-26 with total page 745 pages. Available in PDF, EPUB and Kindle. Book excerpt: An ideal self-study guide for practicing engineers as well as senior undergraduate and beginning graduate students, this book highlights the importance of both physical and numerical modeling in solving dynamics-based estimation problems found in engineering systems, such as spacecraft attitude determination, GPS navigation, orbit determination, and aircraft tracking. With more than 100 pages of new material, this reorganized and expanded edition incorporates new theoretical results, a new chapter on advanced sequential state estimation, and additional examples and exercises. MATLAB codes are available on the book's website.
Book Synopsis Estimation of a Dynamic Linear Model by : R. D. Snyder
Download or read book Estimation of a Dynamic Linear Model written by R. D. Snyder and published by . This book was released on 1985 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Theory of Linear Models by : Bent Jorgensen
Download or read book Theory of Linear Models written by Bent Jorgensen and published by Routledge. This book was released on 2019-01-14 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: Providing a self-contained exposition of the theory of linear models, this treatise strikes a compromise between theory and practice, providing a sound theoretical basis while putting the theory to work in important cases.
Book Synopsis Estimation of Dynamic Models with Error Components by : Theodore Wilbur Anderson
Download or read book Estimation of Dynamic Models with Error Components written by Theodore Wilbur Anderson and published by . This book was released on 1980 with total page 52 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Small Sample Parameter Estimation for Forced Discrete Linear Dynamic Models by : Donald L. Stevens
Download or read book Small Sample Parameter Estimation for Forced Discrete Linear Dynamic Models written by Donald L. Stevens and published by . This book was released on 1979 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: The problem of estimating the parameters of a forced discrete linear dynamic model is considered. The system model is conceptualized to include the value of the initial state as a parameter. The forces driving the system are partitioned into accessible and inaccessible inputs. Accessible inputs are those that are measured; inaccessible inputs are all others, including random disturbances. Maximum likelihood and mean upper likelihood estimators are derived. The mean upper likelihood estimator is a variant of the mean likelihood estimator and apparently has more favorable small sample properties than does the maximum likelihood estimator. A computational algorithm that does not require the inversion or storage of large matrices is developed. The estimators and the algorithm are derived for models having an arbitrary number of inputs and a single output. The extension to a two output system is illustrated; further extension to an arbitrary number of outputs follows trivially. The techniques were developed for the analysis of possibly unique realizations of a process. The assumption that the inaccessible input is a stationary process is necessary only over the period of observation. Freedom from the more general usual assumptions was made possible by treatment of the initial state as a parameter. The derived estimation technique should be particularly suitable for the analysis of observational data. Simulation studies are used to compare the estimators and assess their properties. The mean upper likelihood estimator has consistently smaller mean square error than does the maximum likelihood estimator. An example application is presented, representing a unique realization of a dynamic system. The problems associated with determination of concurrence of a hypothetical "system change" with a temporally identified event are examined, and associated problems of inference of causality based on observational data are discussed with respect to the example.
Book Synopsis Bayesian Forecasting and Dynamic Models by : Mike West
Download or read book Bayesian Forecasting and Dynamic Models written by Mike West and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 720 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.
Book Synopsis Dynamic Disequilibrium Modeling: Theory and Applications by : William A. Barnett
Download or read book Dynamic Disequilibrium Modeling: Theory and Applications written by William A. Barnett and published by Cambridge University Press. This book was released on 1996-06-13 with total page 556 pages. Available in PDF, EPUB and Kindle. Book excerpt: . The organizers of the ninth symposium, which produced the current proceedings volume, were Claude Hillinger at the University of Munich, Giancarlo Gandolfo at the University of Rome "La Sapienza," A. R. Bergstrom at the University of Essex, and P. C. B. Phillips at Yale University.
Book Synopsis Measurement Data Modeling and Parameter Estimation by : Zhengming Wang
Download or read book Measurement Data Modeling and Parameter Estimation written by Zhengming Wang and published by CRC Press. This book was released on 2016-04-19 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the theories, methods, and application techniques of the measurement data mathematical modeling and parameter estimation. It seeks to build a bridge between mathematical theory and engineering practice in the measurement data processing field so theoretical researchers and technical engineers can communicate. It is organized with abundant materials, such as illustrations, tables, examples, and exercises. The authors create examples to apply mathematical theory innovatively to measurement and control engineering. Not only does this reference provide theoretical knowledge, it provides information on first hand experiences.
Book Synopsis Sensor Systems by : Clarence W. de Silva
Download or read book Sensor Systems written by Clarence W. de Silva and published by CRC Press. This book was released on 2016-12-19 with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers sensors and multiple sensor systems, including sensor networks and multi-sensor data fusion. It presents the physics and principles of operation and discusses sensor selection, ratings and performance specifications, necessary hardware and software for integration into an engineering system and signal processing and data analysis. Additionally, it discusses parameter estimation, decision making and practical applications. Even though the book has all the features of a course textbook, it also contains a wealth of practical information on the subject.
Book Synopsis Estimation of Parameters in a Linear Dynamic System with Missing Observations by : David Stewart Stoffer
Download or read book Estimation of Parameters in a Linear Dynamic System with Missing Observations written by David Stewart Stoffer and published by . This book was released on 1982 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Parameter Estimation and Hypothesis Testing in Linear Models by : Karl-Rudolf Koch
Download or read book Parameter Estimation and Hypothesis Testing in Linear Models written by Karl-Rudolf Koch and published by Springer. This book was released on 1988 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook on theoretical geodesy deals with the estimation of unknown parameters, the testing of hypothesis and the estimation of intervals in linear models. The reader will find presentations of the Gauss-Markoff model, the analysis of variance, the multivariate model, the model with unknown variance and covariance components and the regression model, as well as the mixed model for estimation random parameters. To make the book self-contained most of the necessary theorems of vector and matrix-algebra and the probability distributions for the test statistics are derived. Students of geodesy, as well as of mathematics and engineering, will find the geodetical application of mathematical and statistical models extremely useful.
Book Synopsis Theory and Programs for Dynamic Modeling of Tree Rings from Climate by : Paul C. Van Deusen
Download or read book Theory and Programs for Dynamic Modeling of Tree Rings from Climate written by Paul C. Van Deusen and published by . This book was released on 1988 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dynamic Neuroscience written by Zhe Chen and published by Springer. This book was released on 2017-12-27 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows how to develop efficient quantitative methods to characterize neural data and extra information that reveals underlying dynamics and neurophysiological mechanisms. Written by active experts in the field, it contains an exchange of innovative ideas among researchers at both computational and experimental ends, as well as those at the interface. Authors discuss research challenges and new directions in emerging areas with two goals in mind: to collect recent advances in statistics, signal processing, modeling, and control methods in neuroscience; and to welcome and foster innovative or cross-disciplinary ideas along this line of research and discuss important research issues in neural data analysis. Making use of both tutorial and review materials, this book is written for neural, electrical, and biomedical engineers; computational neuroscientists; statisticians; computer scientists; and clinical engineers.