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Nonlinear Statistical Estimation With Numerical Maximum Likelihood
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Book Synopsis Nonlinear Statistical Estimation with Numerical Maximum Likelihood by : Gerald Gerard Brown
Download or read book Nonlinear Statistical Estimation with Numerical Maximum Likelihood written by Gerald Gerard Brown and published by . This book was released on 1974 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: The topics of maximum likelihood estimation and nonlinear programming are developed thoroughly with emphasis on the numerical details of obtaining estimates from highly nonlinear models. Parametric estimation is discussed with the three parameter Weibull family of densities serving as an example. A general nonlinear programming method is discussed for both first and second order representations of the maximum likelihood estimaton, as well as a hybrid of both approaches. A new class of constrained parametric estimators is introduced with numerical methods for their determination. Structural estimation with maximum likelihood is examined, and a Bernoulli regression technique is presented.
Book Synopsis Nonlinear Estimation by : Gavin J.S. Ross
Download or read book Nonlinear Estimation written by Gavin J.S. Ross and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: Non-Linear Estimation is a handbook for the practical statistician or modeller interested in fitting and interpreting non-linear models with the aid of a computer. A major theme of the book is the use of 'stable parameter systems'; these provide rapid convergence of optimization algorithms, more reliable dispersion matrices and confidence regions for parameters, and easier comparison of rival models. The book provides insights into why some models are difficult to fit, how to combine fits over different data sets, how to improve data collection to reduce prediction variance, and how to program particular models to handle a full range of data sets. The book combines an algebraic, a geometric and a computational approach, and is illustrated with practical examples. A final chapter shows how this approach is implemented in the author's Maximum Likelihood Program, MLP.
Book Synopsis Statistical Tools for Nonlinear Regression by : Sylvie Huet
Download or read book Statistical Tools for Nonlinear Regression written by Sylvie Huet and published by Springer Science & Business Media. This book was released on 2013-04-17 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Tools for Nonlinear Regression presents methods for analyzing data. It has been expanded to include binomial, multinomial and Poisson non-linear models. The examples are analyzed with the free software nls2 updated to deal with the new models included in the second edition. The nls2 package is implemented in S-PLUS and R. Several additional tools are included in the package for calculating confidence regions for functions of parameters or calibration intervals, using classical methodology or bootstrap.
Book Synopsis Maximum Likelihood Estimation by Means of Nonlinear Least Squares by : Robert I. Jennrich
Download or read book Maximum Likelihood Estimation by Means of Nonlinear Least Squares written by Robert I. Jennrich and published by . This book was released on 1988 with total page 9 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistical Inference in Non-Linear Models in Econometrics by : Theertham Gangaram
Download or read book Statistical Inference in Non-Linear Models in Econometrics written by Theertham Gangaram and published by LAP Lambert Academic Publishing. This book was released on 2013 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the present book, Chapter-I is an introductory one. It gives general introduction about the nonlinear regression models. A brief review about the existing inferential procedures for nonlinear regression models has been give in Chapter-II. It contains various nonlinear methods, of estimation based on nonlinear least squares and maximum likelihood methods, besides the methods by using some numerical analysis procedures.Chapter-II and IV describe the specification and estimation of some important nonlinear production function models such as Cobb-Douglas, Constant Elasticity of Substitution (CES), Variable Elasticity of Substitution (VES) and Transcedental Logarithmic (Translog) Production functions. Some new Inferential procedures for certain nonlinear regression models have been proposed and developed in Chapter V. The directions for further research along with the conclusions have been presented in Chapter-VI. General selected references regarding nonlinear regression models have been documented under Bibliography.
Book Synopsis Nonlinear models in assessment in the social sciences: estimation by stochastic approximation, a frequentist mcmc by :
Download or read book Nonlinear models in assessment in the social sciences: estimation by stochastic approximation, a frequentist mcmc written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Neste trabalho apresentamos algumas contrubuições ao estudo dos modelosde avaliação estatística usados nas ciências sociais. As contribuiçõesoriginais são: i) uma descrição unificada sobre como a teoria da mediçãoevoluiu nas diversas disciplinas científicas; ii) uma resenha abrangente sobreos métodos de estimação por máxima verossimilhança empregados namedição estatística; iii) uma formulação geral do métodos da máxima verossimilhança tendo em vista a aplicação em modelos não-lineares; e principalmente, iv) a apresentação do método da aproximação estocástica naestimação dos modelos estatísticos de avaliação e medição. Os modelos não-lineares ocorrem freqüentemente nas ciências sociais ondeé importante a modelagem de variáveis de resposta dicotômicas ou ordinais. Em particular, este trabalho trata dos modelos da teoria da respostaao item, dos modelos de regressão logística e dos modelos de componentesaleatórias em geral. A estimação destes modelos ainda é objeto de intensapesquisa. Não se pode afirmar que exista um método de estimaçãointeiramente confiável. Os métodos aproximados produzem estimativas comviés acentuado nas componentes de variância, enquanto os métodos de integração numérica e os métodos bayesianos podem apresentar problemas deconvergência em muitos casos. O método da aproximação estocástica se baseiana maximização da verossimilhança e emprega o algoritmo de Robbins-Monro para resolver a equação do escore. Como um método estocástico elegera um processo de Markov que se aproxima das estimativas desejadas eportanto pode ser considerado um MCMC (Monte Carlo Markov chain)freqüentista. Nas simulações realizadas o método apresentou um bom desempenho, produzindo estimativas com viés pequeno, precisão razoável eraros problemas de convergência.
Book Synopsis Maximum Likelihood Estimation and Inference by : Russell B. Millar
Download or read book Maximum Likelihood Estimation and Inference written by Russell B. Millar and published by John Wiley & Sons. This book was released on 2011-07-26 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable models and new material for the practical implementation of integrated likelihood using the free ADMB software. Fundamental issues of statistical inference are also examined, with a presentation of some of the philosophical debates underlying the choice of statistical paradigm. Key features: Provides an accessible introduction to pragmatic maximum likelihood modelling. Covers more advanced topics, including general forms of latent variable models (including non-linear and non-normal mixed-effects and state-space models) and the use of maximum likelihood variants, such as estimating equations, conditional likelihood, restricted likelihood and integrated likelihood. Adopts a practical approach, with a focus on providing the relevant tools required by researchers and practitioners who collect and analyze real data. Presents numerous examples and case studies across a wide range of applications including medicine, biology and ecology. Features applications from a range of disciplines, with implementation in R, SAS and/or ADMB. Provides all program code and software extensions on a supporting website. Confines supporting theory to the final chapters to maintain a readable and pragmatic focus of the preceding chapters. This book is not just an accessible and practical text about maximum likelihood, it is a comprehensive guide to modern maximum likelihood estimation and inference. It will be of interest to readers of all levels, from novice to expert. It will be of great benefit to researchers, and to students of statistics from senior undergraduate to graduate level. For use as a course text, exercises are provided at the end of each chapter.
Book Synopsis Econometric Modelling with Time Series by : Vance Martin
Download or read book Econometric Modelling with Time Series written by Vance Martin and published by Cambridge University Press. This book was released on 2012-12-28 with total page 924 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a general framework for specifying, estimating, and testing time series econometric models. Special emphasis is given to estimation by maximum likelihood, but other methods are also discussed, including quasi-maximum likelihood estimation, generalized method of moments estimation, nonparametric estimation, and estimation by simulation. An important advantage of adopting the principle of maximum likelihood as the unifying framework for the book is that many of the estimators and test statistics proposed in econometrics can be derived within a likelihood framework, thereby providing a coherent vehicle for understanding their properties and interrelationships. In contrast to many existing econometric textbooks, which deal mainly with the theoretical properties of estimators and test statistics through a theorem-proof presentation, this book squarely addresses implementation to provide direct conduits between the theory and applied work.
Book Synopsis Nonlinear Estimation and Classification by : David D. Denison
Download or read book Nonlinear Estimation and Classification written by David D. Denison and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: Researchers in many disciplines face the formidable task of analyzing massive amounts of high-dimensional and highly-structured data. This is due in part to recent advances in data collection and computing technologies. As a result, fundamental statistical research is being undertaken in a variety of different fields. Driven by the complexity of these new problems, and fueled by the explosion of available computer power, highly adaptive, non-linear procedures are now essential components of modern "data analysis," a term that we liberally interpret to include speech and pattern recognition, classification, data compression and signal processing. The development of new, flexible methods combines advances from many sources, including approximation theory, numerical analysis, machine learning, signal processing and statistics. The proposed workshop intends to bring together eminent experts from these fields in order to exchange ideas and forge directions for the future.
Book Synopsis Statistical Inference in Nonlinear Models by : Geraldo da Silva e Souza
Download or read book Statistical Inference in Nonlinear Models written by Geraldo da Silva e Souza and published by . This book was released on 1979 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimation and hypothesis testing are considered for a system of simultaneous, monlinear, implicit equations. These problems are studied in a general setting. A given objective function, the pseudo likelihood, defines an estimator. Conditions are set forth such that this estimator is consistent and asymptotically normaly distributed. The Wald's test and analogs of the lagrange multiplier test and the likelihood ratio test are derived from this estimator and their null and non-null distributions are given. To illustrate the theory, results are applied in three instances: maximum likelihood estimation in simultaneous nonlinear systems, single equation nonlinear explicit models, and seemingly unrelated nonlinear regression models.
Book Synopsis Numerical Methods of Statistics by : John F. Monahan
Download or read book Numerical Methods of Statistics written by John F. Monahan and published by Cambridge University Press. This book was released on 2011-04-18 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains how computer software is designed to perform the tasks required for sophisticated statistical analysis. For statisticians, it examines the nitty-gritty computational problems behind statistical methods. For mathematicians and computer scientists, it looks at the application of mathematical tools to statistical problems. The first half of the book offers a basic background in numerical analysis that emphasizes issues important to statisticians. The next several chapters cover a broad array of statistical tools, such as maximum likelihood and nonlinear regression. The author also treats the application of numerical tools; numerical integration and random number generation are explained in a unified manner reflecting complementary views of Monte Carlo methods. Each chapter contains exercises that range from simple questions to research problems. Most of the examples are accompanied by demonstration and source code available from the author's website. New in this second edition are demonstrations coded in R, as well as new sections on linear programming and the Nelder–Mead search algorithm.
Book Synopsis Maximum Likelihood Estimation of Nonlinear Systems of Equations by : William A. Barnett
Download or read book Maximum Likelihood Estimation of Nonlinear Systems of Equations written by William A. Barnett and published by . This book was released on 1974 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Numerical Methods of Statistics by : John F. Monahan
Download or read book Numerical Methods of Statistics written by John F. Monahan and published by Cambridge University Press. This book was released on 2001-02-05 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 2001 book provides a basic background in numerical analysis and its applications in statistics.
Book Synopsis Parameter Estimation in Nonlinear Dynamic Systems by : W. J. H. Stortelder
Download or read book Parameter Estimation in Nonlinear Dynamic Systems written by W. J. H. Stortelder and published by . This book was released on 1998 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Numerical Issues in Statistical Computing for the Social Scientist by : Micah Altman
Download or read book Numerical Issues in Statistical Computing for the Social Scientist written by Micah Altman and published by John Wiley & Sons. This book was released on 2004-02-15 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: At last—a social scientist's guide through the pitfalls of modern statistical computing Addressing the current deficiency in the literature on statistical methods as they apply to the social and behavioral sciences, Numerical Issues in Statistical Computing for the Social Scientist seeks to provide readers with a unique practical guidebook to the numerical methods underlying computerized statistical calculations specific to these fields. The authors demonstrate that knowledge of these numerical methods and how they are used in statistical packages is essential for making accurate inferences. With the aid of key contributors from both the social and behavioral sciences, the authors have assembled a rich set of interrelated chapters designed to guide empirical social scientists through the potential minefield of modern statistical computing. Uniquely accessible and abounding in modern-day tools, tricks, and advice, the text successfully bridges the gap between the current level of social science methodology and the more sophisticated technical coverage usually associated with the statistical field. Highlights include: A focus on problems occurring in maximum likelihood estimation Integrated examples of statistical computing (using software packages such as the SAS, Gauss, Splus, R, Stata, LIMDEP, SPSS, WinBUGS, and MATLAB®) A guide to choosing accurate statistical packages Discussions of a multitude of computationally intensive statistical approaches such as ecological inference, Markov chain Monte Carlo, and spatial regression analysis Emphasis on specific numerical problems, statistical procedures, and their applications in the field Replications and re-analysis of published social science research, using innovative numerical methods Key numerical estimation issues along with the means of avoiding common pitfalls A related Web site includes test data for use in demonstrating numerical problems, code for applying the original methods described in the book, and an online bibliography of Web resources for the statistical computation Designed as an independent research tool, a professional reference, or a classroom supplement, the book presents a well-thought-out treatment of a complex and multifaceted field.
Book Synopsis Elements of Statistical Computing by : R.A. Thisted
Download or read book Elements of Statistical Computing written by R.A. Thisted and published by Routledge. This book was released on 2017-10-19 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistics and computing share many close relationships. Computing now permeates every aspect of statistics, from pure description to the development of statistical theory. At the same time, the computational methods used in statistical work span much of computer science. Elements of Statistical Computing covers the broad usage of computing in statistics. It provides a comprehensive account of the most important computational statistics. Included are discussions of numerical analysis, numerical integration, and smoothing. The author give special attention to floating point standards and numerical analysis; iterative methods for both linear and nonlinear equation, such as Gauss-Seidel method and successive over-relaxation; and computational methods for missing data, such as the EM algorithm. Also covered are new areas of interest, such as the Kalman filter, projection-pursuit methods, density estimation, and other computer-intensive techniques.
Book Synopsis Parameter Estimation in Stochastic Differential Equations by : Jaya P. N. Bishwal
Download or read book Parameter Estimation in Stochastic Differential Equations written by Jaya P. N. Bishwal and published by Springer. This book was released on 2007-09-26 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parameter estimation in stochastic differential equations and stochastic partial differential equations is the science, art and technology of modeling complex phenomena. The subject has attracted researchers from several areas of mathematics. This volume presents the estimation of the unknown parameters in the corresponding continuous models based on continuous and discrete observations and examines extensively maximum likelihood, minimum contrast and Bayesian methods.
