Measurement Error in Nonlinear Models, Carroll, Raymond J.

Автор: Erik W. Grafarend , Silvelyn Zwanzig , Joseph L. Awange
Название: Applications of Linear and Nonlinear Models
ISBN: 3030945979 ISBN-13(EAN): 9783030945978
Издательство: Springer
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Цена: 186330.00 T
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Описание: This book provides numerous examples of linear and nonlinear model applications. Here, we present a nearly complete treatment of the Grand Universe of linear and weakly nonlinear regression models within the first 8 chapters. Our point of view is both an algebraic view and a stochastic one. For example, there is an equivalent lemma between a best, linear uniformly unbiased estimation (BLUUE) in a Gauss–Markov model and a least squares solution (LESS) in a system of linear equations. While BLUUE is a stochastic regression model, LESS is an algebraic solution. In the first six chapters, we concentrate on underdetermined and overdetermined linear systems as well as systems with a datum defect. We review estimators/algebraic solutions of type MINOLESS, BLIMBE, BLUMBE, BLUUE, BIQUE, BLE, BIQUE, and total least squares. The highlight is the simultaneous determination of the first moment and the second central moment of a probability distribution in an inhomogeneous multilinear estimation by the so-called E-D correspondence as well as its Bayes design. In addition, we discuss continuous networks versus discrete networks, use of Grassmann–Plucker coordinates, criterion matrices of type Taylor–Karman as well as FUZZY sets. Chapter seven is a speciality in the treatment of an overjet. This second edition adds three new chapters: (1) Chapter on integer least squares that covers (i) model for positioning as a mixed integer linear model which includes integer parameters. (ii) The general integer least squares problem is formulated, and the optimality of the least squares solution is shown. (iii) The relation to the closest vector problem is considered, and the notion of reduced lattice basis is introduced. (iv) The famous LLL algorithm for generating a Lovasz reduced basis is explained. (2) Bayes methods that covers (i) general principle of Bayesian modeling. Explain the notion of prior distribution and posterior distribution. Choose the pragmatic approach for exploring the advantages of iterative Bayesian calculations and hierarchical modeling. (ii) Present the Bayes methods for linear models with normal distributed errors, including noninformative priors, conjugate priors, normal gamma distributions and (iii) short outview to modern application of Bayesian modeling. Useful in case of nonlinear models or linear models with no normal distribution: Monte Carlo (MC), Markov chain Monte Carlo (MCMC), approximative Bayesian computation (ABC) methods. (3) Error-in-variables models, which cover: (i) Introduce the error-in-variables (EIV) model, discuss the difference to least squares estimators (LSE), (ii) calculate the total least squares (TLS) estimator. Summarize the properties of TLS, (iii) explain the idea of simulation extrapolation (SIMEX) estimators, (iv) introduce the symmetrized SIMEX (SYMEX) estimator and its relation to TLS, and (v) short outview to nonlinear EIV models. The chapter on algebraic solution of nonlinear system of equations has also been updated in line with the new emerging field of hybrid numeric-symbolic solutions to systems of nonlinear equations, ermined system of nonlinear equations on curved manifolds. The von Mises–Fisher distribution is characteristic for circular or (hyper) spherical data. Our last chapter is devoted to probabilistic regression, the special Gauss–Markov model with random effects leading to estimators of type BLIP and VIP including Bayesian estimation. A great part of the work is presented in four appendices. Appendix A is a treatment, of tensor algebra, namely linear algebra, matrix algebra, and multilinear algebra. Appendix B is devoted to sampling distributions and their use in terms of confidence intervals and confidence regions. Appendix C reviews the elementary notions of statistics, namely random events and stochastic processes. Appendix D introduces the basics of Groebner basis algebra, its careful definition, the Buchberger algorithm, especially the C. F. Gauss combinatorial algorithm.

Автор: Fisher Jr.
Название: Explanatory Models, Unit Standards, and Personalized Learning in Educational Measurement
ISBN: 981193746X ISBN-13(EAN): 9789811937460
Издательство: Springer
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Цена: 37260.00 T
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Описание: The papers by Jack Stenner included in this book document the technical details of an art and science of measurement that creates new entrepreneurial business opportunities. Jack brought theory, instruments, and data together in ways that are applicable not only in the context of a given test of reading or mathematics ability, but which more importantly catalyzed literacy and numeracy capital in new fungible expressions. Though Jack did not reflect in writing on the inferential, constructive processes in which he engaged, much can be learned by reviewing his work with his accomplishments in mind. A Foreword by Stenner's colleague and co-author on multiple works, William P. Fisher, Jr., provides key clues concerning (a) how Jack's understanding of measurement and its values aligns with social and historical studies of science and technology, and (b) how recent developments in collaborations of psychometricians and metrologists are building on and expanding Jack's accomplishments. This is an open access book.

Автор: Fisher Jr.
Название: Explanatory Models, Unit Standards, and Personalized Learning in Educational Measurement
ISBN: 9811937494 ISBN-13(EAN): 9789811937491
Издательство: Springer
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Цена: 37260.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The papers by Jack Stenner included in this book document the technical details of an art and science of measurement that creates new entrepreneurial business opportunities. Jack brought theory, instruments, and data together in ways that are applicable not only in the context of a given test of reading or mathematics ability, but which more importantly catalyzed literacy and numeracy capital in new fungible expressions. Though Jack did not reflect in writing on the inferential, constructive processes in which he engaged, much can be learned by reviewing his work with his accomplishments in mind. A Foreword by Stenner's colleague and co-author on multiple works, William P. Fisher, Jr., provides key clues concerning (a) how Jack's understanding of measurement and its values aligns with social and historical studies of science and technology, and (b) how recent developments in collaborations of psychometricians and metrologists are building on and expanding Jack's accomplishments. This is an open access book.

Автор: Yi, Grace Y ; Delaigle, Aurore ; Gustafson, Paul
Название: Handbook of Measurement Error Models
ISBN: 1032070080 ISBN-13(EAN): 9781032070087
Издательство: Taylor&Francis
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Цена: 81650.00 T
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Автор: Bolfarine Heleno, de Castro Mбrio, Galea Manuel
Название: Regression Models for the Comparison of Measurement Methods
ISBN: 3030579344 ISBN-13(EAN): 9783030579340
Издательство: Springer
Цена: 46570.00 T
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Описание: This book provides an updated account of the regression techniques employed in comparing analytical methods and to test the biases of one method relative to others - a problem commonly found in fields like analytical chemistry, biology, engineering, and medicine.

Автор: Grace Y. Yi
Название: Statistical Analysis with Measurement Error or Misclassification
ISBN: 1493966383 ISBN-13(EAN): 9781493966387
Издательство: Springer
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Цена: 111790.00 T
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Описание: Inference Framework and Method.- Measurement Error and Misclassification: Introduction.- Survival Data with Measurement Error.- Recurrent Event Data with Measurement Error.- Longitudinal Data with Covariate Measurement Error.- Multi-State Models with Error-Prone Data.- Case-Control Studies with Measurement Error or Misclassification.- Analysis with Error in Responses.- Miscellaneous Topics.- Appendix.- References.

Автор: Grace Y. Yi
Название: Statistical Analysis with Measurement Error or Misclassification
ISBN: 1493982575 ISBN-13(EAN): 9781493982578
Издательство: Springer
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Цена: 139750.00 T
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Описание: This monograph on measurement error and misclassification covers a broad range of problems and emphasizes unique features in modeling and analyzing problems arising from medical research and epidemiological studies. Many measurement error and misclassification problems have been addressed in various fields over the years as well as with a wide spectrum of data, including event history data (such as survival data and recurrent event data), correlated data (such as longitudinal data and clustered data), multi-state event data, and data arising from case-control studies. Statistical Analysis with Measurement Error or Misclassification: Strategy, Method and Application brings together assorted methods in a single text and provides an update of recent developments for a variety of settings. Measurement error effects and strategies of handling mismeasurement for different models are closely examined in combination with applications to specific problems. Readers with diverse backgrounds and objectives can utilize this text. Familiarity with inference methods—such as likelihood and estimating function theory—or modeling schemes in varying settings—such as survival analysis and longitudinal data analysis—can result in a full appreciation of the material, but it is not essential since each chapter provides basic inference frameworks and background information on an individual topic to ease the access of the material. The text is presented in a coherent and self-contained manner and highlights the essence of commonly used modeling and inference methods. This text can serve as a reference book for researchers interested in statistical methodology for handling data with measurement error or misclassification; as a textbook for graduate students, especially for those majoring in statistics and biostatistics; or as a book for applied statisticians whose interest focuses on analysis of error-contaminated data. Grace Y. Yi is Professor of Statistics and University Research Chair at the University of Waterloo. She is the 2010 winner of the CRM-SSC Prize, an honor awarded in recognition of a statistical scientist's professional accomplishments in research during the first 15 years after having received a doctorate. She is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute.

Автор: Tatarinova Tatiana, Schumitzky Alan
Название: Nonlinear Mixture Models: A Bayesian Approach
ISBN: 1848167563 ISBN-13(EAN): 9781848167568
Издательство: World Scientific Publishing
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Цена: 95040.00 T
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Описание: Provides an introduction to the important subject of nonlinear mixture models from a Bayesian perspective. This title contains background material, a brief description of Markov chain theory, as well as novel algorithms and their applications.

Автор: Juan I. Yuz; Graham C. Goodwin
Название: Sampled-Data Models for Linear and Nonlinear Systems
ISBN: 1447155610 ISBN-13(EAN): 9781447155614
Издательство: Springer
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Цена: 121890.00 T
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Описание: In this book, rather than emphasize differences between sampled-data and continuous-time systems, the authors proceed from the premise that, with modern sampling rates as high as they are, it is more appropriate to emphasise connections and similarities.

Автор: Juan I. Yuz; Graham C. Goodwin
Название: Sampled-Data Models for Linear and Nonlinear Systems
ISBN: 1447169972 ISBN-13(EAN): 9781447169970
Издательство: Springer
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Цена: 104480.00 T
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Описание: In this book, rather than emphasize differences between sampled-data and continuous-time systems, the authors proceed from the premise that, with modern sampling rates as high as they are, it is more appropriate to emphasise connections and similarities.

Автор: Gy?rgy Terdik
Название: Bilinear Stochastic Models and Related Problems of Nonlinear Time Series Analysis
ISBN: 0387988726 ISBN-13(EAN): 9780387988726
Издательство: Springer
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Цена: 97820.00 T
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Описание: The first two chapters are devoted to the basic theory of nonlinear functions of stationary Gaussian processes, Hermite polynomials, cumulants and higher order spectra, multiple Wiener-Ito integrals and finally chaotic Wiener-Ito spectral representation of subordinated processes.

Автор: Andrej P?zman
Название: Nonlinear Statistical Models
ISBN: 0792322479 ISBN-13(EAN): 9780792322474
Издательство: Springer
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Цена: 88500.00 T
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