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A Strategy For Parameter Estimation In The Nonlinear Errors In Variables Model
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Book Synopsis A Strategy for Parameter Estimation in the Nonlinear Errors in Variables Model by : Daniela Elena Cojocaru
Download or read book A Strategy for Parameter Estimation in the Nonlinear Errors in Variables Model written by Daniela Elena Cojocaru and published by . This book was released on 2004 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Measurement Error in Nonlinear Models by : Raymond J. Carroll
Download or read book Measurement Error in Nonlinear Models written by Raymond J. Carroll and published by CRC Press. This book was released on 1995-07-06 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph provides an up-to-date discussion of analysis strategies for regression problems in which predictor variables are measured with errors. The analysis of nonlinear regression models includes generalized linear models, transform-both-sides models and quasilikelihood and variance function problems. The text concentrates on the general ideas and strategies of estimation and inference rather than being concerned with a specific problem. Measurement error occurs in many fields, such as biometry, epidemiology and economics. In particular, the book contains a large number of epidemiological examples. An outline of strategies for handling progressively more difficult problems is also provided.
Book Synopsis Parameter Estimation and Inverse Problems by : Richard C. Aster
Download or read book Parameter Estimation and Inverse Problems written by Richard C. Aster and published by Elsevier. This book was released on 2018-10-16 with total page 406 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parameter Estimation and Inverse Problems, Third Edition, is structured around a course at New Mexico Tech and is designed to be accessible to typical graduate students in the physical sciences who do not have an extensive mathematical background. The book is complemented by a companion website that includes MATLAB codes that correspond to examples that are illustrated with simple, easy to follow problems that illuminate the details of particular numerical methods. Updates to the new edition include more discussions of Laplacian smoothing, an expansion of basis function exercises, the addition of stochastic descent, an improved presentation of Fourier methods and exercises, and more. Features examples that are illustrated with simple, easy to follow problems that illuminate the details of a particular numerical method Includes an online instructor’s guide that helps professors teach and customize exercises and select homework problems Covers updated information on adjoint methods that are presented in an accessible manner
Book Synopsis Consistent Estimation for Some Nonlinear Errors-in-variables Models by : Cheng Hsiao
Download or read book Consistent Estimation for Some Nonlinear Errors-in-variables Models written by Cheng Hsiao and published by . This book was released on 1988 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Parameter Estimation in Engineering and Science by : James Vere Beck
Download or read book Parameter Estimation in Engineering and Science written by James Vere Beck and published by James Beck. This book was released on 1977 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to and survey of parameter estimation; Probability; Introduction to statistics; Parameter estimation methods; Introduction to linear estimation; Matrix analysis for linear parameter estimation; Minimization of sum of squares functions for models nonlinear in parameters; Design of optimal experiments.
Book Synopsis Estimation for the Nonlinear Errors-in-variables Model by : Yongming Qu
Download or read book Estimation for the Nonlinear Errors-in-variables Model written by Yongming Qu and published by . This book was released on 2002 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimation of the parameters of the functional nonlinear measurement error model is considered. A simulation bias adjusted (SIMBA) estimation procedure is presented. In the SIMBA procedure, internal Monte Carlo simulation based on the sample data is used to adjust a naive estimator, such as the ordinary least squares estimator, for bias. Let the measurement error variance [(Sigma symbol followed by superscript 2 over subscript un)] be a sequence depending on the sample size n, and assume [Sigma symbol followed by superscript 2 over subscript un] [right pointing arrow] 0 as n [right pointing arrow] [Infinity symbol]. Under some regularity conditions, the order in probability convergence rate for the SIMBA estimator is max [(Sigma symbol followed by superscript 4 over subscript un, n superscript -1/2)], while the order in probability convergence rate for the ordinary least squares estimator is max [(Sigma symbol followed by superscript 2 over subscript un, n superscript -1/2)]. Monte Carlo simulation is conducted to test the performance of SIMBA for four models: linear model, quadratic model, cosine model and logistic model. Monte Carlo simulation shows that the SIMBA estimation procedure outperforms or is comparable to methods such as simulation extrapolation, regression calibration and adjusted least squares. An example application of SIMBA estimation for the logistic regression model with errors in variables is given. In the example, the relation between minerals from dietary intake and the supplement use for people over 50 is studied. The data are from the two surveys: the Third National Health and Nutrition Examination Survey and the related Supplemental Nutrition Survey. One interesting result is that people whose dietary intake of minerals is high are more likely to take supplements.
Book Synopsis Nonlinear Parameter Estimation by : Yonathan Bard
Download or read book Nonlinear Parameter Estimation written by Yonathan Bard and published by . This book was released on 1974 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Problem formulation; Estimators and their properties; Methods of estimation; Computation of estimates; Interpretation of the estimates; Dynamic models; Some special problems; Design of experiments.
Book Synopsis Consistent Estimation of Linear and Nonlinear Errors-in- Variables Models with Validation Information by : Lung-Fei Lee
Download or read book Consistent Estimation of Linear and Nonlinear Errors-in- Variables Models with Validation Information written by Lung-Fei Lee and published by . This book was released on 1992 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Consistent Estimation of Linear and Nonlinear Errors-in- Variables Models with Validation Information by : Lung-Fei Lee
Download or read book Consistent Estimation of Linear and Nonlinear Errors-in- Variables Models with Validation Information written by Lung-Fei Lee and published by . This book was released on 1992 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Estimation of Nonlinear Errors-in-variables Models by : Cheng Hsiao
Download or read book Estimation of Nonlinear Errors-in-variables Models written by Cheng Hsiao and published by . This book was released on 1996 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 A Simulated Semiparametric Estimation of Nonlinear Errors-in-variables Models by : Liqun Wang
Download or read book A Simulated Semiparametric Estimation of Nonlinear Errors-in-variables Models written by Liqun Wang and published by . This book was released on 1996 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Estimation of Nonlinear Errors-in-variables Models by : Kirk M. Wolter
Download or read book Estimation of Nonlinear Errors-in-variables Models written by Kirk M. Wolter and published by . This book was released on 2002 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: An estimator is presented for the coefficients of the quadratic functional relationship. The estimator is show to be asymptotically normally distributed as the sample size increases.
Book Synopsis Flexible Simulated Moment Estimation of Nonlinear Errors-in-variables Models by : Whitney K. Newey
Download or read book Flexible Simulated Moment Estimation of Nonlinear Errors-in-variables Models written by Whitney K. Newey and published by . This book was released on 1993 with total page 29 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Measurement Error in Nonlinear Models by : Raymond J. Carroll
Download or read book Measurement Error in Nonlinear Models written by Raymond J. Carroll and published by CRC Press. This book was released on 2006-06-21 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: It's been over a decade since the first edition of Measurement Error in Nonlinear Models splashed onto the scene, and research in the field has certainly not cooled in the interim. In fact, quite the opposite has occurred. As a result, Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition has been revamped and ex
Book Synopsis A Parallel Implementation for Parameter Estimation with Implicit Models by : Lorenz T. Biegler
Download or read book A Parallel Implementation for Parameter Estimation with Implicit Models written by Lorenz T. Biegler and published by . This book was released on 1992 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "Parameter estimation and data regression represent special classes of optimization problems. Often, nonlinear programming methods can be tailored to take advantage of the least squares, or more generally, the maximum likelihood, objective function. In previous studies we have developed large-scale nonlinear programming methods that are based on tailored quasi-Newton updating strategies and matrix decomposition of the process model. The resulting algorithms converge in a reduced space of the parameters while simultaneously converging the process model. Moreover, tailoring the method to least squares functions led to significant improvements in the performance of the algorithm. These approaches can be very efficient for both explicit and implit [sic] models (i.e., problems with small and large degrees of freedom, respectively). In the latter case, degrees of freedom are proportional to a potentially large number of data sets. Applications of this case include errors-in- all-variables estimation, data reconciliation and identification of process parameters. To deal with this structure, we apply a decomposition approach that performs a quadratic programming factorization for each data set. Because these are small components of large problems, an efficient and reliable algorithm results. These methods have generally been implemented on standard von Neumann architectures and few studies exist that exploit the parallelism of nonlinear programming algorithms. It is therefore interesting to note that for implicit model parameter estimation and related process applications, this approach can be quite amenable to parallel computation, because the major cost occurs in matrix decompositions for each data set. Here we describe an implementation of this approach on the Alliant FX/8 parallel computer at the Advanced Computing Research Facility at Argonne National Laboratory. Special attention is paid to the architecture of this machine and its effect on the performance of the algorithm. This approach is demonstrated on five small, undetermined regression problems as well as a larger process example that demonstrates simultaneous data reconciliation and parameter estimation."
Book Synopsis Parameter Estimation in Nonlinear Continuous-time Dynamic Models with Modelling Errors and Process Disturbances by : M. Saeed Varziri
Download or read book Parameter Estimation in Nonlinear Continuous-time Dynamic Models with Modelling Errors and Process Disturbances written by M. Saeed Varziri and published by . This book was released on 2008 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model-based control and process optimization technologies are becoming more commonly used by chemical engineers. These algorithms rely on fundamental or empirical models that are frequently described by systems of differential equations with unknown parameters. It is, therefore, very important for modellers of chemical engineering processes to have access to reliable and efficient tools for parameter estimation in dynamic models. The purpose of this thesis is to develop an efficient and easy-to-use parameter estimation algorithm that can address difficulties that frequently arise when estimating parameters in nonlinear continuous-time dynamic models of industrial processes. The proposed algorithm has desirable numerical stability properties that stem from using piece-wise polynomial discretization schemes to transform the model differential equations into a set of algebraic equations. Consequently, parameters can be estimated by solving a nonlinear programming problem without requiring repeated numerical integration of the differential equations. Possible modelling discrepancies and process disturbances are accounted for in the proposed algorithm, and estimates of the process disturbance intensities can be obtained along with estimates of model parameters and states. Theoretical approximate confidence interval expressions for the parameters are developed. Through a practical two-phase nylon reactor example, as well as several simulation studies using stirred tank reactors, it is shown that the proposed parameter estimation algorithm can address difficulties such as: different types of measured responses with different levels of measurement noise, measurements taken at irregularly-spaced sampling times, unknown initial conditions for some state variables, unmeasured state variables, and unknown disturbances that enter the process and influence its future behaviour.
