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Author : Roger Higdon
Publisher :
ISBN 13 :
Total Pages : 25 pages
Book Rating : 4.:/5 (13 download)
Download or read book Maximum Likelihood Computations for Regression with Measurement Error written by Roger Higdon and published by . This book was released on 1998 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Raymond J. Carroll
Publisher : CRC Press
ISBN 13 : 1420010131
Total Pages : 484 pages
Book Rating : 4.4/5 (2 download)
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
Author : Roger Higdon
Publisher :
ISBN 13 :
Total Pages : 176 pages
Book Rating : 4.:/5 (41 download)
Download or read book Application and Computation of Likelihood Methods for Regression with Measurement Error written by Roger Higdon and published by . This book was released on 1998 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis advocates the use of maximum likelihood analysis for generalized regression models with measurement error in a single explanatory variable. This will be done first by presenting a computational algorithm and the numerical details for carrying out this algorithm on a wide variety of models. The computational methods will be based on the EM algorithm in conjunction with the use of Gauss-Hermite quadrature to approximate integrals in the E-step. Second, this thesis will demonstrate the relative superiority of likelihood-ratio tests and confidence intervals over those based on asymptotic normality of estimates and standard errors, and that likelihood methods may be more robust in these situations than previously thought. The ability to carry out likelihood analysis under a wide range of distributional assumptions, along with the advantages of likelihood ratio inference and the encouraging robustness results make likelihood analysis a practical option worth considering in regression problems with explanatory variable measurement error.
Author : Raymond J. Carroll
Publisher : CRC Press
ISBN 13 : 9780412047213
Total Pages : 334 pages
Book Rating : 4.0/5 (472 download)
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.
Author : Eun-Young Suh
Publisher :
ISBN 13 :
Total Pages : 202 pages
Book Rating : 4.:/5 (482 download)
Download or read book Semiparametric Maximum Likelihood for Regression with Measurement Error written by Eun-Young Suh and published by . This book was released on 2001 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Semiparametric maximum likelihood analysis allows inference in errors-invariables models with small loss of efficiency relative to full likelihood analysis but with significantly weakened assumptions. In addition, since no distributional assumptions are made for the nuisance parameters, the analysis more nearly parallels that for usual regression. These highly desirable features and the high degree of modelling flexibility permitted warrant the development of the approach for routine use. This thesis does so for the special cases of linear and nonlinear regression with measurement errors in one explanatory variable. A transparent and flexible computational approach is developed, the analysis is exhibited on some examples, and finite sample properties of estimates, approximate standard errors, and likelihood ratio inference are clarified with simulation.
Author : Antara Majumdar
Publisher :
ISBN 13 :
Total Pages : 194 pages
Book Rating : 4.:/5 (181 download)
Download or read book Maximum Likelihood Estimation of Measurement Error Models Based on the Monte Carlo EM Algorithm written by Antara Majumdar and published by . This book was released on 2007 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Likelihood based estimation of stochastic models when one of the explanatory variables is masked by measurement error, is presented. Special methods are required to estimate the parameters of a model with one or more explanatory variables that are measured with error. In such models, the variable measured with error is unobservable. Only an unbiased manifestation is observable. The method proposed, provides an adjustment to obtain unbiased estimates of model parameters. The correction of bias, however, is not possible without additional identifying information. An instrumental variable is a practical form of additional information that can be used for this purpose. By treating the unobservable explanatory variable as 'missing' data the Markov Chain Monte Carlo Expectation Maximization (MCEM) algorithm is applied for maximum likelihood estimation of the parameters of a measurement error model with identifying information in the form of an instrumental variable. Implementation strategies, computational aspects, behavior of the estimators and inference resulting from application of the MCEM algorithm to the instrumental variable measurement error model are studied. A general methodology is developed that encompasses a variety of previously studied special case models and it is shown how they all can be modeled and estimated using the MCEM algorithm. Through our method it is shown how a structural logistic regression measurement error model can be directly fitted without the probit approximation. This was not possible prior to the research presented in this dissertation. The proposed methodology is compared numerically with the exact maximum likelihood estimates for two normal family models. Also, the behavior of the method is investigated when one of the variance parameters is near the boundary of the parameter space. The problem of measurement error in a survival time model with right censoring is considered and it is shown how the proposed method can be used to estimate a hazard function model, by construction of some special likelihoods and further methodological development. Two methods have been proposed, one of which is a semi-parametric method and the other is full parametric.
Author : Philip J. Brown
Publisher : American Mathematical Soc.
ISBN 13 : 9780821854457
Total Pages : 264 pages
Book Rating : 4.8/5 (544 download)
Download or read book Statistical Analysis of Measurement Error Models and Applications written by Philip J. Brown and published by American Mathematical Soc.. This book was released on 1990-12-06 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Measurement error models describe functional relationships among variables observed, subject to random errors of measurement. Examples include linear and nonlinear errors-in-variables regression models, calibration and inverse regression models, factor analysis models, latent structure models, and simultaneous equations models. Such models are used in a wide variety of areas, including medicine, the life sciences, econometrics, chemometrics, geology, sample surveys, and time series. Although the problem of estimating the parameters of such models exists in most scientific fields, there is a need for more sources that treat measurement error models as an area of statistical methodology. This volume is designed to address that need. This book contains the proceedings of an AMS-IMS-SIAM Joint Summer Research Conference in the Mathematical Sciences on Statistical Analysis of Measurement Error Models and Applications. The conference was held at Humboldt State University in Arcata, California in June 1989. The papers in this volume fall into four broad groups. The first group treats general aspects of the measurement problem and features a discussion of the history of measurement error models. The second group focuses on inference for the nonlinear measurement error model, an active area of research which generated considerable interest at the conference. The third group of papers examines computational aspects of estimation, while the final set studies estimators possessing robustness properties against deviations from common model assumptions.
Author : Vicente J. Monleon-Moscardo
Publisher :
ISBN 13 :
Total Pages : 252 pages
Book Rating : 4.:/5 (635 download)
Download or read book Regression Calibration and Maximum Likelihood Inference for Measurement Error Models written by Vicente J. Monleon-Moscardo and published by . This book was released on 2005 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regression calibration inference seeks to estimate regression models with measurement error in explanatory variables by replacing the mismeasured variable by its conditional expectation, given a surrogate variable, in an estimation procedure that would have been used if the true variable were available. This study examines the effect of the uncertainty in the estimation of the required conditional expectation on inference about regression parameters, when the true explanatory variable and its surrogate are observed in a calibration dataset and related through a normal linear model. The exact sampling distribution of the regression calibration estimator is derived for normal linear regression when independent calibration data are available. The sampling distribution is skewed and its moments are not defined, but its median is the parameter of interest. It is shown that, when all random variables are normally distributed, the regression calibration estimator is equivalent to maximum likelihood provided a natural estimate of variance is non-negative. A check for this equivalence is useful in practice for judging the suitability of regression calibration. Results about relative efficiency are provided for both external and internal calibration data. In some cases maximum likelihood is substantially more efficient than regression calibration. In general, though, a more important concern when the necessary conditional expectation is uncertain, is that inferences based on approximate normality and estimated standard errors may be misleading. Bootstrap and likelihood-ratio inferences are preferable.
Author : Mary Margaret Dowling
Publisher :
ISBN 13 :
Total Pages : 232 pages
Book Rating : 4.E/5 ( download)
Download or read book Parameter Estimation in Nonlinear Regression with Covariate Measurement Error written by Mary Margaret Dowling and published by . This book was released on 1991 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Daniel W. Schafer
Publisher :
ISBN 13 :
Total Pages : 12 pages
Book Rating : 4.:/5 (13 download)
Download or read book Maximum Likelihood Estimates for Probit Regression with Measurement Errors written by Daniel W. Schafer and published by . This book was released on 1992 with total page 12 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Paul Gustafson
Publisher : CRC Press
ISBN 13 : 0203502760
Total Pages : 213 pages
Book Rating : 4.2/5 (35 download)
Download or read book Measurement Error and Misclassification in Statistics and Epidemiology written by Paul Gustafson and published by CRC Press. This book was released on 2003-09-25 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mismeasurement of explanatory variables is a common hazard when using statistical modeling techniques, and particularly so in fields such as biostatistics and epidemiology where perceived risk factors cannot always be measured accurately. With this perspective and a focus on both continuous and categorical variables, Measurement Error and Misclassi
Author : T. Fomby
Publisher : Elsevier
ISBN 13 : 0762310758
Total Pages : 266 pages
Book Rating : 4.7/5 (623 download)
Download or read book Maximum Likelihood Estimation of Misspecified Models written by T. Fomby and published by Elsevier. This book was released on 2003-12-12 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comparative study of pure and pretest estimators for a possibly misspecified two-way error component model / Badi H. Baltagi, Georges Bresson, Alain Pirotte -- Estimation, inference, and specification testing for possibly misspecified quantile regression / Tae-Hwan Kim, Halbert White -- Quasimaximum likelihood estimation with bounded symmetric errors / Douglas Miller, James Eales, Paul Preckel -- Consistent quasi-maximum likelihood estimation with limited information / Douglas Miller, Sang-Hak Lee -- An examination of the sign and volatility switching arch models under alternative distributional assumptions / Mohamed F. Omran, Florin Avram -- estimating a linear exponential density when the weighting matrix and mean parameter vector are functionally related / Chor-yiu Sin -- Testing in GMM models without truncation / Timothy J. Vogelsang -- Bayesian analysis of misspecified models with fixed effects / Tiemen Woutersen -- Tests of common deterministic trend slopes applied to quarterly global temperature data / Thomas B. Fomby, Timothy J. Vogelsang -- The sandwich estimate of variance / James W. Hardin -- Test statistics and critical values in selectivity models / R. Carter Hill, Lee C. Adkins, Keith A. Bender -- Introduction / Thomas B Fomby, R. Carter Hill.
Author : John P. Buonaccorsi
Publisher : CRC Press
ISBN 13 : 1420066587
Total Pages : 465 pages
Book Rating : 4.4/5 (2 download)
Download or read book Measurement Error written by John P. Buonaccorsi and published by CRC Press. This book was released on 2010-03-02 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: Over the last 20 years, comprehensive strategies for treating measurement error in complex models and accounting for the use of extra data to estimate measurement error parameters have emerged. Focusing on both established and novel approaches, Measurement Error: Models, Methods, and Applications provides an overview of the main techniques and illu
Author : Corrado Corradi
Publisher :
ISBN 13 :
Total Pages : 21 pages
Book Rating : 4.:/5 (848 download)
Download or read book A Note on the Computation of Maximum Likelihood Estimates in Linear Regression Models with Autocorrelated Errors written by Corrado Corradi and published by . This book was released on 1979 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Jerry A. Hausman
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (111 download)
Download or read book Errors in the Dependent Variable of Quantile Regression Models written by Jerry A. Hausman and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The popular quantile regression estimator of Koenker and Bassett (1978) is biased if there is an additive error term. Approaching this problem as an errors-in-variables problem where the dependent variable suffers from classical measurement error, we present a sieve maximum-likelihood approach that is robust to left-hand side measurement error. After providing sufficient conditions for identification, we demonstrate that when the number of knots in the quantile grid is chosen to grow at an adequate speed, the sieve maximum-likelihood estimator is consistent and asymptotically normal, permitting inference via bootstrapping. We verify our theoretical results with Monte Carlo simulations and illustrate our estimator with an application to the returns to education highlighting changes over time in the returns to education that have previously been masked by measurement-error bias.
Author : Paul P. Eggermont
Publisher : Springer Science & Business Media
ISBN 13 : 0387689028
Total Pages : 580 pages
Book Rating : 4.3/5 (876 download)
Download or read book Maximum Penalized Likelihood Estimation written by Paul P. Eggermont and published by Springer Science & Business Media. This book was released on 2009-06-02 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unique blend of asymptotic theory and small sample practice through simulation experiments and data analysis. Novel reproducing kernel Hilbert space methods for the analysis of smoothing splines and local polynomials. Leading to uniform error bounds and honest confidence bands for the mean function using smoothing splines Exhaustive exposition of algorithms, including the Kalman filter, for the computation of smoothing splines of arbitrary order.
Author : Raymond L. Chambers
Publisher : CRC Press
ISBN 13 : 1420011359
Total Pages : 374 pages
Book Rating : 4.4/5 (2 download)
Download or read book Maximum Likelihood Estimation for Sample Surveys written by Raymond L. Chambers and published by CRC Press. This book was released on 2012-05-02 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sample surveys provide data used by researchers in a large range of disciplines to analyze important relationships using well-established and widely used likelihood methods. The methods used to select samples often result in the sample differing in important ways from the target population and standard application of likelihood methods can lead to
