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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 : Huyen D. Nguyen
Publisher :
ISBN 13 :
Total Pages : 64 pages
Book Rating : 4.4/5 (64 download)
Download or read book Parameter Estimation for the Logistic Regression Model with Errors in Covariate written by Huyen D. Nguyen and published by . This book was released on 2021 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a logistic regression model, when the covariate is measured with error, the estimators of the regression coefficient parameters can be biased. We propose a method for estimating parameters of a logistic regression with case-control data, when the covariate is subject to measurement error. The density of the covariate is estimated by using the deconvolution kernel density estimation. The parameters of the regression are estimated by the integrated squared distance based on the log ratio of the estimated density. We show the consistency and the asymptotic normality of the proposed estimators. Simulation study shows the superiority of the proposed method in different sample sizes and measurement error magnitudes scenario. The methodology is applied to estimating the relationship of systolic blood pressure and the presence of coronary heart disease.
Author : Philip J. Brown
Publisher : American Mathematical Soc.
ISBN 13 : 0821851179
Total Pages : 262 pages
Book Rating : 4.8/5 (218 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 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt: Measurement error models describe functional relationships among variables observed, subject to random errors of measurement. This book treats general aspects of the measurement problem and features a discussion of the history of measurement error models.
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 : 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 : Suyong Song
Publisher :
ISBN 13 : 9781124139869
Total Pages : 354 pages
Book Rating : 4.1/5 (398 download)
Download or read book Endogeneity and Measurement Error in Nonparametric and Semiparametric Models written by Suyong Song and published by . This book was released on 2010 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: It has long been an area of interest to consider a consistent estimation of nonlinear models with measurement error or endogeneity in the explanatory variables. Contrast to linear parametric models, both topics in nonlinear models are difficult to correct for. As a result, many of studies have addressed only one of them in nonlinear models, although controlling for only one mostly fails to identify economically meaningful structural parameters. Thus, this dissertation presents solutions to simultaneously control for both endogeneity and measurement error in general nonlinear regression models. Chapter one of this dissertation studies the identification and estimation of covariate-conditioned average marginal effects of endogenous regressors in nonseparable models when the regressors are mismeasured. Endogeneity is controlled for by making use of covariates as conditioning instruments; this ensures independence between the endogenous causes and other unobservable drivers of the dependent variable. Moreover, distributions of the underlying true causes from their error-laden measurements are recovered. Specifically, it is shown that two error-laden measurements of the unobserved true causes are sufficient to identify objects of interest and to deliver consistent estimators. Chapter two develops semiparametric estimation of models defined by conditional moment restrictions, where the unknown functions depend on endogenous variables which are contaminated by nonclassical measurement errors. A two-stage estimation procedure is proposed to recover the true conditional density of endogenous variables given conditioning variables masked by measurement errors, and to rectify the difficulty associated with endogeneity of the unknown functions. Chapter three investigates empirical importance of endogeneity and measurement error in economic examples. The proposed methods in chapter one and two are applied to topics of interest, the impact of family income on children's achievement and the estimation of Engel curves, respectively. The first application finds that the effects of family income on both math and reading scores from the proposed estimator are positive and that the magnitudes of the income effects are substantially larger than previously recognized. From the second application, findings indicate that correcting for both endogeneity and measurement error obtains significantly different shapes of Engel curves, compared to the method which ignores measurement error on total expenditure.
Author : Sandra Nolte
Publisher : LIT Verlag Münster
ISBN 13 : 3643900465
Total Pages : 162 pages
Book Rating : 4.6/5 (439 download)
Download or read book Measurement Error in Nonlinear Models written by Sandra Nolte and published by LIT Verlag Münster. This book was released on 2010 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book analyzes how the choice of a particular disclosure limitation method, namely additive and multiplicative measurement error, affects the quality of the data and limits its usefulness for empirical research. Generally, a disclosure limitation method can be regarded as a data filter that transforms the true data generating process. This book focuses explicitly on the consequences of additive and multiplicative measurement error for the properties of nonlinear econometric estimators. It investigates the extent to which appropriate econometric techniques can yield consistent and unbiased estimates of the true data generating process in the case of disclosure limitation. Sandra Nolte received her PhD in Economics at the University of Konstanz, Germany in 2008 and is a postdoctoral researcher at the Financial Econometric Research Centre at the Warwick Business School, UK since 2009. Her research areas include microeconometrics and financial econometrics.
Author : Xiaohong Chen
Publisher :
ISBN 13 :
Total Pages : 59 pages
Book Rating : 4.:/5 (129 download)
Download or read book Identification and Inference of Nonlinear Models Using Two Samples with Aribrary Measurement Errors written by Xiaohong Chen and published by . This book was released on 2006 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers identification and inference of a general latent nonlinear model using two samples, where a covariate contains arbitrary measurement errors in both samples, and neither sample contains an accurate measurement of the corresponding true variable. The primary sample consists of some dependent variables, some error-free covariates and an error-ridden covariate, where the measurement error has unknown distribution and could be arbitrarily correlated with the latent true values. The auxiliary sample consists of another noisy measurement of the mismeasured covariate and some error-free covariates. We first show that a general latent nonlinear model is nonparametrically identified using the two samples when both could have nonclassical errors, with no requirement of instrumental variables nor independence between the two samples. When the two samples are independent and the latent nonlinear model is parameterized, we propose sieve quasi maximum likelihood estimation (MLE) for the parameter of interest, and establish its root-n consistency and asymptotic normality under possible misspecification, and its semiparametric efficiency under correct specification. We also provide a sieve likelihood ratio model selection test to compare two possibly misspecified parametric latent models. A small Monte Carlo simulation and an empirical example are presented.
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 : Yonathan Bard
Publisher :
ISBN 13 :
Total Pages : 356 pages
Book Rating : 4.:/5 (319 download)
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.
Author : Lin Xue
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (135 download)
Download or read book Regularized Regression in Generalized Linear Measurement Error Models with Instrumental Variables -variable Selection and Parameter Estimation written by Lin Xue and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Regularization method is a commonly used technique in high dimensional data analysis. With properly chosen tuning parameter for certain penalty functions, the resulting estimator is consistent in both variable selection and parameter estimation. Most regularization methods assume that the data can be observed and precisely measured. However, it is well-known that the measurement error (ME) is ubiquitous in real-world datasets. In many situations some or all covariates cannot be observed directly or are measured with errors. For example, in cardiovascular disease related studies, the goal is to identify important risk factors such as blood pressure, cholesterol level and body mass index, which cannot be measured precisely. Instead, the corresponding proxies are employed for analysis. If the ME is ignored in regularized regression, the resulting naive estimator can have high selection and estimation bias. Accordingly, the important covariates are falsely dropped from the model and the redundant covariates are retained in the model incorrectly. We illustrate how ME affects the variable selection and parameter estimation through theoretical analysis and several numerical examples. To correct for the ME effects, we propose the instrumental variable assisted regularization method for linear and generalized linear models. We showed that the proposed estimator has the oracle property such that it is consistent in both variable selection and parameter estimation. The asymptotic distribution of the estimator is derived. In addition, we showed that the implementation of the proposed method is equivalent to the plug-in approach under linear models, and the asymptotic variance-covariance matrix has a compact form. Extensive simulation studies in linear, logistic and poisson log-linear regression showed that the proposed estimator outperforms the naive estimator in both linear and generalized linear models. Although the focus of this study is the classical ME, we also discussed the variable selection and estimation in the setting of Berkson ME. In particular, our finite sample simulation studies show that in contrast to the estimation in linear regression, the Berkson ME may cause bias in variable selection and estimation. Finally, the proposed method is applied to real datasets of diabetes and Framingham heart study.
Author : Wenxin Jiang
Publisher :
ISBN 13 :
Total Pages : 404 pages
Book Rating : 4.E/5 ( download)
Download or read book Aspects of Misspecification in Statistical Models written by Wenxin Jiang and published by . This book was released on 1996 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Kevin K. Anderson
Publisher :
ISBN 13 :
Total Pages : 13 pages
Book Rating : 4.:/5 (182 download)
Download or read book The Estimation of Parameters in Nonlinear, Implicit Measurement Error Models with Experiment-wide Measurements written by Kevin K. Anderson and published by . This book was released on 1994 with total page 13 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Dealing with Measurement Error in Covariates with Special Reference to Logistic Regression Model: a Flexible Parametric Approach written by and published by . This book was released on 2002 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In many fields of statistical application the fundamental task is to quantify the association between some explanatory variables or covariates and a response or outcome variable through a suitable regression model. The accuracy of such quantification depends on how precisely we measure the relevant covariates. In many instances, we can not measure some of the covariates accurately, rather we can measure noisy versions of them. In statistical terminology this is known as measurement errors or errors in variables. Regression analyses based on noisy covariate measurements lead to biased and inaccurate inference about the true underlying response-covariate associations. In this thesis we investigate some aspects of measurement error modelling in the case of binary logistic regression models. We suggest a flexible parametric approach for adjusting the measurement error bias while estimating the response-covariate relationship through logistic regression model. We investigate the performance of the proposed flexible parametric approach in comparison with the other flexible parametric and nonparametric approaches through extensive simulation studies. We also compare the proposed method with the other competitive methods with respect to a real-life data set. Though emphasis is put on the logistic regression model the proposed method is applicable to the other members of the generalized linear models, and other types of non-linear regression models too. Finally, we develop a new computational technique to approximate the large sample bias that my arise due to exposure model misspecification in the estimation of the regression parameters in a measurement error scenario.
Author : Sylvie Huet
Publisher : Springer Science & Business Media
ISBN 13 : 0387215743
Total Pages : 242 pages
Book Rating : 4.3/5 (872 download)
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 2006-04-18 with total page 242 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.
Author : Jouni Kuha
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (61 download)
Download or read book Simulation-based Estimation Methods for Regression Models with Covariate Measurement Error written by Jouni Kuha and published by . This book was released on 1995 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : David Birkes
Publisher : John Wiley & Sons
ISBN 13 : 1118150244
Total Pages : 248 pages
Book Rating : 4.1/5 (181 download)
Download or read book Alternative Methods of Regression written by David Birkes and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Of related interest. Nonlinear Regression Analysis and its Applications Douglas M. Bates and Donald G. Watts ".an extraordinary presentation of concepts and methods concerning the use and analysis of nonlinear regression models.highly recommend[ed].for anyone needing to use and/or understand issues concerning the analysis of nonlinear regression models." --Technometrics This book provides a balance between theory and practice supported by extensive displays of instructive geometrical constructs. Numerous in-depth case studies illustrate the use of nonlinear regression analysis--with all data sets real. Topics include: multi-response parameter estimation; models defined by systems of differential equations; and improved methods for presenting inferential results of nonlinear analysis. 1988 (0-471-81643-4) 365 pp. Nonlinear Regression G. A. F. Seber and C. J. Wild ".[a] comprehensive and scholarly work.impressively thorough with attention given to every aspect of the modeling process." --Short Book Reviews of the International Statistical Institute In this introduction to nonlinear modeling, the authors examine a wide range of estimation techniques including least squares, quasi-likelihood, and Bayesian methods, and discuss some of the problems associated with estimation. The book presents new and important material relating to the concept of curvature and its growing role in statistical inference. It also covers three useful classes of models --growth, compartmental, and multiphase --and emphasizes the limitations involved in fitting these models. Packed with examples and graphs, it offers statisticians, statistical consultants, and statistically oriented research scientists up-to-date access to their fields. 1989 (0-471-61760-1) 768 pp. Mathematical Programming in Statistics T. S. Arthanari and Yadolah Dodge "The authors have achieved their stated intention.in an outstanding and useful manner for both students and researchers.Contains a superb synthesis of references linked to the special topics and formulations by a succinct set of bibliographical notes.Should be in the hands of all system analysts and computer system architects." --Computing Reviews This unique book brings together most of the available results on applications of mathematical programming in statistics, and also develops the necessary statistical and programming theory and methods. 1981 (0-471-08073-X) 413 pp.
