Semi Parametric Estimation In The Excess Risk Model With Covaraite Measurement Error PDF Download
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Author | : M. L. Martin |
Publisher | : |
Total Pages | : 47 |
Release | : 2001 |
Genre | : |
ISBN | : |
Download Semi-parametric Estimation in the Excess Risk Model with Covaraite Measurement Error Book in PDF, ePub and Kindle
Author | : Marie-Laure Martin-Magniette |
Publisher | : |
Total Pages | : 34 |
Release | : 2006 |
Genre | : |
ISBN | : |
Download Semi-parametric Estimation of the Hazard Function in a Model with Covariate Measurement Error Book in PDF, ePub and Kindle
Author | : Ronald Keith Knickerbocker |
Publisher | : |
Total Pages | : 154 |
Release | : 1993 |
Genre | : |
ISBN | : |
Download Dimension Reduction in Semiparametric Measurement Error Models with Errors in Covariates Book in PDF, ePub and Kindle
Author | : Jungsywan H. Sepanski |
Publisher | : |
Total Pages | : 272 |
Release | : 1991 |
Genre | : Analysis of variance |
ISBN | : |
Download Semiparametric Quasilikelihood and Variance Function Estimation in Measurement Error Models Book in PDF, ePub and Kindle
Author | : Kirill S. Evdokimov |
Publisher | : |
Total Pages | : |
Release | : 2022 |
Genre | : |
ISBN | : |
Download Simple Estimation of Semiparametric Models with Measurement Errors Book in PDF, ePub and Kindle
We develop a practical way of addressing the Errors-In-Variables (EIV) problem in the Generalized Method of Moments (GMM) framework. We focus on the settings in which the variance of the measurement errors is a fraction of that of the mismeasured variables, which is typical for empirical applications. For any initial set of moment conditions our approach provides a “corrected” set of moment conditions that do not suffer from the EIV bias. The EIV-robust estimator is then computed as a standard GMM estimator with these corrected moment conditions. We show that our estimator is √n-consistent, and that the standard tests and confidence intervals provide valid inference. This is true even when the EIV are so large that the naive estimator (that ignores the EIV problem) may have a large bias with confidence intervals having 0% coverage. Our approach requires no nonparametric estimation, which can be particularly useful when the measurement errors are multivariate, serially correlated, or non-classical.
Author | : Hongyu Jiang |
Publisher | : |
Total Pages | : 116 |
Release | : 2000 |
Genre | : |
ISBN | : |
Download Semi-parametric Modeling of the Semi-competing Risks Problem Book in PDF, ePub and Kindle
Author | : Wenliang Lu |
Publisher | : |
Total Pages | : 158 |
Release | : 2000 |
Genre | : Covariate different method |
ISBN | : |
Download Semiparametric Estimation for Current Status Data with Flexible Covariate Effects Book in PDF, ePub and Kindle
Author | : Antara Majumdar |
Publisher | : |
Total Pages | : 194 |
Release | : 2007 |
Genre | : |
ISBN | : |
Download Maximum Likelihood Estimation of Measurement Error Models Based on the Monte Carlo EM Algorithm Book in PDF, ePub and Kindle
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 | : Glen T. Sueyoshi |
Publisher | : |
Total Pages | : 48 |
Release | : 1990 |
Genre | : Competing risks |
ISBN | : |
Download Semi-parametric Proportional Hazards Estimation of Competing Risks Models with Time-varying Covariates Book in PDF, ePub and Kindle
Author | : Eun-Young Suh |
Publisher | : |
Total Pages | : 202 |
Release | : 2001 |
Genre | : Error analysis (Mathematics) |
ISBN | : |
Download Semiparametric Maximum Likelihood for Regression with Measurement Error Book in PDF, ePub and Kindle
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.