Add to ORKGAbstract: We consider the estimation problem of a logistic regression model. We assume the response observations and covariate values are both subject to measure-ment errors. We discuss some parametric and semiparametric estimation methods using mismeasured observations with validation data and derive their asypmtotic distributions. Our results are extentions of some well known results in the litera-ture. Comparisons of the asymptotic covariance matrices of the studied estimators are made, and some lower and upper bounds for the asymptotic relative eciencies are given to show the advantages of the semiparametric method. Some simulation results also show the method performs well. Key words and phrases: Kernel estimation, estimated likelihood...
This paper investigates the use of a pseudo-likelihood approach for inference in regression models w...
Neste trabalho estudamos o modelo de regressão logística com erro de medida nas covariáveis. Abordam...
Maximum likelihood estimation of regression parameters with incomplete covariate information usually...
In many fields of statistical application the fundamental task is to quantify the association betwee...
In many fields of statistical application the fundamental task is to quantify the association betwee...
Maximum likelihood estimation of regression parameters with incomplete covariate information usually...
Maximum likelihood estimation of regression parameters with incomplete covariate information usually...
In a logistic regression model, when the covariate is measured with error, the estimators of the reg...
Includes bibliographical references (p. 96-98).In a variety of regression applications, measurement ...
[[abstract]]Errors in measurement frequently occur in observing responses. If case–control data are ...
Vita.In many regression models one or more of the covariates are measured with error. It is well kno...
Vita.In many regression models one or more of the covariates are measured with error. It is well kno...
This paper investigates the use of a pseudo-likelihood approach for inference in regression models w...
Neste trabalho estudamos o modelo de regressão logística com erro de medida nas covariáveis. Abordam...
This paper investigates the use of a pseudo-likelihood approach for inference in regression models w...
This paper investigates the use of a pseudo-likelihood approach for inference in regression models w...
Neste trabalho estudamos o modelo de regressão logística com erro de medida nas covariáveis. Abordam...
Maximum likelihood estimation of regression parameters with incomplete covariate information usually...
In many fields of statistical application the fundamental task is to quantify the association betwee...
In many fields of statistical application the fundamental task is to quantify the association betwee...
Maximum likelihood estimation of regression parameters with incomplete covariate information usually...
Maximum likelihood estimation of regression parameters with incomplete covariate information usually...
In a logistic regression model, when the covariate is measured with error, the estimators of the reg...
Includes bibliographical references (p. 96-98).In a variety of regression applications, measurement ...
[[abstract]]Errors in measurement frequently occur in observing responses. If case–control data are ...
Vita.In many regression models one or more of the covariates are measured with error. It is well kno...
Vita.In many regression models one or more of the covariates are measured with error. It is well kno...
This paper investigates the use of a pseudo-likelihood approach for inference in regression models w...
Neste trabalho estudamos o modelo de regressão logística com erro de medida nas covariáveis. Abordam...
This paper investigates the use of a pseudo-likelihood approach for inference in regression models w...
This paper investigates the use of a pseudo-likelihood approach for inference in regression models w...
Neste trabalho estudamos o modelo de regressão logística com erro de medida nas covariáveis. Abordam...
Maximum likelihood estimation of regression parameters with incomplete covariate information usually...