Conditional density estimation with covariate measurement error

Polynomial regression with normal covariate measurement error

This paper derives the exact functional form of an error contaminated regression function when the error free regression is a polynomial function of error free covariates (discrete or continuous) which are contaminated by normally distributed measurement error, with coe¢cients which may be arbitrary functions of error free covariates. The form of higher order central moment error contaminated r...

Survival Analysis With Heterogeneous Covariate Measurement Error

This article is motivated by a time-to-event analysis where the covariate of interest was measured at the wrong time. We show that the problem can be formulated as a special case of survival analysis with heterogeneous covariate measurement error and develop a general analytic framework. We study the asymptotic behavior of the naive partial likelihood estimates and analytically demonstrate that...

Density Estimation with Normal Measurement Error with Unknown Variance

Abstract: This paper deals with the problem of estimating a density based on observations which are contaminated by a normally distributed error whose variance is unknown. In the case of a completely unknown error variance, the impossibility of a uniformly consistent estimation is shown; however, a semi-uniformly consistent estimator is constructed under nonparametric smoothness conditions on t...

Maximum likelihood estimation of generalized linear models with covariate measurement error

Generalized linear models with covariate measurement error can be estimated by maximum likelihood using gllamm, a program that fits a large class of multilevel latent variable models (Rabe-Hesketh, Skrondal, and Pickles 2004b). The program uses adaptive quadrature to evaluate the log-likelihood, producing more reliable results than many other methods (Rabe-Hesketh, Skrondal, and Pickles 2002). ...

Estimation Methods with Ordered Covariate Subject to Measurement Error and Missingness in Semi-Ecological Design