Nonparametric Berkson regression under normal measurement error and bounded design
نویسندگان
چکیده
منابع مشابه
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...
متن کاملNonparametric Regression in the Presence of Measurement Error
In many regression applications the independent variable is measured with error. When this happens, conventional parametric and nonparametric regression techniques are no longer valid. We consider two different approaches to nonparametric regression. The first uses the SIMEX method and makes no assumption about the distribution of the unobserved error–prone predictor. For this approach we deriv...
متن کاملCorrection for covariate measurement error in nonparametric regression
Many areas of applied statistics have become aware of the problem of measurement error-prone variables and their appropriate analysis. Simply ignoring the error in the analysis usually leads to biased estimates, like e.g. in the regression with error-prone covariates. While this problem has been discussed at length for parametric regression, only few methods exist to handle nonparametric regres...
متن کاملEffect of Berkson measurement error on parameter estimates in Cox regression models.
We study the effect of additive and multiplicative Berkson measurement error in Cox proportional hazard model. By plotting the true and the observed survivor function and the true and the observed hazard function dependent on the exposure one can get ideas about the effect of this type of error on the estimation of the slope parameter corresponding to the variable measured with error. As an exa...
متن کاملMinimum Distance Regression Model Checking with Berkson Measurement Errors
Lack-of-fit testing of a regression model with Berkson measurement error has not been discussed in the literature to date. To fill this void, we propose a class of tests based on minimized integrated square distances between a nonparametric regression function estimator and the parametric model being fitted. We prove asymptotic normality of these test statistics under the null hypothesis and th...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 2010
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2009.10.010