Add to ORKGIn this paper we give reason to hope that errors in regression variables are not as harmful as one might expect. Specifically, we will show that although the errors can change the values of the quantities one computes in a regression analysis, under certain conditions they leave the distributions of the quantities approximately unchanged
Charles University in Prague Faculty of Mathematics and Physics ABSTRACT OF DOCTORAL THESIS Michal P...
Covariates of regression analysis are often measured with error in medical research. Indeed, many me...
A linear regression model, where covariates and a response are subject to errors, is considered in t...
AbstractLinear regression models are studied when variables of interest are observed in the presence...
AbstractIn this paper, to test goodness of fit to any fixed distribution of errors in multivariate l...
We present a survey of possible algorithms and their rounding off tranca tion, arithmetic error bou...
More than thirty years ago Halbert White inaugurated a “modelrobust” form of statistical inference b...
I consider the estimation of linear regression models when the independent variables are measured wi...
The problem of using information available from one variable X to make inferenceabout another Y is c...
Measurement error biases OLS results. When the measurement error variance in absolute or relative (r...
In many empirical applications of regression discontinuity designs, the running variable used by the...
The practical problem is not why specification errors are made but how to detect them. There are nu...
We consider a regression of y on x given by a pair of mean and variance functions with a parameter v...
There are over three decades of largely unrebutted criticism of regression analysis as practiced in ...
Type I error rates in multiple regression, and hence the chance for false positive research findings...
Charles University in Prague Faculty of Mathematics and Physics ABSTRACT OF DOCTORAL THESIS Michal P...
Covariates of regression analysis are often measured with error in medical research. Indeed, many me...
A linear regression model, where covariates and a response are subject to errors, is considered in t...
AbstractLinear regression models are studied when variables of interest are observed in the presence...
AbstractIn this paper, to test goodness of fit to any fixed distribution of errors in multivariate l...
We present a survey of possible algorithms and their rounding off tranca tion, arithmetic error bou...
More than thirty years ago Halbert White inaugurated a “modelrobust” form of statistical inference b...
I consider the estimation of linear regression models when the independent variables are measured wi...
The problem of using information available from one variable X to make inferenceabout another Y is c...
Measurement error biases OLS results. When the measurement error variance in absolute or relative (r...
In many empirical applications of regression discontinuity designs, the running variable used by the...
The practical problem is not why specification errors are made but how to detect them. There are nu...
We consider a regression of y on x given by a pair of mean and variance functions with a parameter v...
There are over three decades of largely unrebutted criticism of regression analysis as practiced in ...
Type I error rates in multiple regression, and hence the chance for false positive research findings...
Charles University in Prague Faculty of Mathematics and Physics ABSTRACT OF DOCTORAL THESIS Michal P...
Covariates of regression analysis are often measured with error in medical research. Indeed, many me...
A linear regression model, where covariates and a response are subject to errors, is considered in t...