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A nonlinear structural errors-in-variables model is investigated, where the response variable has a density belonging to an exponential family and the error-prone covariate follows a Gaussian… (More)

We consider a Poisson model, where the mean depends on certain covariates in a log-linear way with unknown regression parameters. Some or all of the covariates are measured with errors. The… (More)

A polynomial functional relationship with errors in both variables can be consistently estimated by constructing an ordinary least squares estimator for the regression coefficients, assuming… (More)

We consider a polynomial regression model, where the covariate is measured with Gaussian errors. The measurement error variance is supposed to be known. The covariate is normally distributed with… (More)

We consider a regression of $y$ on $x$ given by a pair of mean and variance functions with a parameter vector $\theta$ to be estimated that also appears in the distribution of the regressor variable… (More)

In a polynomial regression with measurement errors in the covariate, the latter being supposed to be normally distributed, one has (at least) three ways to estimate the unknown regression parameters:… (More)

We compare two consistent estimators of the parameter vector beta of a general exponential family measurement error model with respect to their relative efficiency. The quasi score (QS) estimator… (More)

Using rounded data to estimate moments and regression coefficients typically biases the estimates. We explore the bias-inducing effects of rounding, thereby reviewing widely dispersed and often half… (More)

An adjusted least squares estimator, introduced by Cheng and Schneeweiss for consistently estimating a polynomial regression of any degree with errors in the variables, is modified such that it shows… (More)

- Hans Schneeweiss
- Multivariate behavioral research
- 1997

A sufficient condition in terms of the unique variances of a common factor model is given for the results of factor analysis to come close to those of principal component analysis. Principal… (More)