Ursula U. Müller
Texas A&M University
56 Papers
257 Citations
Ursula U. Müller is an academic researcher from Texas A&M University. The author has contributed to research in topics: Estimator & Nonparametric regression. The author has an hindex of 16, co-authored 56 publications. Previous affiliations of Ursula U. Müller include University of Bremen.
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Papers
Estimating linear functionals in nonlinear regression with responses missing at random
TL;DR: In this paper, the authors consider regression models with parametric (linear or nonlinear) regression function and allow responses to be "missing at random" and assume that the errors have mean zero and are independent of the covariates.
Robust estimation for homoscedastic regression in the secondary analysis of case-control data
TL;DR: In this paper, the authors take up the problem of estimating a regression function when the covariates of interest are assumed to follow a homoscedastic regression model, but the distribution of the disease is unknown.
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Estimating linear functionals in nonlinear regression with responses missing at random
TL;DR: In this paper, the authors consider regression models with parametric (linear or nonlinear) regression function and allow responses to be ''missing at random'' and assume that the errors have mean zero and are independent of the covariates.
30
The transfer principle: A tool for complete case analysis
TL;DR: In this paper, a general method for deriving limiting distributions of complete case statistics for missing data models from corresponding results for the model where all data are observed is presented, which provides a convenient tool for obtaining the asymptotic behavior of established full data methods without lengthy proofs.
Estimating the innovation distribution in nonparametric autoregression
TL;DR: In this article, a Bahadur representation for a residual-based estimator of the innovation distribution function in a nonparametric autoregressive model is presented, where the residuals are based on a local linear smoother for the autoregression function.