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20052023
most citedOptimal discrimination designs

72 citations · 338 across the 55 of their papers we have counts for

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Showing 2016 · stat.MEShow all

6 papers · 2 filters

stat.ME2016

Optimal discrimination designs for semi-parametric models

Holger Dette, Roman Guchenko, Viatcheslav Melas +1

Much of the work in the literature on optimal discrimination designs assumes that the models of interest are fully specified, apart from unknown parameters in some models. Recent w…

stat.ME2016

Best linear unbiased estimators in continuous time regression models

Holger Dette, Andrey Pepelyshev, Anatoly Zhigljavsky

In this paper the problem of best linear unbiased estimation is investigated for continuous-time regression models. We prove several general statements concerning the explicit form…

stat.ME2016

Multiscale inference for multivariate deconvolution

Konstantin Eckle, Nicolai Bissantz, Holger Dette

In this paper we provide new methodology for inference of the geometric features of a multivariate density in deconvolution. Our approach is based on multiscale tests to detect sig…

stat.ME2016

Regularization parameter selection in indirect regression by residual based bootstrap

Nicolai Bissantz, Justin Chown, Holger Dette

Residual-based analysis is generally considered a cornerstone of statistical methodology. For a special case of indirect regression, we investigate the residual-based empirical dis…

stat.ME2016

Assessing the similarity of dose response and target doses in two non-overlapping subgroups

Frank Bretz, Kathrin Möllenhoff, Holger Dette +2

We consider two problems that are attracting increasing attention in clinical dose finding studies. First, we assess the similarity of two non-linear regression models for two non-…

stat.ME2016

Bayesian -optimal designs for error-in-variables models

Maria Konstantinou, Holger Dette

Bayesian optimality criteria provide a robust design strategy to parameter misspecification. We develop an approximate design theory for Bayesian -optimality for non-linear regr…