Results 41 to 50 of about 78 (50)
An Information Inequality for the Bayes Risk Under Truncated Squared Error Loss [PDF]
, 1993 A bound is given for the Bayes risk of an estimator under truncated squared error loss. The bound derives from an information inequality for the risk under this loss.
Brown, Lawrence D
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Erratum: Higher order elicitability and Osband's principle. [PDF]
, 2019 This note corrects conditions in Proposition 3.4 and Theorem 5.2(ii) and comments on imprecisions in Propositions 4.2 and 4.4 in Fissler and Ziegel (2016)
Fissler, T, Ziegel, J
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A new example for a proper scoring rule
, 2020 We give a new example for a proper scoring rule motivated by the form of Anderson--Darling distance of distribution functions and Example 5 in Brehmer and Gneiting (2020).Comment: 8 ...
Barczy, Matyas
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Sparse Estimators and the Oracle Property, or the Return of Hodges’ Estimator [PDF]
, 2005 We point out some pitfalls related to the concept of an oracle property as used in Fan and Li (2001, 2002, 2004) which are reminiscent of the well-known pitfalls related to Hodges’ estimator.
Leeb, Hannes, Pötscher, Benedikt M.
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Sparse Estimators and the Oracle Property, or the Return of Hodges' Estimator [PDF]
We point out some pitfalls related to the concept of an oracle property as used in Fan and Li (2001, 2002, 2004) which are reminiscent of the well-known pitfalls related to Hodges’ estimator.
Benedikt M. Poetscher, Hannes Leeb
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Sequential multiple hypothesis testing in presence of control variables [PDF]
, 2008 Suppose that at any stage of a statistical experiment a control variable $X$ that affects the distribution of the observed data $Y$ at this stage can be used. The distribution of $Y$ depends on some unknown parameter $\theta$, and we consider the problem
Novikov, Andrey
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High-dimensional Inference for Dynamic Treatment Effects
, 2022 This paper proposes a confidence interval construction for heterogeneous treatment effects in the context of multi-stage experiments with $N$ samples and high-dimensional, $d$, confounders.
Bradic, Jelena +2 more
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Regularisation for the approximation of functions by mollified discretisation methods
Some prominent discretisation methods such as finite elements provide a way to approximate a function of $d$ variables from $n$ values it takes on the nodes $x_i$ of the corresponding mesh.
Hoffmann, Marc, Pouchol, Camille
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