85 citations · 188 across the 7 of their papers we have counts for
7 papers
Characterizing M-estimators
Timo Dimitriadis, Tobias Fissler, Johanna Ziegel
We characterize the full classes of M-estimators for semiparametric models of general functionals by formally connecting the theory of consistent loss functions from forecast evalu…
Osband's Principle for Identification Functions
Timo Dimitriadis, Tobias Fissler, Johanna Ziegel
Given a statistical functional of interest such as the mean or median, a (strict) identification function is zero in expectation at (and only at) the true functional value. Identif…
Honest calibration assessment for binary outcome predictions
Timo Dimitriadis, Lutz Duembgen, Alexander Henzi +2
Probability predictions from binary regressions or machine learning methods ought to be calibrated: If an event is predicted to occur with probability , it should materialize wi…
A safe Hosmer-Lemeshow test
Alexander Henzi, Marius Puke, Timo Dimitriadis +1
This article proposes an alternative to the Hosmer-Lemeshow (HL) test for evaluating the calibration of probability forecasts for binary events. The approach is based on e-values,…
The Efficiency Gap
Timo Dimitriadis, Tobias Fissler, Johanna Ziegel
Parameter estimation via M- and Z-estimation is equally powerful in semiparametric models for one-dimensional functionals due to a one-to-one relation between corresponding loss an…
Evaluating probabilistic classifiers: Reliability diagrams and score decompositions revisited
Timo Dimitriadis, Tilmann Gneiting, Alexander I. Jordan
A probability forecast or probabilistic classifier is reliable or calibrated if the predicted probabilities are matched by ex post observed frequencies, as examined visually in rel…
Regression Based Expected Shortfall Backtesting
Sebastian Bayer, Timo Dimitriadis
This paper introduces novel backtests for the risk measure Expected Shortfall (ES) following the testing idea of Mincer and Zarnowitz (1969). Estimating a regression framework for…