Bayesian Linear Regression (2009)
Walter, Gero, Augustin, Thomas
The paper is concerned with Bayesian analysis under prior-data conflict, i.e. the situation when observed data are rather unexpected under the prior (and the sample size is not large enough to...
Conditional variable importance for random forests (2008)
Strobl, Carolin, Boulesteix, Anne-Laure, Kneib, Thomas, Augustin, Thomas, Zeileis, Achim
Abstract Background Random forests are becoming increasingly popular in many scientific fields because they can cope with "small n large p" problems, complex interactions and even highly correlated...
Conditional Variable Importance for Random Forests (2008)
Strobl, Carolin, Boulesteix, Anne-Laure, Kneib, Thomas, Augustin, Thomas, Zeileis, Achim
Random forests are becoming increasingly popular in many scientific fields because they can cope with ``small n large p'' problems, complex interactions and even highly correlated predictor...
Empirical evaluation of the near-miss-to-Weber’s law: a visual discrimination experiment (2008)
THOMAS AUGUSTIN, TANJA ROSCHER
Many pure tone intensity discrimination data support the hypothesis that the sensitivity function grows as a power function of the stimulus intensity (near-miss-to-Weber's law). In order to test...
Correcting for Measurement Error in Parametric Duration Models By Quasi-Likelihood (2007)
In regression models for duration data it is usually implicitly assumed that all variables are measured and operationalized exactly. If measurement error is present, however, but not taken into...
Evaluating microarray-based classifiers: an overview (2007)
Boulesteix, Anne-Laure, Strobl, Carolin, Augustin, Thomas, Daumer, Martin
For the last eight years, microarray-based class prediction has been the subject of numerous publications in medicine, bioinformatics and statistics journals. However, in many articles, the...
Bayesian Learning for a Class of Priors with Prescribed Marginals (2006)
Held, Hermann, Kriegler, Elmar, Augustin, Thomas
We present Bayesian updating of an imprecise probability measure, represented by a class of precise multidimensional probability measures. Choice and analysis of our class are motivated by expert...
Coolen, F. P. A., Augustin, Thomas
Nonparametric Predictive Inference (NPI) is a general methodology to learn from data in the absence of prior knowledge and without adding unjustified assumptions. This paper develops NPI for...
A Review on Joint Models in Biometrical Research (2006)
Neuhaus, Anneke, Augustin, Thomas, Heumann, Christian, Daumer, Martin
In some fields of biometrical research joint modelling of longitudinal measures and event time data has become very popular. This article reviews the work in that area of recent fruitful research by...
Bayesian Learning for a Class of Priors with (2006)
Prescribed Marginals, Hermann Held, Elmar Kriegler, Thomas Augustin
We present Bayesian updating of an imprecise probability measure, represented by a class of precise multidimensional probability measures. Choice and analysis of our class are motivated by expert...
Unbiased split selection for classification trees based on the Gini Index (2006)
Carolin Strobl, Anne-laure Boulesteix, Thomas Augustin
The Gini gain is one of the most common variable selection criteria in machine learning. We derive the exact distribution of the maximally selected Gini gain in the context of binary classification...
Unbiased split selection for classification trees based on the Gini Index (2006)
Carolin Strobl, Anne-laure Boulesteix, Thomas Augustin
Unbiased split selection for classification trees based
Hans Schneeweiß, Thomas Augustin
A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in...
Some Recent Advances in Measurement Error Models and Methods (2005)
Schneeweiß, Hans, Augustin, Thomas
A measurement error model is a regression model with (substantial) measurement errors in the variables. Disregarding these measurement errors in estimating the regression parameters results in...
Unbiased split selection for classification trees based on the Gini Index (2005)
Strobl, Carolin, Boulesteix, Anne-Laure, Augustin, Thomas
The Gini gain is one of the most common variable selection criteria in machine learning. We derive the exact distribution of the maximally selected Gini gain in the context of binary classification...
On weighted local fitting and its relation to the Horvitz-Thompson estimator (2005)
Einbeck, Jochen, Augustin, Thomas
Weighting is a largely used concept in many fields of statistics and has frequently caused controversies on its justification and profit. In this paper, we analyze a weighted version of the...
Carolin Strobl, Anne-laure Boulesteix, Thomas Augustin, Gini Gain, Gini Gain, Variable Selection
bias
Generating Survival Times to Simulate Cox Proportional Hazards Models (2003)
Bender, R., Augustin, Thomas, Blettner, Maria
This paper discusses techniques to generate survival times for simulation studies regarding Cox proportional hazards models. In linear regression models, the response variable is directly connected...
Augustin, Thomas, Döring, A., Rummel, D.
For instance nutritional data are often subject to severe measurement error, and an adequate adjustment of the estimators is indispensable to avoid deceptive conclusions. This paper discusses and...
On the Symbiosis of Two Concepts of Conditional Interval Probability (2003)
Kurt Weichselberger, Thomas Augustin
This paper argues in favor of the thesis that two different concepts of conditional interval probability are needed, in order to serve the huge variety of tasks conditional probability has in the...
This paper discusses fundamental aspects of inference with imprecise probabilities from the decision theoretic point of view. It is shown why the equivalence of prior risk and posterior loss, well...
Der Meßprozeß in der Psychologie wird durch Störgrößen beeinflußt, die während eines Experiments auf die Versuchsperson einwirken. So ist es möglich, daß eine Versuchsperson, die zu einem...
Der Meßprozeß in der Psychologie wird durch Störgrößen beeinflußt, die während eines Experiments auf die Versuchsperson einwirken. So ist es möglich, daß eine Versuchsperson, die zu einem...
Generalized basic probability assignments (2002)
Dempster-Shafer theory allows to construct belief functions from (precise) basic probability assignments. The present paper extends this idea substantially. By considering SETS of basic probability...
This paper studies Cox`s proportional hazards model under covariate measurement error. Nakamura`s (1990) methodology of corrected log-likelihood will be applied to the so called Breslow likelihood,...
Regensburg, Universiẗat, Diss., 2002.
Nonparametric predictive inference and interval probability (2001)
Augustin, Thomas, Coolen, F.P.A.
This paper presents the unique position of A(n)-based nonparametric predictive inference within the theory of interval probability. It provides a completely new understanding, leading to powerful new...
This contribution studies the Cox model under covariate measurement error. Methods proposed in the literature to adjust for measurement error are reviewed. The basic structural and functional...
On Robust Sequential Analysis - Kiefer-Weiss Optimal Testing under Interval Probability (2001)
Augustin, Thomas, Pöhlmann, S.
Usual sequential testing procedures often are very sensitive against even small deviations from the `ideal model' underlying the hypotheses. This makes robust procedures highly desirable. To rely on...
Thomas Augustin, Sigrid Pohlmann
Usual sequential testing procedures often are very sensitive against even small deviations from the `ideal model ' underlying the hypotheses. This makes robust procedures highly desirable. To...
Quasi-score equations derived from corrected mean and variance functions allow for consistent parameter estimation under measurement error. However, the practical use of some approaches relying on...
Heaping and its Consequences for Duration Analysis (2000)
This paper analyses the consequences of heaping in duration models. Heaping is a specific form of response error typical to retrospectively collected labor force status data. Respondents round-off...
Correcting for measurement error in parametric duration models by quasi-likelihood (1999)
In regression models for duration data it is usually implicitly assumed that all variables are measured and operationalized exactly. If measurement error is present, however, but not taken into...
The paper studies the extension of one of the basic issues of classical statistics to interval probability. It is concerned with the Generalized Neyman-Pearson problem, i.e. an alternative testing...
Thesis (doctoral)--Rheinische Friedrich-Wilhelms-Universität zu Bonn, 1978.
Conditional variable importance for random forests
Strobl, Carolin, Boulesteix, Anne-Laure, Kneib, Thomas, Augustin, Thomas, Zeileis, Achim
Stevens' Direct Scaling Methods and the Uniqueness Problem
Direct scaling methods, ratio estimation, ratio production, uniqueness problem, scale types, axiomatic measurement theory,
The problem of meaningfulness: Weber's law, Guilford's power law, and the near-miss-to-Weber's law
The present paper provides reformulations of common models of discrimination, like for instance, Weber's law, Guilford's power law, and the near-miss-to-Weber's law. All models are based on the...
Ambiguity, Interval probability, Decision Making, Expected Utility, Choquet Expected Utility, Imprecise Probabilities, Capacities, Lower and Upper Probabilities, Non-additive Measures, Belief...