Inhaltsbereich
Statistische Methoden
Übersicht
Inferenz mit defizitären Daten
- Eva Endres, Paul Fink, and
Thomas Augustin.
Imprecise imputation: A
nonparametric micro approach reflecting the natural uncertainty of
statistical matching with categorical data.
Journal of Official Statistical, 35:599–624, 2019.
- Cornelia Fuetterer, Georg
Schollmeyer, and Thomas Augustin.
Constructing
simulation data with dependency structure for unreliable single-cell
rna-sequencing data using copulas.
In Jasper De Bock, Cassio P. de Campos,
Gert de Cooman, Erik Quaeghebeur, and
Gregory Wheeler, editors, Proceedings of the Eleventh
International Symposium on Imprecise Probabilities: Theories and
Applications, volume 103 of Proceedings of Machine Learning
Research, pages 216–224, Thagaste, Ghent, Belgium, 03–06 Jul 2019.
PMLR.
(PDF)
- Aziz Omar and Thomas
Augustin.
Estimation of
classification probabilities in small domains accounting for nonresponse
relying on imprecise probability.
International Journal of Approximate Reasoning, 115:134–143,
2019.
Substantially extended, invited special issue version of Omar & Augustin
(2018, SMPS).
- Julia Plass, Marco Cattaneo,
Thomas Augustin, Georg Schollmeyer, and
Christian Heumann.
Reliable inference in categorical
regression analysis for non-randomly coarsened observations.
International Statistical Review, 87:580–603, 2019.
- Georg Schollmeyer.
A short note on
the equivalence of the ontic and the epistemic view on data imprecision for
the case of stochastic dominance for interval-valued data.
In Jasper De Bock, Cassio P. de Campos,
Gert de Cooman, Erik Quaeghebeur, and
Gregory Wheeler, editors, Proceedings of the Eleventh
International Symposium on Imprecise Probabilities: Theories and
Applications, volume 103 of Proceedings of Machine Learning
Research, pages 330–337, Thagaste, Ghent, Belgium, 03–06 Jul 2019.
PMLR.
(PDF)
- Thomas Augustin.
Imprecise
sampling models for modelling unobserved heterogeneity? Basic ideas of a
credal likelihood concept.
In Davide Ciucci, Gabriella Pasi, and
Barbara Vantaggi, editors, Scalable Uncertainty
Management (Proceedings of the 12th International Conference, SUM 2018, Milan
(Italy), volume 11 of Lecture Notes in Artificial
Intelligence, pages 351–358. Springer, 2018.
- Eva Endres, Paul Fink, and
Thomas Augustin.
Imprecise imputation: A
nonparametric micro approach reflecting the natural uncertainty of
statistical matching with categorical data.
Technical Report 214, Department of Statistics, LMU Munich, 2018.
- Paul Fink.
Contributions to
reasoning on imprecise data: Imprecise classification trees, generalized
linear regression on microaggregated data and imprecise
imputation.
PhD thesis, Department of Statistics, LMU Munich, 2018.
- Aziz Omar and Thomas
Augustin.
Estimation
of classification probabilities in small domains accounting for nonresponse
relying on imprecise probability.
In Sebastien Destercke, Thierry Denoeux,
Maria Angeles Gil, Przemyslaw Grzegorzewski,
and Olgierd Hryniewicz, editors, SMPS 2018: Uncertainty
Modelling in Data Science, volume 832 of Advances in Intelligent
Systems and Computing, pages 175–182. Springer, 2018.
- Julia Plass.
Statistical modelling of
categorical data under ontic and epistemic imprecision: contributions to
power set based analyses, cautious likelihood inference and (non-)testability
of coarsening mechanism.
PhD thesis, Department of Statistics, LMU Munich, 2018.
- Paul
Fink and Thomas Augustin.
(Generalized) linear
regression on microaggregated data – from nuisance parameter optimization to
partial identification.
In Alessandro Antonucci, Giorgio Corani,
Inés Couso, and Sébastien Destercke,
editors, Proceedings of the Tenth International Symposium on Imprecise
Probability: Theories and Applications, volume 62 of Proceedings
of Machine Learning Research, pages 157–168. PMLR, 2017.
- Julia Plass, Marco Cattaneo,
Thomas Augustin, Georg Schollmeyer, and
Christian Heumann.
Towards a reliable categorical
regression analysis for non-randomly coarsened observations: An analysis with
German labour market data.
Technical Report 206, Department of Statistics, LMU Munich, 2017.
- Julia Plass, Marco Cattaneo,
Georg Schollmeyer, and Thomas Augustin.
On the testability of
coarsening assumptions: A hypothesis test for subgroup independence.
International Journal of Approximate Reasoning, 90:292–306,
2017.
- Julia Plass, Marco Cattaneo,
Georg Schollmeyer, and Thomas Augustin.
On the testability of
coarsening assumptions: A hypothesis test for subgroup independence.
Technical Report 201, Department of Statistics, LMU Munich, 2017.
- Julia Plass, Aziz Omar, and
Thomas Augustin.
Towards a cautious
modelling of missing data in small area estimation.
In Alessandro Antonucci, Giorgio Corani,
Inés Couso, and Sébastien Destercke,
editors, Proceedings of the Tenth International Symposium on Imprecise
Probability: Theories and Applications, volume 62 of Proceedings
of Machine Learning Research, pages 253–264. PMLR, 2017.
- Julia
Plass, Aziz Omar, and Thomas Augustin.
Towards a cautious
modelling of missing data in small area estimation.
In Alessandro Antonucci, Giorgio Corani,
Inés Couso, and Sébastien Destercke,
editors, Proceedings of the Tenth International Symposium on Imprecise
Probability: Theories and Applications, volume 62 of Proceedings
of Machine Learning Research, pages 253–264. PMLR, 2017.
- Georg Schollmeyer.
Reliable statistical
modeling of weakly structured information: contributions to partial
identification, stochastic partial ordering and imprecise
probabilities.
PhD thesis, Department of Statistics, LMU Munich, 2017.
- Julia Plass, Marco Cattaneo,
Georg Schollmeyer, and Thomas Augustin.
Testing of coarsening
mechanisms: Coarsening at random versus subgroup independence.
In Maria Brigida Ferraro, Paolo Giordani,
Barbara Vantaggi, Marek Gagolewski,
María Ángeles Gil, Przemysław
Grzegorzewski, and Olgierd Hryniewicz, editors, Soft
Methods for Data Science, pages 415–422. Springer, SMPS, 2016.
- Julia Plass, Thomas
Augustin, Marco Cattaneo, and Georg
Schollmeyer.
Statistical modelling
under epistemic data imprecision: Some results on estimating multinomial
distributions and logistic regression for coarse categorical data.
In Thomas Augustin, Serena Doria,
Enrique Miranda, and Erik Quaeghebeur, editors,
ISIPTA '15, Proceedings of the Ninth International Symposium on
Imprecise Probability: Theories and Applications, pages 247–256,
Rome, 2015. Aracne.
- Julia Plass, Paul Fink,
Norbert Schöning, and Thomas Augustin.
Statistical modelling
in surveys without neglecting the undecided: Multinomial logistic
regression models and imprecise classification trees under ontic data
imprecision.
In Thomas Augustin, Serena Doria,
Enrique Miranda, and Erik Quaeghebeur, editors,
ISIPTA '15, Proceedings of the Ninth International Symposium on
Imprecise Probability: Theories and Applications, pages 257–266,
Rome, 2015. Aracne.
- Julia Plaß, Paul Fink,
Norbert Schöning, and Thomas Augustin.
Statistical modelling in surveys without neglecting ``the undecided'':
Multinomial logistic regression models and imprecise classification trees
under ontic data imprecision - extended version.
Technical Report 179, Department of Statistics, LMU Munich, 2015.
- Georg Schollmeyer and Thomas
Augustin.
Statistical modeling under
partial identification: Distinguishing three types of identification
regions in regression analysis with interval data.
International Journal of Approximate Reasoning, 56, Part
B:224–248, 2015.
- Marco Cattaneo and Andrea
Wiencierz.
On the implementation of
LIR: the case of simple linear regression with interval data.
Computational Statistics, 29(3–4):743–767, 2014.
- Julia
Plaß.
Coarse categorical data under epistemic and ontologic uncertainty: Comparison
and extension of some approaches.
Master's thesis, LMU Munich, 2013.
- Georg Schollmeyer and Thomas
Augustin.
On
sharp identification regions for regression under interval data.
In Fabio Cozman, Therry Denœux,
Sébastien Destercke, and Teddy Seidfenfeld,
editors, ISIPTA '13, Proceedings of the Eighth International Symposium
on Imprecise Probability: Theories and Applications, pages 285–294,
Manno, 2013. SIPTA.
- Andrea
Wiencierz.
Regression analysis with
imprecise data.
PhD thesis, Department of Statistics, LMU Munich, 2013.
- Marco Cattaneo and Andrea
Wiencierz.
Likelihood-based imprecise
regression.
International Journal of Approximate Reasoning, 53(8):1137–1154,
2012.
- Helmut Küchenhoff, Thomas
Augustin, and Anne Kunz.
Partially identified prevalence estimation under misclassification using the
Kappa coefficient.
International Journal of Approximate Reasoning, 53(8):1168–1182,
2012.
- Andrea Wiencierz and Marco
Cattaneo.
An exact algorithm for
likelihood-based imprecise regression in the case of simple linear regression
with interval data.
In Rudolf Kruse, Michael Berthold,
Christian Moewes, Marıa Ángeles Gil,
Przemysław Grzegorzewski, and Olgierd
Hryniewicz, editors, Synergies of Soft Computing and Statistics for
Intelligent Data Analysis, volume 190 of Advances in
Intelligent Systems and Computing, pages 293–301. Springer,
2012.
- Marco Cattaneo and Andrea
Wiencierz.
Regression
with imprecise data: A robust approach.
In Frank Coolen, Gert de Cooman,
Thomas Fetz, and Michael Oberguggenberger,
editors, ISIPTA '11, Proceedings of the Seventh International
Symposium on Imprecise Probability: Theories and Applications, pages
119–128, Manno, 2011. SIPTA.
- Marco Cattaneo and Andrea
Wiencierz.
Robust regression with
imprecise data.
Technical Report 114, Department of Statistics, LMU Munich, 2011.
- Helmut Küchenhoff, Thomas
Augustin, and Anne Kunz.
Partially
identified prevalence estimation under misclassification using the Kappa
coefficient.
In Frank Coolen, Gert de Cooman,
Thomas Fetz, and Michael Oberguggenberger,
editors, ISIPTA '11, Proceedings of the Seventh International
Symposium on Imprecise Probability: Theories and Applications, pages
237–246, Manno, 2011. SIPTA.
- Thomas Augustin and Robert
Hable.
On the impact of
robust statistics on imprecise probability models: a review.
Structural Safety, 32(6):358–365, 2010.
- David Rummel, Thomas
Augustin, and Helmut Küchenhoff.
Correction for
covariate measurement error in nonparametric longitudinal regression.
Biometrics, 66(4):1209–1219, 2010.
- Thomas Augustin, Angela
Döring, and David Rummel.
Regression calibration
for Cox regression under heteroscedastic measurement error —
Determining risk factors of cardiovascular diseases from error-prone
nutritional replication data.
In Christian Heumann and Shalabh, editors,
Recent Advances in Linear Models and Related Areas, Essays in Honour
of Helge Toutenburg, pages 253–278. Physika Verlag, Heidelberg,
2008.
- Lev Utkin and Thomas
Augustin.
Decision making under
imperfect measurement using the imprecise Dirichlet model.
International Journal of Approximate Reasoning, 44(3):322–338,
2007.
- Hans Schneeweiß and Thomas
Augustin.
Some recent advances in
measurement error models and methods.
Allgemeines Statistisches Archiv, 90(1):183–197, 2006.
Reprinted in: O. Hübner and J. Frohn (2006, eds.): Modern Econometric
Analysis — Surveys on Recent Developments. Springer. Heidelberg.
- Ralf Bender, Thomas Augustin,
and Maria Blettner.
Generating survival times to
simulate Cox proportional hazards models.
Statistics in Medicine, 24(11):1713–1723, 2005.
- Thomas
Augustin.
An exact corrected
log-likelihood function for Cox's proportional hazards model under
measurement error and some extensions.
Scandinavian Journal of Statistics, 31(1):43–50, 2004.
- Thomas Augustin and Joachim
Wolff.
A bias analysis of Weibull
models under heaped data.
Statistical Papers, 45(2):211–229, 2004.
- Anneke Neuhaus, Martin
Daumer, Ludwig Kappos, Thomas Augustin, and
Helmut Küchenhoff.
Modelling time to progression in multiple sclerosis regarding the error in the
response variable.
Multiple Sclerosis, 10(7032, Suppl. 2):99, 2004.
- Joachim Wolff and Thomas
Augustin.
Heaping and its consequences for duration analysis: a simulation study.
Allgemeines Statistisches Archiv, 87(1):59–86, 2003.
- Thomas
Augustin.
Some basic results on the extension of quasi-likelihood based measurement error
correction to multivariate and flexible structural models.
In Wolfgang Gaul and Gunter Ritter, editors,
Classification, Automation, and New Media (Passau, 2000),
pages 29–36. Springer, Berlin, 2002.
- Thomas Augustin.
Survival Analysis under Measurement Error.
Post-doctoral Thesis. Department of Statistics, University of Munich (LMU) (172
pages), 2002.
- Thomas Augustin and Regina
Schwarz.
Cox's proportional hazards model under covariate measurement error. A
review and comparison of methods.
In Sabine Van Huffel and Philippe Lemmerling,
editors, Total Least Squares and Errors-in-Variables Modeling (Leuven,
2001), pages 175–184. Kluwer Acad. Publ., Dordrecht, 2002.
- Thomas
Augustin.
Quasi-likelihood based correction for measurement error in accelerated failure
time models.
In Herwig Friedl, Andrea Berghold, and
Göran Kauermann, editors, Statistical Modelling.
Proceedings of the 14th International Workshop on Statistical
Modelling, pages 421–424. l, 1999.
nach oben
Relationale Methoden in der statistischen Datenanalyse
- Georg Schollmeyer.
A short note on
the equivalence of the ontic and the epistemic view on data imprecision for
the case of stochastic dominance for interval-valued data.
In Jasper De Bock, Cassio P. de Campos,
Gert de Cooman, Erik Quaeghebeur, and
Gregory Wheeler, editors, Proceedings of the Eleventh
International Symposium on Imprecise Probabilities: Theories and
Applications, volume 103 of Proceedings of Machine Learning
Research, pages 330–337, Thagaste, Ghent, Belgium, 03–06 Jul 2019.
PMLR.
(PDF)
- Christoph Jansen, Georg
Schollmeyer, and Thomas Augustin.
Concepts for decision
making under severe uncertainty with partial ordinal and partial cardinal
preferences.
International Journal of Approximate Reasoning, 98:112–131, 2018.
Substantially extended, invited special issue version of Jansen, Schollmeyer &
Augustin (2017, ISIPTA).
- Christoph Jansen, Georg
Schollmeyer, and Thomas Augustin.
Concepts for decision
making under severe uncertainty with partial ordinal and partial cardinal
preferences.
In Alessandro Antonucci, Giorgio Corani,
Inés Couso, and Sébastien Destercke,
editors, Proceedings of the Tenth International Symposium on Imprecise
Probability: Theories and Applications, volume 62 of Proceedings
of Machine Learning Research, pages 181–192. PMLR, 2017.
- Georg
Schollmeyer.
Application of lower quantiles
for complete lattices to ranking data: analyzing outlyingness of preference
orderings.
Technical Report 208, Department of Statistics, LMU, 2017.
- Georg
Schollmeyer.
Lower quantiles for complete
lattices.
Technical Report 207, Department of Statistics, LMU, 2017.
- Georg Schollmeyer.
Reliable statistical
modeling of weakly structured information: contributions to partial
identification, stochastic partial ordering and imprecise
probabilities.
PhD thesis, Department of Statistics, LMU Munich, 2017.
- Georg Schollmeyer, Christoph
Jansen, and Thomas Augustin.
Detecting stochastic dominance
for poset-valued random variables as an example of linear programming on
closure systems.
Technical Report 209, Department of Statistics, LMU, 2017.
- Georg Schollmeyer, Christoph
Jansen, and Thomas Augustin.
A simple descriptive method
for multidimensional item response theory based on stochastic dominance.
Technical Report 210, Department of Statistics, LMU, 2017.
nach oben
Probabilistische Grafische Modelle
- Eva Endres and Thomas
Augustin.
Utilizing log-linear Markov
networks to integrate categorical data files.
Technical Report 222, Department of Statistics, LMU Munich, 2019.
- Eva Endres, Katrin Newger,
and Thomas Augustin.
Binary data fusion using
undirected probabilistic graphical models: Combining statistical matching
and the Ising model.
Technical Report 223, Department of Statistics, LMU Munich, 2019.
- Eva Endres and Thomas
Augustin.
Statistical
matching of discrete data by Bayesian networks.
In Alessandro Antonucci, Giorgio Corani, and
Cassio Polpo de Campos, editors, Journal of Machine
Learning Research Workshop and Conference Proceedings (Proceedings of the
Eighth International Conference on Probabilistic Graphical Models),
volume 52, pages 159–170, 2016.
- Eva Endres and Thomas
Augustin.
Probabilistic
graphical models for statistical matching.
Poster presentation, 2015.
ISIPTA '15: Ninth International Symposium on Imprecise Probability: Theories
and Applications.
- Alessandro Antonucci, Marco
Cattaneo, and Giorgio Corani.
Likelihood-based robust
classification with Bayesian networks.
In Salvatore Greco, Bernadette Bouchon-Meunier,
Giulianella Coletti, Mario Fedrizzi,
Benedetto Matarazzo, and Ronald Yager, editors,
Advances in Computational Intelligence, Part 3, volume 299 of
Communications in Computer and Information Science, pages
491–500. Springer, 2012.
- Alessandro Antonucci, Marco
Cattaneo, and Giorgio Corani.
Likelihood-based
naive credal classifier.
In Frank Coolen, Gert de Cooman,
Thomas Fetz, and Michael Oberguggenberger,
editors, ISIPTA '11, Proceedings of the Seventh International
Symposium on Imprecise Probability: Theories and Applications, pages
21–30, Manno, 2011. SIPTA.
- Thomas Augustin, Frank
Coolen, Matthias Troffaes, and Serafín
Moral, editors.
Special issue
on imprecise probability in statistical inference and decision making.
International Journal of Approximate Reasoning, 51 (9),
1011–1172, 2010.
- Marco
Cattaneo.
Likelihood-based
inference for probabilistic graphical models: Some preliminary results.
In Petri Myllymäki, Teemu Roos, and
Tommi Jaakkola, editors, PGM 2010, Proceedings of The
Fifth European Workshop on Probabilistic Graphical Models, pages
57–64. HIIT Publications, 2010.
- Thomas Augustin, Frank
Coolen, Matthias Troffaes, and Serafín
Moral, editors.
Proceedings of
ISIPTA '09. Fifth International Symposium on Imprecise Probabilities and
Their Applications, Manno, 2009. SIPTA.
- Thomas Augustin, Enrique
Miranda, and Jirina Vejnarova, editors.
Special issue
on imprecise probability models and their applications. International
Journal of Approximate Reasoning, 50 (4), 581–694,
2009.
- Marco
Cattaneo.
A generalization
of credal networks.
In Thomas Augustin, Frank Coolen,
Serafín Moral, and Matthias Troffaes,
editors, ISIPTA '09, Proceedings of the Sixth International Symposium
on Imprecise Probability: Theories and Applications, pages 79–88,
Manno, 2009. SIPTA.
- Marco
Cattaneo.
Probabilistic-possibilistic
belief networks.
Technical Report 32, Department of Statistics, LMU Munich, 2008.
nach oben
Rekursive Partitionierung
- Luisa
Ebner, Malte Nalenz, Annette ten Teije,
Frank van Harmelen, and Thomas Augustin.
Expert rulefit: Complementing rule ensembles with expert knowledge.
In 19th International Conference on Artificial Intelligence in Medicine,
KR4HC Workshop, 2021.
Currently unavailable under the original address. Instead available under:
https://github.com/maltenlz/Malte-Nalenz/blob/main/ERF.pdf.
- Malte Nalenz and Thomas
Augustin.
Cultivated random forests: Robust decision tree learning through tree
structured ensembles.
2021.
Technical Report. Available under:
https://epub.ub.uni-muenchen.de/77861.
- Paul Fink.
Contributions to
reasoning on imprecise data: Imprecise classification trees, generalized
linear regression on microaggregated data and imprecise
imputation.
PhD thesis, Department of Statistics, LMU Munich, 2018.
- Paul
Fink.
imptree:
Classification Trees with Imprecise Probabilities, 2018.
R package version 0.5.1.
- Malte Nalenz and Mattias
Villani.
Tree ensembles with rule structured horseshoe regularization.
Annals of Applied Statistics, 12(4):2379–2408, 2018.
- Julia Plass, Paul Fink,
Norbert Schöning, and Thomas Augustin.
Statistical modelling
in surveys without neglecting the undecided: Multinomial logistic
regression models and imprecise classification trees under ontic data
imprecision.
In Thomas Augustin, Serena Doria,
Enrique Miranda, and Erik Quaeghebeur, editors,
ISIPTA '15, Proceedings of the Ninth International Symposium on
Imprecise Probability: Theories and Applications, pages 257–266,
Rome, 2015. Aracne.
- Julia Plaß, Paul Fink,
Norbert Schöning, and Thomas Augustin.
Statistical modelling in surveys without neglecting ``the undecided'':
Multinomial logistic regression models and imprecise classification trees
under ontic data imprecision - extended version.
Technical Report 179, Department of Statistics, LMU Munich, 2015.
- Paul
Fink and Richard Crossman.
Entropy
based classification trees.
In Fabio Cozman, Therry Denœux,
Sébastien Destercke, and Teddy Seidfenfeld,
editors, ISIPTA '13, Proceedings of the Eighth International Symposium
on Imprecise Probability: Theories and Applications, pages 139–147,
Manno, 2013. SIPTA.
- Julia Kopf.
Model-based recursive
partitioning meets item response theory: New statistical methods for the
detection of differential item functioning and appropriate anchor
selection.
PhD thesis, Department of Statistics, LMU Munich, 2013.
- Julia
Kopf, Thomas Augustin, and Carolin Strobl.
The potential of model-based recursive partitioning in the social sciences —
Revisiting Ockham's Razor.
In John McArdle and Gilbert Ritschard, editors,
Contemporary Issues in Exploratory Data Mining, pages 75–95.
Routledge, New York, 2013.
- Paul Fink.
Ensemble methods for classification trees under imprecise probabilities.
Master's thesis, LMU Munich, 2012.
- Richard Crossman, Joaquín
Abellán, Thomas Augustin, and Frank
Coolen.
Building
imprecise classification trees with entropy ranges.
In Frank Coolen, Gert de Cooman,
Thomas Fetz, and Michael Oberguggenberger,
editors, ISIPTA '11, Proceedings of the Seventh International
Symposium on Imprecise Probability: Theories and Applications, pages
129–138, Manno, 2011. SIPTA.
- Julia
Kopf, Thomas Augustin, and Carolin Strobl.
The potential of model-based
recursive partitioning in the social sciences — Revisiting Ockham's
Razor.
Technical Report 88, Department of Statistics, LMU Munich, 2010.
- Carolin Strobl, Julia Kopf,
and Achim Zeileis.
A new method for detecting differential item functioning in the Rasch model.
Technical Report 92, Department of Statistics, LMU Munich, 2010.
- Carolin Strobl, Julia Kopf,
and Achim Zeileis.
Wissen Frauen weniger oder nur das Falsche? Ein statistisches Modell
für unterschiedliche Aufgaben-Schwierigkeiten in Teilstichproben.
In Sabine Trepte and Markus Verbeet, editors,
Wissenswelten des 21. Jahrhunderts - Erkenntnisse aus dem
Studentenpisa-Test des SPIEGEL, pages 255–272. VS Verlag, Wiesbaden,
2010.
- Carolin Strobl and Thomas
Augustin.
Adaptive selection of extra cutpoints — an approach towards reconciling
robustness and interpretability in classification trees.
Journal of Statistical Theory and Practice, 3:119–135, 2009.
- Anne-Laure Boulesteix, Carolin
Strobl, Thomas Augustin, and Martin Daumer.
Evaluating microarray–based classifiers: an overview.
Cancer Informatics, 6:77–97, 2008.
- Carolin Strobl, Anne-Laure
Boulesteix, Thomas Kneib, Thomas Augustin, and
Achim Zeileis.
Conditional variable
importance for random forests.
BMC Bioinformatics, 9:307–317, 2008.
- Carolin Strobl, Anne-Laure
Boulesteix, and Thomas Augustin.
Unbiased split selection
for classification trees based on the Gini-index.
Computational Statistics and Data Analysis, 52(1):483–501,
2007.
nach oben
Lebensdaueranalyse
- Matthias Schmid, Thomas
Hielscher, Thomas Augustin, and Olaf Gefeller.
A robust alternative
to the Schemper-Henderson measure of prediction error.
Biometrics, 67(2):524–535, 2011.
- Anneke Neuhaus, Thomas
Augustin, Christian Heumann, and Martin
Daumer.
A review on joint models in biometrical research.
Journal of Statistical Theory and Practice, 3:855–868, 2009.
- Ralf Bender, Thomas Augustin,
and Maria Blettner.
Letter to the editor: Generating
survival times to simulate Cox proportional hazards models — by Ralf
Bender, Thomas Augustin and Maria Blettner, Statistics in
Medicine 2005; 24 : 1713–1723.
Statistics in Medicine, 25(11):1978–1979, 2006.
- Ralf Bender, Thomas Augustin,
and Maria Blettner.
Generating survival times to
simulate Cox proportional hazards models.
Statistics in Medicine, 24(11):1713–1723, 2005.
- Thomas
Augustin.
An exact corrected
log-likelihood function for Cox's proportional hazards model under
measurement error and some extensions.
Scandinavian Journal of Statistics, 31(1):43–50, 2004.
- Thomas Augustin and Joachim
Wolff.
A bias analysis of Weibull
models under heaped data.
Statistical Papers, 45(2):211–229, 2004.
- Anneke Neuhaus, Martin
Daumer, Ludwig Kappos, Thomas Augustin, and
Helmut Küchenhoff.
Modelling time to progression in multiple sclerosis regarding the error in the
response variable.
Multiple Sclerosis, 10(7032, Suppl. 2):99, 2004.
- Joachim Wolff and Thomas
Augustin.
Heaping and its consequences for duration analysis: a simulation study.
Allgemeines Statistisches Archiv, 87(1):59–86, 2003.
- Thomas
Augustin.
Survival Analysis under Measurement Error.
Post-doctoral Thesis. Department of Statistics, University of Munich (LMU) (172
pages), 2002.
- Thomas Augustin and Regina
Schwarz.
Cox's proportional hazards model under covariate measurement error. A
review and comparison of methods.
In Sabine Van Huffel and Philippe Lemmerling,
editors, Total Least Squares and Errors-in-Variables Modeling (Leuven,
2001), pages 175–184. Kluwer Acad. Publ., Dordrecht, 2002.
nach oben
Statistisches Matching
- Eva Endres and Thomas
Augustin.
Utilizing log-linear Markov
networks to integrate categorical data files.
Technical Report 222, Department of Statistics, LMU Munich, 2019.
- Eva Endres, Paul Fink, and
Thomas Augustin.
Imprecise imputation: A
nonparametric micro approach reflecting the natural uncertainty of
statistical matching with categorical data.
Journal of Official Statistical, 35:599–624, 2019.
- Eva Endres, Katrin Newger,
and Thomas Augustin.
Binary data fusion using
undirected probabilistic graphical models: Combining statistical matching
and the Ising model.
Technical Report 223, Department of Statistics, LMU Munich, 2019.
- Eva Endres, Paul Fink, and
Thomas Augustin.
Imprecise imputation: A
nonparametric micro approach reflecting the natural uncertainty of
statistical matching with categorical data.
Technical Report 214, Department of Statistics, LMU Munich, 2018.
- Paul Fink.
Contributions to
reasoning on imprecise data: Imprecise classification trees, generalized
linear regression on microaggregated data and imprecise
imputation.
PhD thesis, Department of Statistics, LMU Munich, 2018.
- Paul Fink and Eva Endres.
impimp: Imprecise
Imputation for Statistical Matching, 2018.
R package version 0.3.0.
- Eva Endres, Paul Fink, and
Thomas Augustin.
Imprecise
imputation for statistical matching.
Poster presentation, 2017.
ISIPTA '17: Tenth International Symposium on Imprecise Probability: Theories
and Applications.
- Eva Endres and Thomas
Augustin.
Statistical
matching of discrete data by Bayesian networks.
In Alessandro Antonucci, Giorgio Corani, and
Cassio Polpo de Campos, editors, Journal of Machine
Learning Research Workshop and Conference Proceedings (Proceedings of the
Eighth International Conference on Probabilistic Graphical Models),
volume 52, pages 159–170, 2016.
- Eva Endres and Thomas
Augustin.
Probabilistic
graphical models for statistical matching.
Poster presentation, 2015.
ISIPTA '15: Ninth International Symposium on Imprecise Probability: Theories
and Applications.
nach oben
Likelihood Inferenz
- Thomas Augustin.
Imprecise
sampling models for modelling unobserved heterogeneity? Basic ideas of a
credal likelihood concept.
In Davide Ciucci, Gabriella Pasi, and
Barbara Vantaggi, editors, Scalable Uncertainty
Management (Proceedings of the 12th International Conference, SUM 2018, Milan
(Italy), volume 11 of Lecture Notes in Artificial
Intelligence, pages 351–358. Springer, 2018.
- Julia Plass.
Statistical modelling of
categorical data under ontic and epistemic imprecision: contributions to
power set based analyses, cautious likelihood inference and (non-)testability
of coarsening mechanism.
PhD thesis, Department of Statistics, LMU Munich, 2018.
- Julia Plass, Marco Cattaneo,
Thomas Augustin, Georg Schollmeyer, and
Christian Heumann.
Towards a reliable categorical
regression analysis for non-randomly coarsened observations: An analysis with
German labour market data.
Technical Report 206, Department of Statistics, LMU Munich, 2017.
- Julia Plass, Marco Cattaneo,
Georg Schollmeyer, and Thomas Augustin.
On the testability of
coarsening assumptions: A hypothesis test for subgroup independence.
International Journal of Approximate Reasoning, 90:292–306,
2017.
- Julia Plass, Marco Cattaneo,
Georg Schollmeyer, and Thomas Augustin.
On the testability of
coarsening assumptions: A hypothesis test for subgroup independence.
Technical Report 201, Department of Statistics, LMU Munich, 2017.
- Julia Plass, Marco Cattaneo,
Georg Schollmeyer, and Thomas Augustin.
Testing of coarsening
mechanisms: Coarsening at random versus subgroup independence.
In Maria Brigida Ferraro, Paolo Giordani,
Barbara Vantaggi, Marek Gagolewski,
María Ángeles Gil, Przemysław
Grzegorzewski, and Olgierd Hryniewicz, editors, Soft
Methods for Data Science, pages 415–422. Springer, SMPS, 2016.
- Julia Plass, Thomas Augustin,
Marco Cattaneo, and Georg Schollmeyer.
Statistical modelling
under epistemic data imprecision: Some results on estimating multinomial
distributions and logistic regression for coarse categorical data.
In Thomas Augustin, Serena Doria,
Enrique Miranda, and Erik Quaeghebeur, editors,
ISIPTA '15, Proceedings of the Ninth International Symposium on
Imprecise Probability: Theories and Applications, pages 247–256,
Rome, 2015. Aracne.
- Marco
Cattaneo.
A continuous updating
rule for imprecise probabilities.
In Anne Laurent, Olivier Strauss,
Bernadette Bouchon-Meunier, and Ronald Yager,
editors, Information Processing and Management of Uncertainty in
Knowledge-Based Systems, Part 3, volume 444 of Communications
in Computer and Information Science, pages 426–435. Springer,
2014.
- Marco Cattaneo and Andrea
Wiencierz.
On the implementation of
LIR: the case of simple linear regression with interval data.
Computational Statistics, 29(3–4):743–767, 2014.
- Marco
Cattaneo.
Likelihood decision functions.
Electronic Journal of Statistics, 7:2924–2946, 2013.
- Andrea
Wiencierz.
Regression analysis with
imprecise data.
PhD thesis, Department of Statistics, LMU Munich, 2013.
- Marco Cattaneo and Andrea
Wiencierz.
Likelihood-based imprecise
regression.
International Journal of Approximate Reasoning, 53(8):1137–1154,
2012.
- Andrea Wiencierz and Marco
Cattaneo.
An exact algorithm for
likelihood-based imprecise regression in the case of simple linear regression
with interval data.
In Rudolf Kruse, Michael Berthold,
Christian Moewes, Marıa Ángeles Gil,
Przemysław Grzegorzewski, and Olgierd
Hryniewicz, editors, Synergies of Soft Computing and Statistics for
Intelligent Data Analysis, volume 190 of Advances in
Intelligent Systems and Computing, pages 293–301. Springer,
2012.
- Marco Cattaneo and Andrea
Wiencierz.
Regression
with imprecise data: A robust approach.
In Frank Coolen, Gert de Cooman,
Thomas Fetz, and Michael Oberguggenberger,
editors, ISIPTA '11, Proceedings of the Seventh International
Symposium on Imprecise Probability: Theories and Applications, pages
119–128, Manno, 2011. SIPTA.
- Marco Cattaneo and Andrea
Wiencierz.
Robust regression with
imprecise data.
Technical Report 114, Department of Statistics, LMU Munich, 2011.
- Andrea Wiencierz, Sonja
Greven, and Helmut Küchenhoff.
Restricted likelihood ratio testing
in linear mixed models with general error covariance structure.
Electronic Journal of Statistics, 5:1718–1734, 2011.
- Marco
Cattaneo.
Likelihood-based
inference for probabilistic graphical models: Some preliminary results.
In Petri Myllymäki, Teemu Roos, and
Tommi Jaakkola, editors, PGM 2010, Proceedings of The
Fifth European Workshop on Probabilistic Graphical Models, pages
57–64. HIIT Publications, 2010.
nach oben
Item-Response Theorie
- Georg Schollmeyer, Christoph
Jansen, and Thomas Augustin.
Detecting stochastic dominance
for poset-valued random variables as an example of linear programming on
closure systems.
Technical Report 209, Department of Statistics, LMU, 2017.
- Georg Schollmeyer, Christoph
Jansen, and Thomas Augustin.
A simple descriptive method
for multidimensional item response theory based on stochastic dominance.
Technical Report 210, Department of Statistics, LMU, 2017.
- Julia Kopf.
Model-based recursive
partitioning meets item response theory: New statistical methods for the
detection of differential item functioning and appropriate anchor
selection.
PhD thesis, Department of Statistics, LMU Munich, 2013.
- Carolin Strobl, Julia Kopf,
and Achim Zeileis.
A new method for detecting differential item functioning in the Rasch model.
Technical Report 92, Department of Statistics, LMU Munich, 2010.
- Carolin Strobl, Julia Kopf,
and Achim Zeileis.
Wissen Frauen weniger oder nur das Falsche? Ein statistisches Modell
für unterschiedliche Aufgaben-Schwierigkeiten in Teilstichproben.
In Sabine Trepte and Markus Verbeet, editors,
Wissenswelten des 21. Jahrhunderts - Erkenntnisse aus dem
Studentenpisa-Test des SPIEGEL, pages 255–272. VS Verlag, Wiesbaden,
2010.
