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Sta­tistik mit Anwendungen im Be­reich der Ingenieur­wissen­schaften

Publikationen Christine Müller


Monographien / Books

 

Eingereichte Arbeiten / Submitted Papers

 

Angenommene Arbeiten / Accepted Papers

2022

2021

  • Malevich, N., Müller, C.H., Dreier, J., Kansteiner, M., Biermann, D., Ferreira, M. and Tillmann, W. (2021). Experimental and statistical analysis of the wear of diamond impregnated tools. Wear, Volumes 468-469, Article 203574, doi.org/10.1016/j.wear.2020.203574 .
  • Müller, C.H., Dohme, H., Malcherczyk, D., Biermann, D., and Tillmann, W. (2021+). Detecting diamond breakouts of diamond impregnated tools for core drilling of concrete by force measurements. Erscheint in: Artificial Intelligence, Big Data,Data Science and Machine Learning in Statistics. Eds. K.-L. Tsui, A. Steland, Springer. Preprint.
  • Malcherczyk, D.A., Leckey, K., and Müller, C.H. (2021). K-sign depth: From asymptotics to efficient implementation. Journal of Statistical Planning and Inference 215, 344-355.
  • Heinrich, J., Maurer, R., Leckey, K., Müller, C.H., and Ickstadt, I. (2021).  Detektieren ermüdungsbedingter Spannstahlbrüche mittels Rissmonitoring im Versuch und am Bauwerk. Bauingenieur, 96, 92-101.

2020

  • Leckey, K., Müller, C.H., Szugat, S., and  Maurer, R. (2020). Prediction intervals for load sharing systems in accelerated life testing. Quality and Reliability Engineering International, 36,1895-1915. doi.org/10.1002/qre.2664
  • Kirchho ff, D., Kuhnt, S., Bloch, L., and Müller, C.H. (2020). Detection of circlelike overlapping objects in thermal spray images. Quality and Reliability Engineering International, 36, 2639-2659. doi.org/10.1002/qre.2689  

2019

2018

  • Kansteiner, M., Biermann, D., Malevich, N., Horn, M., Müller, C., Ferreira, M., Tillmann, W. (2018). Analysis of the wear behaviour of diamond impregnated tools used for the core drilling of concrete with statistical lifetime prediction. In: Proceedings of Euro PM2018, Europe's annual powder metallurgy congress and exhibition, Bilbao, 14 - 18 October 2018.
  • Müller, C.H., and Meinke, S.H. (2018). Trimmed likelihood estimators for stochastic differential equations with an application to crack growth analysis from photos.  Archives of Data Science, Series A (Online First), 3 (1), doi:10.5445/KSP/1000083488/01
  • Hermann, S., Ickstadt, K. and Müller, Ch.H. (2018). Bayesian prediction for a jump di ffusion process with application to crack growth in fatigue experiments. Reliability Engineering and System Safety, 179, 83–96. DOI:10.1016/j.ress.2016.08.012 . Preprint: PDF-File.

2017

  • Clarke, B.R., Höller, A., Müller, C.H., Wamahiu, K. (2017). Investigation of the performance of trimmed estimators of life time distributions with censoring.  Australian & New Zealand Journal of Statistics 59, 513–525. Preprint: PDF-File.
  • Kansteiner, M., Biermann, D., Dagge, M., Müller, C., Ferreira, M., Tillmann, W. (2017). Statistical evaluation of the wear behaviour of diamond impregnated tools used for the core drilling of concrete. In: Proceedings of Euro PM2017, Europe's annual powder metallurgy congress and exhibition, Milan, 1st - 5th October 2017.
  • Heinrich, J., Maurer, R., Hermann, S., Ickstadt, K., Müller, C. (2017). Resistance of Prestressed Concrete Structures to Fatigue in Domain of Endurance Limit. In: Proceedings of fib Symposium Maastricht, High Tech Concrete: Where Technology and Engineering Meet, 12th - 14th June 2017.
  • Szugat, S., Bakhtin, I., Fechtel, L., Hüsch, M., Riehl, J., Tegethoff, C., Müller, C.H. (2017). Bedingungen für hohe Publikationsraten von Ländern in hochrangigen internationalen Sta­tistik-Fachzeitschriften. AStA Wirtschafts- und Sozialstatistisches Archiv 11, 33-49, DOI:10.1007/s11943-017-0201-0

2016

 2014

2013

2012

2011

2010

2009

2008

  • Katina, St., Wellmann, R. and Müller, Ch.H. (2008). Simplicial depth estimators and tests in examples from shape analysis. Tatra Mt. Math. Publ. 39, 95–104. Preprint: PDF-File

2007

  • Hillebrand, M. and Ch.H. Müller (2007). Outlier robust corner-preserving methods for reconstructing noisy images. Ann. Statist. 35, 132-165. Preprint: PDF-File
  • Garlipp, T. and Müller, Ch.H. (2007). Robust jump detection in regression surface. Sankhya 69, 55-86. Preprint: PDF-File
  • Wellmann, R., Katina, St. and Müller, Ch.H. (2007). Calculation of simplicial depth estimators for polynomial regression with applications. Computational Statistics and Data Analysis 51, 5025-5040. Preprint: PDF-File
  • Fathy, Y. and Müller, Ch.H. (2007). Bayes estimators of covariance parameters and the influence of designs. In: mODa 8 - Advances in Model-Oriented Design and Analysis. Eds. J. López-Fidalgo, J. M. Rodríguez-Díaz, and B. Torsney, Physica-Verlag, Heidelberg, 49-56. Preprint: PDF-File

2006

  • Garlipp, T. and Müller, Ch.H. (2006). Detection of linear and circular shapes in image analysis. Computational Statistics and Data Analysis 51, 1479-1490. Preprint: PDF-File
  • Hillebrand, M. and Ch.H. Müller (2006). On consistency of redescending M-kernel smoothers. Metrika 63, 71 - 90. Preprint: PDF-File

2005

  • Ch.H. Müller (2005). Depth estimators and tests based on the likelihood principle with application to regression. Journal of Multivariate Analysis 95, 153-181. Preprint: PDF-File
  • Müller, Ch.H. and Garlipp, T. (2005). Simple consistent cluster methods based on redescending M-estimators with an application to edge identification in images. Journal of Multivariate Analysis 92, 359-385. Preprint: PDF-File
  • Garlipp, T. and Müller, Ch.H. (2005). Regression clustering with redescending M-estimators. In: Innovations in Classification, Data Science, and Information Systems, eds. D. Baier, K.-D. Wernecke, Springer-Verlag, Heidelberg, 38-45. PDF-File

2004

  • Mizera, I. and Ch.H. Müller (2004). Location-scale depth. Journal of the American Statistical Association 99, 949-966. With discussion. Preprints: PDF-File, PDF-File of Rejoinder
  • Ch.H. Müller (2004). Redescending M-estimators in regression analysis, cluster analysis and image analysis. Discussiones Mathematicae - Probability and Statistics 24, 59-75. Preprint: PDF-File
  • Ch.H. Müller and Kitsos, C.P. (2004). Optimal design criteria based on tolerance regions. In: mODa 7 - Advances in Model-Oriented Design and Analysis, eds. A. Di Bucchianico, H. Läuter, H.P. Wynn, Physica-Verlag, Heidelberg, 107-115. Preprint: PDF-File

2003

  • Müller, Ch.H. and Neykov, N. (2003). Breakdown points of trimmed likelihood estimators and related estimators in generalized linear models. J. Statist. Plann. Inference. 116, 503-519. Preprint: PDF-File
  • Müller, Ch.H. (2003). Robust estimators for estimating discontinuous functions. In: Developments in Robust Statistics, eds. R. Dutter, P. Filzmoser, U. Gather, P.J. Rousseeuw , Physica-Verlag, Heidelberg, 266-276. Preprint: PDF-File
  • Neykov, N. and Müller, Ch.H. (2003). Breakdown point and computation of trimmed likelihood estimators in generalized linear models. In: Developments in Robust Statistics, eds. R. Dutter, P. Filzmoser, U. Gather, P.J. Rousseeuw , Physica-Verlag, Heidelberg, 277-286. Preprint: PDF-File

2002

  • Müller, Ch.H. (2002). Robust estimators for estimating discontinuous functions. Metrika 55, 99-109. Preprint: PDF-File
  • Müller, Ch.H. (2002). Comparison of high breakdown point estimators for image denoising. Allg. Stat. Archiv 86, 307-321.
  • Mizera, I. and Müller, Ch.H. (2002). Breakdown points of Cauchy regression-scale estimators. Statist. & Probab. Letters 57, 79-89.

2001

  • Bednarski, T. and Müller, Ch.H. (2001). Optimal bounded influence regression and scale M-estimators. Statistics 35, 349-369. Preprint: PDF-File
  • Müller, Ch.H. and Uhlig, St. (2001). Estimation of variance components with high breakdown point and high efficiency. Biometrika 88, 353-366.
  • Mizera, I. and Müller, Ch.H. (2001). The influence of the design on the breakdown point of l1-type M-estimators. In: MODA 6 - Advances in Model-Oriented Design and Analysis, eds. A.C. Atkinson, P. Hackl, W.G. Müller, Physica-Verlag, Heidelberg, 193-200.
  • Müller, Ch.H. (2001). Trimmed likelihood estimators in generalized linear models. In: Proceedings of the Sixth International Conference on Computer Data Analysis and Modeling, eds. S. Aivazian, Y. Kharin, H. Rieder, Minsk, Vol.2, 142-150. Preprint: PDF-File

2000

  • Müller, Ch.H. (2000). Asymptotic normality and efficiency of variance components estimators with high breakdown points. Discussiones Mathematicae - Algebra and Stochastic Methods 20, 85-95.

1999

  • Mizera, I. and Müller, Ch.H. (1999). Breakdown points and variation exponents of robust M-estimators in linear models. Ann. Statist.27, 1164-1177.
  • Herwig, R., Poustka, A.J., Müller, Ch.H., Bull, Ch., Lehrach, H. and O'Brien, J. (1999). Large-scale clustering of cDNA-fingerprinting data. Genome Research 9, 1093-1105.
  • Müller, Ch.H. (1999). On the use of high breakdown point estimators in the image analysis. Tatra Mountains Math. Publ. 17, 283-293.
  • Müller, Ch.H. (1999). Bayesian designs versus maximin efficient designs for nonlinear problems. In Proceedings of the 52nd Session of the International Statistical Institute, Tome LVIII, Bulletin of the International Statistical Institute, Edita Ltd, Helsinki, 145-148.

1998

  • Müller, Ch.H. (1998). Optimum robust testing in linear models. Ann. Statist. 26, 1126-1146.
  • Müller, Ch.H. and Pázman, A. (1998). Applications of necessary and sufficient conditions for maximin efficient designs. Metrika, 48, 1-19.
  • Müller, Ch.H. (1998). Breakdown points of estimators for aspects of linear models. In MODA 5 - Advances in Model-Oriented Data Analysis and Experimental Design, eds. A.C. Atkinson, L. Pronzato, H.P. Wynn, Physica-Verlag, Heidelberg, 137-144.

1997

  • Müller, Ch.H. (1997). L1-tests in linear models: Tests with maximum relative power. In L1-Statistical Procedures and Related Topics, ed. Y. Dodge, IMS Lecture Notes - Monograph Series 31, Hayward, 91-99.
  • Müller, Ch.H. (1997). Robust inference and experimental design for multi-factor models. In: Industrial Statistics. Aims and Computational Aspects, eds. C.P. Kitsos, L. Edler, Physica-Verlag, Heidelberg, 165-174.

1996

  • Müller, Ch.H. (1996). Diskussionsbeitrag zu den Aufsätzen von Plummer und Clayton sowie von Festing und Lovell. J. Roy. Statist. Soc. Ser. B 58, 148.
  • Müller, Ch.H. (1996). Optimal breakdown point maximizing designs. Tatra Mountains Math. Publ. 7, 79-85.
  • Müller, Ch.H. (1996). High breakdown point designs. In Robust Statistics, Data Analysis, and Computer Intensive Methods - In Honor of Peter Huber's 60th Birthday, ed. H. Rieder. Lecture Notes in Statistics 109, Springer, New York, 353-360.
  • Müller, Ch.H. (1996). Computing high breakdown point estimators for planned experiments and for models with qualitative factors. In COMPSTAT'96, Proceedings in Computational Statistics, ed. A. Prat, Physica-Verlag, 379-384.

1995

  • Müller, Ch.H. (1995). Maximin efficient designs for estimating nonlinear aspects in linear models. J. Statist. Plann. Inference. 44, 117-132.
  • Müller, Ch.H. (1995). Breakdown points for designed experiments. J. Statist. Plann. Inference. 45, 413-427.
  • Müller, Ch.H. (1995). Optimal breakdown point maximizing designs. Metrika42, 244-245.
  • Kitsos, C.P. and Müller, Ch.H. (1995). Robust linear calibration. Statistics27, 93-106.
  • Kitsos, C.P. and Müller, Ch.H. (1995). Robust estimation of non-linear aspects. In MODA 4 - Advances in Model-Oriented Data Analysis, eds. C.P.Kitsos, W.G. Müller, Physica-Verlag, Heidelberg, 223-233.

1994

  • Müller, Ch.H. (1994). Optimal designs for robust estimation in conditionally contaminated linear models. J. Statist. Plann. Inference. 38, 125-140.
  • Müller, Ch.H. (1994). One-step-M-estimators in conditionally contaminated linear models. Stat. Decis. 12, 331-342.
  • Müller, Ch.H. (1994). Asymptotic behaviour of one-step-M-estimators in contaminated non-linear models. In Asymptotic Statistics, eds. P. Mandl, M. Hušková, Physica-Verlag, Heidelberg, 395-404.
  • Müller, Ch.H. (1994). On the calculation of MSE minimizing robust estimators. In COMPSTAT'94, Proceedings in Computational Statistics, eds. R. Dutter, W. Grossmann, Physica-Verlag, Heidelberg, 257-262.
  • Müller, Ch.H. (1994). Optimal bias bounds for robust estimation in linear models. In Proceedings of the International Conference on Linear Statistical Inference LINSTAT'93, eds. T. Calinski, R. Kala, Kluwer Academic Publishers, Dordrecht, 97-102.

1993

  • Müller, Ch.H. (1993). Behaviour of asymptotically optimal designs for robust estimation at finite sample sizes. In Model-Oriented Data Analysis, eds. W.G. Müller, H.P. Wynn, A.A. Zhigljavsky, Physica-Verlag, Heidelberg, 53-62.

1992

  • Kurotschka, V. and Müller, Ch.H. (1992). Optimum robust estimation of linear aspects in conditionally contaminated linear models. Ann. Statist. 20, 331-350.
  • Müller, Ch.H. (1992). L1-estimation and testing in conditionally contaminated linear models. In L1-Statistical Analysis and Related Methods, ed. Y. Dodge, North-Holland, Amsterdam, 69-76.
  • Müller, Ch.H. (1992). Robust estimation with minimum bias and A-optimal designs. In PROBASTAT'91, Proceedings of the International Conference on Probability and Mathematical Statistics, eds. A. Pázman, J. Volaufová, 109-115.

 

 

Öffentlich zugängliche Arbeiten / Publicly Available Papers

2022

2021

 

Buchbesprechungen / Book Reviews

  • Scholz, Ch.H. (1980). Buchbesprechung: Spalt, D.D. (1979). Was ist und was soll die Mathematische Biologie. Wissenschaftliche Buchgesellschaft Darmstadt. Willdenowia 10, 126.
  • Müller, Ch.H. (1997). Buchbesprechung: Morgenthaler, St. and Tukey, J.W. (1991). Configural Polysampling: A Route to Practical Robustness. John Wiley, New York. Statistics.
  • Müller, Ch.H. (1998). Buchbesprechung: Handbook of Statistics 13. Design and Analysis of Experiments. Eds. S. Ghosh, C.R. Rao. North-Holland, Amsterdam. Mathematical Reviews.
  • Müller, Ch.H. (2000). Buchbesprechung: Dodge, Y. and Jurecková (2000). Adaptive Regression. Springer. Metrika.
  • Müller, Ch.H. (2007). Buchbesprechung: Lee, Y., Nelder, J.A. and Pawitan, Y. (2006). Chapman & Hall/CRC. Biometrical Journal.

 

 

Sonstige Manuskripte / Other Manuscipts

  • Müller, Ch.H. (1984). Asymptotisch optimale und robuste M-Schätzungen. Diplomarbeit. Freie Universität Berlin.
  • Müller, Ch.H. (1991). Sta­tistik für Biologen. Vorlesungsskript am Fachbereich Mathematik und Informatik der Freien Universität Berlin

Anfahrt & Lageplan

Der Campus der Technischen Universität Dortmund liegt in der Nähe des Autobahnkreuzes Dortmund West, wo die Sauerlandlinie A45 den Ruhrschnellweg B1/A40 kreuzt. Die Abfahrt Dortmund-Eichlinghofen auf der A45 führt zum Campus Süd, die Abfahrt Dortmund-Dorstfeld auf der A40 zum Campus-Nord. An beiden Ausfahrten ist die Universität ausgeschildert.

Direkt auf dem Campus Nord befindet sich die S-Bahn-Station „Dortmund Universität“. Von dort fährt die S-Bahn-Linie S1 im 15- oder 30-Minuten-Takt zum Hauptbahnhof Dortmund und in der Gegenrichtung zum Hauptbahnhof Düsseldorf über Bochum, Essen und Duisburg. Außerdem ist die Universität mit den Buslinien 445, 447 und 462 zu erreichen. Eine Fahrplanauskunft findet sich auf der Homepage des Verkehrsverbundes Rhein-Ruhr, außerdem bieten die DSW21 einen interaktiven Liniennetzplan an.
 

Vom Flughafen Dortmund aus gelangt man mit dem AirportExpress innerhalb von gut 20 Minuten zum Dortmunder Hauptbahnhof und von dort mit der S-Bahn zur Universität. Ein größeres Angebot an internationalen Flugverbindungen bietet der etwa 60 Kilometer entfernte Flughafen Düsseldorf, der direkt mit der S-Bahn vom Bahnhof der Universität zu erreichen ist.

Zu den Wahrzeichen der TU Dortmund gehört die H-Bahn. Linie 1 verkehrt im 10-Minuten-Takt zwischen Dortmund Eichlinghofen und dem Technologiezentrum über Campus Süd und Dortmund Universität S, Linie 2 pendelt im 5-Minuten-Takt zwischen Campus Nord und Campus Süd. Diese Strecke legt sie in zwei Minuten zurück.