Matthias Schmid

发表

Assessment of facial soft-tissue profiles based on lateral photographs versus three-dimensional face scans

Martin Fink, Ursula Hirschfelder, Veronika Hirschinger, 2016, Journal of Orofacial Orthopedics / Fortschritte der Kieferorthopädie.

Robust reduced order control for nonlinear distributed systems of Burgers class

Matthias Schmid, 2009 .

An Approach to Efficiently Storing Property Graphs in Relational Databases

Matthias Schmid, 2019, Grundlagen von Datenbanken.

A permutation test to analyse systematic bias and random measurement errors of medical devices via boosting location and scale models

Matthias Schmid, Andreas Mayr, Annette Pfahlberg, 2017, Statistical methods in medical research.

Multiple-Spell Analysis

Gerhard Tutz, Matthias Schmid, G. Tutz, 2016 .

Proceedings of Reisensburg 2010

Hans A. Kestler, Matthias Schmid, Harald Binder, 2013, Comput. Stat..

Genome-wide generalized additive models

Achim Tresch, Matthias Schmid, Daniel Schulz, 2016 .

Estimation and regularization techniques for regression models with multidimensional prediction functions

Torsten Hothorn, Matthias Schmid, Sergej Potapov, 2010, Stat. Comput..

Flight Performance Calculation of Rotorcraft in the Conceptual Design Phase

Matthias Schmid, Peter Weiand, Dominik Schwinn, 2018 .

Sparse kernel deep stacking networks

Matthias Schmid, Thomas Welchowski, M. Schmid, 2018, Comput. Stat..

The betaboost package-a software tool for modelling bounded outcome variables in potentially high-dimensional epidemiological data.

Andreas Mayr, Benjamin Hofner, Matthias Schmid, 2018, International journal of epidemiology.

The residual‐based predictiveness curve: A visual tool to assess the performance of prediction models

Bernd Bischl, Anne-Laure Boulesteix, Matthias Schmid, 2016, Biometrics.

Advances in Intelligent Data Analysis XIV

Bernd Bischl, Neil D. Lawrence, Michèle Sebag, 2015, Lecture Notes in Computer Science.

Rotorcraft Fuselage Weight Assessment in Early Design Stages

Matthias Schmid, Peter Weiand, Dominik Schwinn, 2018 .

Model-based boosting in R Introduction to Gradient Boosting

Matthias Schmid, M. Schmid, 2011 .

Boosting the discriminatory power of sparse survival models via optimization of the concordance index and stability selection

Benjamin Hofner, Matthias Schmid, Andreas Mayr, 2016, BMC Bioinformatics.

On the use of Harrell's C for clinical risk prediction via random survival forests

Matthias Schmid, Andreas Ziegler, Marvin N. Wright, 2015, Expert Syst. Appl..

Structural Sizing of a Rotorcraft Fuselage Using an Integrated Design Approach

Matthias Schmid, Peter Weiand, Dominik Schwinn, 2020 .

The application of digital rights management systems in the music industry - an empirical investigation

Matthias Schmid, Marc Fetscherin, 2003, Proceedings Third International Conference on WEB Delivering of Music.

Competing Risks Models

Gerhard Tutz, Matthias Schmid, G. Tutz, 2016 .

On the validity of time-dependent AUC estimators

Hans A. Kestler, Matthias Schmid, Sergej Potapov, 2015, Briefings Bioinform..

Koffein-Boost und Kolibri-Präzision. Wie der Knowledge Graph die Google-Suche verändert

Matthias Schmid, M. Schmid, 2013 .

Panel Model with Multiplicative Measurement Errors

Matthias Schmid, Gerd Ronning, Hans Schneeweiss, 2015 .

Proceedings of Reisensburg 2014–2015

Bernd Bischl, Hans A. Kestler, Matthias Schmid, 2018, Comput. Stat..

High-Dimensional Models: Structuring and Selection of Predictors

Gerhard Tutz, Matthias Schmid, G. Tutz, 2016 .

AUC-based splitting criteria for random survival forests

Matthias Schmid, Marvin N. Wright, Andreas Ziegler, 2015 .

Subdistribution hazard models for competing risks in discrete time.

Matthias Schmid, Steffen Schmitz-Valckenberg, Thomas Welchowski, 2018, Biostatistics.

Laser-Based Hierarchical Grid Mapping for Detection and Tracking of Moving Objects

Matthias Schmid, Jurgen Dickmann, Markus Schütz, 2012 .

Significance Tests for Boosted Location and Scale Models with Linear Base-Learners

Matthias Schmid, Andreas Mayr, Tobias Hepp, 2019, The international journal of biostatistics.

Flexible boosting of accelerated failure time models

Torsten Hothorn, Matthias Schmid, T. Hothorn, 2008, BMC Bioinformatics.

Statistical computing 2009 - Abstracts der 41. Jahrestagung / Hans A. Kestler et al. (eds.)

Matthias Schmid, Hans-Peter Klenk, Berthold Lausen, 2009 .

“Now is a good time to investigate the topic of new generic top-level domains.”

Matthias Schmid, 2013 .

Reliable automotive pre-crash system with out-of-sequence measurement processing

Klaus C. J. Dietmayer, Jürgen Dickmann, Marc M. Muntzinger, 2010, 2010 IEEE Intelligent Vehicles Symposium.

A statistical model for the analysis of beta values in DNA methylation studies

Matthias Schmid, Simone Wahl, Leonie Weinhold, 2016, BMC Bioinformatics.

Adaptive control for spacecraft formation flying with solar radiation pressure and reduction of secular drift

Matthias Schmid, John Crassidis, Aniketh Kalur, 2016, 2016 American Control Conference (ACC).

Statistical Applications in Genetics and Molecular Biology

Torsten Hothorn, Matthias Schmid, 2012 .

GiANT: gene set uncertainty in enrichment analysis

Florian Schmid, Hans A. Kestler, Matthias Schmid, 2016, Bioinform..

A survival tree method for the analysis of discrete event times in clinical and epidemiological studies

Gerhard Tutz, Matthias Schmid, Helmut Küchenhoff, 2016, Statistics in medicine.

Experimental Demonstration of Network Virtualization and Resource Flexibility in the COMCON Project

Michael Duelli, Matthias Schmid, Thomas Zinner, 2012, TRIDENTCOM.

A multidisciplinary process for integrated rotorcraft design

Matthias Schmid, Peter Weiand, Dominik Schwinn, 2020 .

Modeling Discrete Time-To-Event Data

Gerhard Tutz, Matthias Schmid, G. Tutz, 2016 .

GenoGAM: Genome-wide generalized additive models for ChIP-seq analysis

Achim Tresch, Matthias Schmid, Daniel Schulz, 2016, bioRxiv.

Semiparametric regression for discrete time-to-event data

Matthias Schmid, Moritz Berger, M. Schmid, 2017, 1704.04087.

Boosting additive models using component-wise P-Splines

Torsten Hothorn, Matthias Schmid, T. Hothorn, 2008, Comput. Stat. Data Anal..

Model-based boosting in R: a hands-on tutorial using the R package mboost

Benjamin Hofner, Matthias Schmid, Nikolay Robinzonov, 2012, Computational Statistics.

Universal Search, Inbound Marketing und die Suche der Zukunft – Highlights von der SMX München 2012

Matthias Schmid, 2012 .

Landmarks of the Frankfort horizontal plane

Ursula Hirschfelder, Matthias Schmid, Andreas Detterbeck, 2016, Journal of Orofacial Orthopedics / Fortschritte der Kieferorthopädie.

Comparative Study of Digital Rights Management Systems for Music and Text Files

Matthias Schmid, Marc Fetscherin, 2003 .

Basic Regression Models

Gerhard Tutz, Matthias Schmid, G. Tutz, 2016 .

„Jetzt ist ein guter Zeitpunkt, um das Thema „neue generische Top Level Domains“ zu prüfen.“

Matthias Schmid, 2013 .

Universal search, inbound marketing and searching in the future – highlights from SMX Munich 2012

Matthias Schmid, 2012 .

Gradient boosting for distributional regression: faster tuning and improved variable selection via noncyclical updates

Bernd Bischl, Benjamin Hofner, Matthias Schmid, 2018, Stat. Comput..

A PAUC-based Estimation Technique for Disease Classification and Biomarker Selection

Torsten Hothorn, Matthias Schmid, Friedemann Krause, 2012, Statistical applications in genetics and molecular biology.

Tree-Based Approaches

Gerhard Tutz, Matthias Schmid, 2016 .

Boosting joint models for longitudinal and time‐to‐event data

Matthias Schmid, Thomas Kneib, Andreas Mayr, 2016, Biometrical journal. Biometrische Zeitschrift.

Tree-based modeling of time-varying coefficients in discrete time-to-event models

Gerhard Tutz, Matthias Schmid, Moritz Berger, 2019, Lifetime data analysis.

Copula-based modeling of stochastic wind power in Europe and implications for the Swiss power grid

Matthias Schmid, Goran Andersson, Simeon Hagspiel, 2012 .

Ensemble classification of paired data

Matthias Schmid, Alexander Brenning, Berthold Lausen, 2011, Comput. Stat. Data Anal..

Boosting the Concordance Index for Survival Data – A Unified Framework To Derive and Evaluate Biomarker Combinations

Matthias Schmid, Andreas Mayr, M. Schmid, 2013, PloS one.

Unlabeling data can improve classification accuracy

Florian Schmid, Ludwig Lausser, Hans A. Kestler, 2014, Pattern Recognit. Lett..

Abstracts der 45. Arbeitstagung

Matthias Schmid, Hans A. Kestler, F Schmid M Maucher, 2013 .

Structural Analysis of a Rotorcraft Fuselage in a Multidisciplinary Environment

Matthias Schmid, Peter Weiand, Dominik Schwinn, 2017 .

Comparing the usage of digital rights management systems in the music, film, and print industry

Matthias Schmid, Marc Fetscherin, 2003, ICEC '03.

Proceedings of Reisensburg 2011

Hans A. Kestler, Matthias Schmid, Harald Binder, 2014, Comput. Stat..

GenoGAM: Genome-wide generalized additive models for ChIP-seq analysis

Achim Tresch, Matthias Schmid, Georg Stricker, 2017 .

Social Media, Tools und Technologie: Die SEO-Trends von der SMX München

Matthias Schmid, 2011 .

Model-Based Boosting

Torsten Hothorn, Benjamin Hofner, Matthias Schmid, 2015 .

Towards a Data Science Collaboratory

Bernd Bischl, Neil D. Lawrence, Balázs Kégl, 2015 .

Reproducible research in statistics: A review and guidelines for the Biometrical Journal

Benjamin Hofner, Matthias Schmid, Lutz Edler, 2016, Biometrical journal. Biometrische Zeitschrift.

A review of spline function procedures in R

Matthias Schmid, Willi Sauerbrei, Michal Abrahamowicz, 2019, BMC Medical Research Methodology.

Comparing classification methods for diffuse reflectance spectra to improve tissue specific laser surgery

Matthias Schmid, Rajesh Kanawade, Florian Stelzle, 2014, BMC Medical Research Methodology.

Model-based Boosting 2.0

Torsten Hothorn, Benjamin Hofner, Matthias Schmid, 2010, J. Mach. Learn. Res..

gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework

Benjamin Hofner, Matthias Schmid, Andreas Mayr, 2014, 1407.1774.

A framework for parameter estimation and model selection in kernel deep stacking networks

Matthias Schmid, Thomas Welchowski, M. Schmid, 2016, Artif. Intell. Medicine.

Proceedings of Reisensburg 2016–2017

Bernd Bischl, Matthias Schmid, Hans A. Kestler, 2019, Computational statistics (Zeitschrift).

A classification tree approach for the modeling of competing risks in discrete time

Matthias Schmid, Moritz Berger, Steffen Schmitz-Valckenberg, 2018, Adv. Data Anal. Classif..

Tree-structured modelling of varying coefficients

Gerhard Tutz, Matthias Schmid, Moritz Berger, 2017, Stat. Comput..

On the potential of models for location and scale for genome-wide DNA methylation data

Christian Gieger, Matthias Schmid, Simone Wahl, 2014, BMC Bioinformatics.

Subdistribution-Based Imputation for Deep Survival Analysis with Competing Events

Matthias Schmid, Charlotte Behning, Shekoufeh Gorgi Zadeh, 2021 .

A Robust Alternative to the Schemper–Henderson Estimator of Prediction Error

Matthias Schmid, Thomas Augustin, Olaf Gefeller, 2011, Biometrics.

Predicting CYP2D6 phenotype from resting brain perfusion images by gradient boosting

Matthias Schmid, Roberto Viviani, Giulio Napolitano, 2017, Psychiatry Research: Neuroimaging.

The Effect of Microaggregation Procedures on the Estimation of Linear Models: A Simulation Study

Matthias Schmid, Hans Schneeweiss, H. Schneeweiß, 2005 .

Discrimination measures for discrete time-to-event predictions

Gerhard Tutz, Matthias Schmid, Thomas Welchowski, 2017 .

Approaches to Regularized Regression – A Comparison between Gradient Boosting and the Lasso

Matthias Schmid, Olaf Gefeller, Elisabeth Waldmann, 2016, Methods of Information in Medicine.

Machine learning versus statistical modeling

Anne-Laure Boulesteix, Matthias Schmid, A. Boulesteix, 2014, Biometrical journal. Biometrische Zeitschrift.

Proceedings of Reisensburg 2013

Hans A. Kestler, Matthias Schmid, H. Kestler, 2015 .

GAMLSS for high-dimensional data – a flexible approach based on boosting

Benjamin Hofner, Matthias Schmid, Thomas Kneib, 2010 .

Applying additive modelling and gradient boosting to assess the effects of watershed and reach characteristics on riverine assemblages

Matthias Schmid, Donald E. Weller, Kelly O. Maloney, 2012 .

A Framework for Unbiased Model Selection Based on Boosting

Torsten Hothorn, Benjamin Hofner, Matthias Schmid, 2011 .

Geoadditive regression modeling of stream biological condition

Torsten Hothorn, Matthias Schmid, Sergej Potapov, 2010, Environmental and Ecological Statistics.

A comparison of estimators to evaluate the discriminatory power of time‐to‐event models

Matthias Schmid, Sergej Potapov, M. Schmid, 2012, Statistics in medicine.

Boosted Beta Regression

Matthias Schmid, Kelly O. Maloney, Andreas Mayr, 2013, PloS one.

Developing and testing temperature models for regulated systems: A case study on the Upper Delaware River

Matthias Schmid, James E. McKenna, Kelly O. Maloney, 2014 .

Generalized additive models for location, scale and shape for high dimensional data—a flexible approach based on boosting

Benjamin Hofner, Matthias Schmid, Thomas Kneib, 2012 .