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Christian Staerk

Showing results (1-10 of 37) with videos related to

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BMC Bioinformatics|September 17, 2021
Randomized boosting with multivariable base-learners for high-dimensional variable selection and predictionChristian Staerk, Andreas Mayr
International Journal of Gynecological Cancer : Official Journal of the International Gynecological Cancer Society|May 26, 2022
Response to: Correspondence on 'Clinical value of pre-operative scoring systems to predict leiomyosarcoma: results of a validation study in 177 patients from the NOGGO-REGSA Registry' by Vollmer <i>et al</i>Mateja Condic, Christian Staerk, Alexander Mustea
American Journal of Epidemiology|September 29, 2023
Recent Methodological Trends in Epidemiology: No Need for Data-Driven Variable Selection?Christian Staerk, Alliyah Byrd, Andreas Mayr
BMC Public Health|June 6, 2021
Estimating effective infection fatality rates during the course of the COVID-19 pandemic in GermanyChristian Staerk, Tobias Wistuba, Andreas Mayr
Scientific Reports|June 13, 2022
Estimating the course of the COVID-19 pandemic in Germany via spline-based hierarchical modelling of death countsTobias Wistuba, Andreas Mayr, Christian Staerk
The International Journal of Biostatistics|August 11, 2022
Robust statistical boosting with quantile-based adaptive loss functionsJan Speller, Christian Staerk, Andreas Mayr
BMC Medical Genomics|May 16, 2024
Generalizability of polygenic prediction models: how is the R<sup>2</sup> defined on test data?Christian Staerk, Hannah Klinkhammer, Tobias Wistuba, et al.
Statistics in Medicine|March 18, 2023
Boosting multivariate structured additive distributional regression modelsAnnika Strömer, Nadja Klein, Christian Staerk, et al.
Frontiers in Genetics|January 27, 2023
A statistical boosting framework for polygenic risk scores based on large-scale genotype dataHannah Klinkhammer, Christian Staerk, Carlo Maj, et al.
Statistics in Medicine|October 23, 2024
Genetic Prediction Modeling in Large Cohort Studies via Boosting Targeted Loss FunctionsHannah Klinkhammer, Christian Staerk, Carlo Maj, et al.
Pageof 4

Showing results (1-10 of 37) with videos related to

Sort By:
Pageof 4
BMC Bioinformatics|September 17, 2021
Randomized boosting with multivariable base-learners for high-dimensional variable selection and predictionChristian Staerk, Andreas Mayr
International Journal of Gynecological Cancer : Official Journal of the International Gynecological Cancer Society|May 26, 2022
Response to: Correspondence on 'Clinical value of pre-operative scoring systems to predict leiomyosarcoma: results of a validation study in 177 patients from the NOGGO-REGSA Registry' by Vollmer <i>et al</i>Mateja Condic, Christian Staerk, Alexander Mustea
American Journal of Epidemiology|September 29, 2023
Recent Methodological Trends in Epidemiology: No Need for Data-Driven Variable Selection?Christian Staerk, Alliyah Byrd, Andreas Mayr
BMC Public Health|June 6, 2021
Estimating effective infection fatality rates during the course of the COVID-19 pandemic in GermanyChristian Staerk, Tobias Wistuba, Andreas Mayr
Scientific Reports|June 13, 2022
Estimating the course of the COVID-19 pandemic in Germany via spline-based hierarchical modelling of death countsTobias Wistuba, Andreas Mayr, Christian Staerk
The International Journal of Biostatistics|August 11, 2022
Robust statistical boosting with quantile-based adaptive loss functionsJan Speller, Christian Staerk, Andreas Mayr
BMC Medical Genomics|May 16, 2024
Generalizability of polygenic prediction models: how is the R<sup>2</sup> defined on test data?Christian Staerk, Hannah Klinkhammer, Tobias Wistuba, et al.
Statistics in Medicine|March 18, 2023
Boosting multivariate structured additive distributional regression modelsAnnika Strömer, Nadja Klein, Christian Staerk, et al.
Frontiers in Genetics|January 27, 2023
A statistical boosting framework for polygenic risk scores based on large-scale genotype dataHannah Klinkhammer, Christian Staerk, Carlo Maj, et al.
Statistics in Medicine|October 23, 2024
Genetic Prediction Modeling in Large Cohort Studies via Boosting Targeted Loss FunctionsHannah Klinkhammer, Christian Staerk, Carlo Maj, et al.
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