Subject-specific modelling of paired comparison data: A lasso-type penalty approach
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Publication:5142180
DOI10.1177/1471082X17693086MaRDI QIDQ5142180
Gunther Schauberger, Gerhard Tutz
Publication date: 30 December 2020
Published in: Statistical Modelling (Search for Journal in Brave)
Related Items (5)
Analysis of the importance of on-field covariates in the German Bundesliga ⋮ The Maxwell paired comparison model under Bayesian paradigm using informative priors ⋮ Introducing Lasso-type penalisation to generalised joint regression modelling for count data ⋮ Bayesian analysis of the Weibull paired comparison model using numerical approximation ⋮ Masking data: a solution to social desirability bias in paired comparison experiments
Uses Software
Cites Work
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- The Adaptive Lasso and Its Oracle Properties
- The ranking lasso and its application to sport tournaments
- Models for paired comparison data: a review with emphasis on dependent data
- Sparse modeling of categorial explanatory variables
- Modeling heterogeneity in ranked responses by nonparametric maximum likelihood: How do Europeans get their scientific knowledge?
- Bradley-Terry-Luce models with an ordered response
- Estimating the dimension of a model
- RcppArmadillo: accelerating R with high-performance C++ linear algebra
- Extended ordered paired comparison models with application to football data from German Bundesliga
- The analysis of rank ordered preference data based on Bradley-Terry type models
- A log-linear approach for modelling ordinal paired comparison data on motives to start a PhD programme
- Analysing Partial Ranks by Using Smoothed Paired Comparison Methods: An Investigation of Value Orientation in Europe
- Simultaneous Factor Selection and Collapsing Levels in ANOVA
- A Biometrics Invited Paper. Science, Statistics, and Paired Comparisons
- On ordinary ridge regression in generalized linear models
- Modelling the Effect of Subject-Specific Covariates in Paired Comparison Studies with an Application to University Rankings
- Marginal Regression Models for Clustered Ordinal Measurements
- Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
- The Analysis of Longitudinal Polytomous Data: Generalized Estimating Equations and Connections with Weighted Least Squares
- Restricted Estimation of Generalized Linear Models
- Ridge Estimators in Logistic Regression
- Analysis of Ordinal Paired Comparison Data
- A paired comparison approach for the analysis of sets of Likert-scale responses
- Regularization and model selection with categorical predictors and effect modifiers in generalized linear models
- Subject-specific Bradley–Terry–Luce models with implicit variable selection
- Strictly Proper Scoring Rules, Prediction, and Estimation
- Seamless R and C++ Integration with Rcpp
- A Mixture Model for Longitudinal Partially Ranked Data
- Ridge Regression: Biased Estimation for Nonorthogonal Problems
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