Introducing Lasso-type penalisation to generalised joint regression modelling for count data

Related Items (1)

Editorial special issue: statistics in sports

Cites Work

Unnamed Item
Unnamed Item
Unnamed Item
The Adaptive Lasso and Its Oracle Properties
An introduction to copulas.
Boosting additive models using component-wise P-splines
Estimating the dimension of a model
Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
Bivariate copula additive models for location, scale and shape
Generalised joint regression for count data: a penalty extension for competitive settings
Lasso-type penalization in the framework of generalized additive models for location, scale and shape
A uniform framework for the combination of penalties in generalized structured models
A ratings based Poisson model for World Cup soccer simulation
Modelling soccer matches using bivariate discrete distributions with general dependence structure
The Group Lasso for Logistic Regression
Analysis of the importance of on-field covariates in the German Bundesliga
Regression in a copula model for bivariate count data
Copula Link-Based Additive Models for Right-Censored Event Time Data
Subject-specific modelling of paired comparison data: A lasso-type penalty approach
Predicting matches in international football tournaments with random forests
Ranking soccer teams on the basis of their current strength: A comparison of maximum likelihood approaches
Generalized Additive Modeling with Implicit Variable Selection by Likelihood‐Based Boosting
Regularization and Variable Selection Via the Elastic Net
Ridge Regression: Biased Estimation for Nonorthogonal Problems

This page was built for publication: Introducing Lasso-type penalisation to generalised joint regression modelling for count data