Gaining insight with recursive partitioning of generalized linear models

DOI10.1080/00949655.2012.658804zbMath1431.62317OpenAlexW2100674457WikidataQ57263793 ScholiaQ57263793MaRDI QIDQ5218867

Achim Zeileis, Thomas Rusch

Publication date: 6 March 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: http://epub.wu.ac.at/3866/1/Rusch%2BZeileis%2D2012.pdf

zbMATH Keywords

maximum likelihoodgeneralized linear modelsparameter instabilitymodel treesmodel-based recursive partitioningfunctional trees

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)

Related Items

Linear Aggregation in Tree-Based Estimators, Local Linear Forests, Count regression trees, An explicit split point procedure in model-based trees allowing for a quick fitting of GLM trees and GLM forests

Uses Software

• R
• WEKA
• RWeka

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Open-source machine learning: R meets Weka
• Log-normal regression modeling through recursive partitioning
• Regression trees for analysis of count data with extra Poisson variation
• Improving the precision of classification trees
• Recursive partitioning and applications
• Principles and theory for data mining and machine learning
• Nonparametric estimation of conditional quantiles using quantile regression trees
• BUSDM – an algorithm for the bottom-up search of departures from a model
• Tests for Parameter Instability and Structural Change With Unknown Change Point
• Statistical Analysis of Financial Data in S-Plus
• Maximum Likelihood: An Introduction
• On the existence of maximum likelihood estimates in logistic regression models
• Generalized M‐fluctuation tests for parameter instability
• Tree-Structured Logistic Model for Over-Dispersed Binomial Data with Application to Modeling Developmental Effects
• Tests For Constancy Of Model Parameters Over Time
• Tree-Structured Proportional Hazards Regression Modeling
• Tree-structured extreme value model regression
• Tree‐Structured Exponential Regression Modeling
• Automatic locally adaptive smoothing for tree-based set estimation
• Log-gamma regression modeling 'through regression trees
• A Unified Approach to Structural Change Tests Based on ML Scores,FStatistics, and OLS Residuals
• Problems in the Analysis of Survey Data, and a Proposal
• Functional trees
• Robust Statistics
• Maximum Likelihood Estimation of Misspecified Models
• Logistic model trees