Log-normal regression modeling through recursive partitioning
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Publication:672064
DOI10.1016/0167-9473(95)00023-2zbMath0875.62338OpenAlexW2010206741MaRDI QIDQ672064
Publication date: 27 February 1997
Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/0167-9473(95)00023-2
Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12)
Related Items (5)
Unbiased estimates for a lognormal regression problem and a nonparametric alternative ⋮ Distance-based tree models for ranking data ⋮ Gaining insight with recursive partitioning of generalized linear models ⋮ Applied regression analysis bibliography update 1994-97 ⋮ Confidence intervals for lognormal regression and a non-parametric alternative
Uses Software
Cites Work
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- Regression analysis with randomly right-censored data
- Survival modeling through recursive stratification
- Generalized regression trees
- A Method of Analyzing Log-Normally Distributed Survival Data with Incomplete Follow-Up
- Pruning regression trees for censored survival data: the recpam approach
- Regression Trees for Censored Data
- Linear regression with censored data
- Algorithm AS 139: Maximum Likelihood Estimation in a Linear Model from Confined and Censored Normal Data
- Regression with censored data
- Least squares regression with censored data
- Linear Estimation of a Regression Relationship from Censored Data: Part II Best Linear Unbiased Estimation and Theory
- Tree-Structured Proportional Hazards Regression Modeling
- Tree-structured extreme value model regression
- Linear Estimation of a Regression Relationship from Censored Data Part I: Simple Methods and Their Application
- Problems in the Analysis of Survey Data, and a Proposal
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