Stochastic Approximation Boosting for Incomplete Data Problems

DOI10.1111/j.1541-0420.2009.01202.xzbMath1180.62115OpenAlexW2171782678WikidataQ33443375 ScholiaQ33443375MaRDI QIDQ5850964

Petter Laake, Joseph Sexton

Publication date: 21 January 2010

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1541-0420.2009.01202.x

zbMATH Keywords

stochastic approximationMarkov chain Monte Carloincomplete datageneralized additive modelboostinggeneralized linear model

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Numerical analysis or methods applied to Markov chains (65C40) Stochastic approximation (62L20)

Related Items (1)

A hybrid approach for regression analysis with block missing data

Uses Software

• R

Cites Work

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• Boosting algorithms: regularization, prediction and model fitting
• A stochastic approximation algorithm with Markov chain Monte-Carlo method for incomplete data estimation problems
• Inference and missing data
• Monte Carlo EM for Missing Covariates in Parametric Regression Models
• Coupling a stochastic approximation version of EM with an MCMC procedure
• Mixed Models
• Generalized Additive Modeling with Implicit Variable Selection by Likelihood‐Based Boosting
• Measurement Error in Nonlinear Models
• Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models
• A Stochastic Approximation Method

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