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Title of Journal: Stat Comput

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Abbravation: Statistics and Computing

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Springer US

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DOI

10.1016/0093-691x(90)90761-h

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1573-1375

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Boosted coefficient models

Authors: Joseph Sexton Petter Laake
Publish Date: 2011/04/06
Volume: 22, Issue: 4, Pages: 867-876
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Abstract

Regression methods typically construct a mapping from the covariates into the real numbers Here however we consider regression problems where the task is to form a mapping from the covariates into a set of univariate realvalued functions Examples are given by conditional density estimation hazard regression and regression with a functional response Our approach starts by modeling the function of interest using a sum of Bspline basis functions To model dependence on the covariates the coefficients of this expansion are each modeled as functions of the covariates We propose to estimate these coefficient functions using boosted tree models Algorithms are provided for the above three situations and real data sets are used to investigate their performance The results indicate that the proposed methodology performs well In addition it is both straightforward and capable of handling a large number of covariatesThis article is published under an open access license Please check the Copyright Information section for details of this license and what reuse is permitted If your intended use exceeds what is permitted by the license or if you are unable to locate the licence and reuse information please contact the Rights and Permissions team

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  2. A focused information criterion for graphical models
  3. An advancement in clustering via nonparametric density estimation
  4. Clustering via nonparametric density estimation
  5. Sequential importance sampling of binary sequences
  6. Deletion diagnostics for marginal mean and correlation model parameters in estimating equations
  7. Optimal SIR algorithm vs. fully adapted auxiliary particle filter: a non asymptotic analysis
  8. Non-parametric maximum likelihood estimation of interval-censored failure time data subject to misclassification
  9. Gaussian proposal density using moment matching in SMC methods
  10. Bayesian inference for Heston-STAR models
  11. Parsimonious Gaussian mixture models
  12. Efficient estimation of variance components in nonparametric mixed-effects models with large samples
  13. On the computation of the noncentral F and noncentral beta distribution
  14. A new variable importance measure for random forests with missing data
  15. Complement to a comparative analysis of heuristics for the  p -median problem
  16. Likelihood-free parallel tempering
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