Australian & New Zealand Journal of Statistics

Semiparametric model averaging prediction: a Bayesian approach

Journal Article

Summary

We present a novel model averaging method to construct a prediction function in semi‐parametric form. The weighted sum of candidate semi‐parametric models is taken as a prediction of the mean response. Marginal non‐parametric regression models are approximated by spline basis functions and we apply a Bayesian Monte Carlo approach to fit such models. The optimal model weight parameters are estimated by minimising the least squares criterion with an explicit form. We implement our method in extensive simulation studies and illustrate its use with two real medical data examples. Our methods are demonstrated to be more accurate than both classical parametric model averaging methods and existing semi‐parametric regression models.

Related Topics

Related Publications

Related Content

Site Footer

Address:

This website is provided by John Wiley & Sons Limited, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ (Company No: 00641132, VAT No: 376766987)

Published features on StatisticsViews.com are checked for statistical accuracy by a panel from the European Network for Business and Industrial Statistics (ENBIS)   to whom Wiley and StatisticsViews.com express their gratitude. This panel are: Ron Kenett, David Steinberg, Shirley Coleman, Irena Ograjenšek, Fabrizio Ruggeri, Rainer Göb, Philippe Castagliola, Xavier Tort-Martorell, Bart De Ketelaere, Antonio Pievatolo, Martina Vandebroek, Lance Mitchell, Gilbert Saporta, Helmut Waldl and Stelios Psarakis.