Bayesian methods for wavelet series in single-index models
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abstract
Single-index models have found applications in econometrics and biometrics, where multidimensional regression models are often encountered. This article proposes a non-parametric estimation approach that combines wavelet methods for nonequispaced designs with Bayesian models. We consider a wavelet series expansion of the unknown regression function and set prior distributions for the wavelet coefficients and the other model parameters. To ensure model identifiability, the direction parameter is represented via its polar coordinates. We employ ad hoc hierarchical mixture priors that perform shrinkage on wavelet coefficients and use Markov chain Monte Carlo methods for a posteriori inference. We investigate an independence-type Metropolis-Hastings algorithm to produce samples for the direction parameter. Our method leads to simultaneous estimates of the link function and of the index parameters. We present results on both simulated and real data, where we look at comparisons with other methods. 2005 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
