Mixtures of Self-Modelling RegressionsRhonda D Szczesniak1*, Kert Viele2,3 and Robin L Cooper4
- *Corresponding Author:
- Rhonda D Szczesniak
Division of Biostatistics & Epidemiology
Cincinnati Children's Hospital Medical Center, USA
E-mail: rho[email protected]
Received date: July 02, 2014; Accepted date: August 08, 2014; Published date: August 15, 2014
Citation: Szczesniak RD, Viele K, Cooper RL (2014) Mixtures of Self-Modelling Regressions. J Biom Biostat S12:003. doi:10.4172/2155-6180.S12-003
Copyright: © 2014 Szczesniak RD, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are are credited.
A shape invariant model for functions f1,…,fn specifies that each individual function fi can be related to a common shape function g through the relation fi(x)=aig(cix + di) + bi. We consider a flexible mixture model that allows multiple shape functions g1,…,gK, where each fi is a shape invariant transformation of one of those gk. We derive an MCMC algorithm for fitting the model using Bayesian Adaptive Regression Splines (BARS), propose a strategy to improve its mixing properties and utilize existing model selection criteria in semiparametric mixtures to select the number of distinct shape functions. We discuss some of the computational difficulties that arise. The method is illustrated using synaptic transmission data, where the groups of functions may indicate different active zones in a synapse.