Bayesian estimation on semiparametric models with shape restricted B-splines

• Published in: Communications in statistics. Simulation and computation Vol. 47; no. 5; pp. 1315 - 1325
• Main Author: Ding, Jianhua
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 28.05.2018; Taylor & Francis Ltd
• Subjects: Basis functions; Bayesian analysis; Computer simulation; Data analysis; Markov chain Monte Carlo sampler; Markov chains; Monte Carlo simulation; Normal distribution; Penalized B-splines; Shape constrains; Splines; Truncated normal distribution
• ISSN: 0361-0918; 1532-4141
• DOI: 10.1080/03610918.2017.1311915

In this paper, we develop a Bayesian estimation procedure for semiparametric models under shape constrains. The approach uses a hierarchical Bayes framework and characterizations of shape-constrained B-splines. We employ Markov chain Monte Carlo methods for model fitting, using a truncated normal distribution as the prior for the coefficients of basis functions to ensure the desired shape constraints. The small sample properties of the function estimators are provided via simulation and compared with existing methods. A real data analysis is conducted to illustrate the application of the proposed method.