Electronic Journal of Statistics

Bayesian classification of multiclass functional data

Xiuqi Li and Subhashis Ghosal

Full-text: Open access

Abstract

We propose a Bayesian approach to estimating parameters in multiclass functional models. Unordered multinomial probit, ordered multinomial probit and multinomial logistic models are considered. We use finite random series priors based on a suitable basis such as B-splines in these three multinomial models, and classify the functional data using the Bayes rule. We average over models based on the marginal likelihood estimated from Markov Chain Monte Carlo (MCMC) output. Posterior contraction rates for the three multinomial models are computed. We also consider Bayesian linear and quadratic discriminant analyses on the multivariate data obtained by applying a functional principal component technique on the original functional data. A simulation study is conducted to compare these methods on different types of data. We also apply these methods to a phoneme dataset.

Article information

Source
Electron. J. Statist., Volume 12, Number 2 (2018), 4669-4696.

Dates
Received: August 2018
First available in Project Euclid: 22 December 2018

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1545448229

Digital Object Identifier
doi:10.1214/18-EJS1522

Keywords
Multiclass functional data multinomial probit models B-splines posterior contraction rate discriminant analysis

Rights
Creative Commons Attribution 4.0 International License.

Citation

Li, Xiuqi; Ghosal, Subhashis. Bayesian classification of multiclass functional data. Electron. J. Statist. 12 (2018), no. 2, 4669--4696. doi:10.1214/18-EJS1522. https://projecteuclid.org/euclid.ejs/1545448229


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