Joint classification and prediction of random curves using heavy‐tailed process functional regression

Cao，Chunzheng1; Liu，Xin1; Cao，Shuren1; Shi，Jian Qing2

2023-04-01

10.1016/j.patcog.2022.109213

PATTERN RECOGNITION   Impact Factor & Quartile

0031-3203

We propose a heavy-tailed process functional regression to jointly perform classification and prediction of time-varying functional data. We use two independent scale mixtures of Gaussian Processes to respectively model random effects and random errors, yielding robust inferences against both magnitude and shape outliers. We classify random curves by posterior predictive probabilities of class labels and offer a weighted prediction of future curve trends. A Bayesian estimation procedure is implemented through an MCMC sampling algorithm. The performance of classification and prediction of the proposed model is evaluated using simulated studies and some real data sets.

Bayesian estimation Functional data analysis Heavy-tailed process MCMC Outliers

SCI

English

Others

WOS:000900808400002

ENGINEERING

2-s2.0-85144816531

Scopus

1.School of Mathematics and Statistics,Nanjing University of Information Science and Technology,China
2.Department of Statistics and Data Science,College of Science,Southern University of Science and Technology,China

Cao，Chunzheng,Liu，Xin,Cao，Shuren,等. Joint classification and prediction of random curves using heavy‐tailed process functional regression[J]. PATTERN RECOGNITION,2023,136.

Cao，Chunzheng,Liu，Xin,Cao，Shuren,&Shi，Jian Qing.(2023).Joint classification and prediction of random curves using heavy‐tailed process functional regression.PATTERN RECOGNITION,136.

Cao，Chunzheng,et al."Joint classification and prediction of random curves using heavy‐tailed process functional regression".PATTERN RECOGNITION 136(2023).