TY - JOUR
T1 - Bayesian temporal density estimation with autoregressive species sampling models
AU - Jo, Youngin
AU - Jo, Seongil
AU - Lee, Yung Seop
AU - Lee, Jaeyong
N1 - Publisher Copyright:
© 2018 The Korean Statistical Society
PY - 2018/9
Y1 - 2018/9
N2 - We propose a novel Bayesian nonparametric (BNP) model, which is built on a class of species sampling models, for estimating density functions of temporal data. In particular, we introduce species sampling mixture models with temporal dependence. To accommodate temporal dependence, we define dependent species sampling models by modeling random support points and weights through an autoregressive model, and then we construct the mixture models based on the collection of these dependent species sampling models. We propose an algorithm to generate posterior samples and present simulation studies to compare the performance of the proposed models with competitors that are based on Dirichlet process mixture models. We apply our method to the estimation of densities for the price of apartment in Seoul, the closing price in Korea Composite Stock Price Index (KOSPI), and climate variables (daily maximum temperature and precipitation) of around the Korean peninsula.
AB - We propose a novel Bayesian nonparametric (BNP) model, which is built on a class of species sampling models, for estimating density functions of temporal data. In particular, we introduce species sampling mixture models with temporal dependence. To accommodate temporal dependence, we define dependent species sampling models by modeling random support points and weights through an autoregressive model, and then we construct the mixture models based on the collection of these dependent species sampling models. We propose an algorithm to generate posterior samples and present simulation studies to compare the performance of the proposed models with competitors that are based on Dirichlet process mixture models. We apply our method to the estimation of densities for the price of apartment in Seoul, the closing price in Korea Composite Stock Price Index (KOSPI), and climate variables (daily maximum temperature and precipitation) of around the Korean peninsula.
KW - Autoregressive species sampling models
KW - Dependent random probability measures
KW - Mixture models
KW - Temporal structured data
UR - http://www.scopus.com/inward/record.url?scp=85045012148&partnerID=8YFLogxK
U2 - 10.1016/j.jkss.2018.02.002
DO - 10.1016/j.jkss.2018.02.002
M3 - Article
AN - SCOPUS:85045012148
SN - 1226-3192
VL - 47
SP - 248
EP - 262
JO - Journal of the Korean Statistical Society
JF - Journal of the Korean Statistical Society
IS - 3
ER -