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5 results on '"HAOXIN ZHUANG"'

4. A Bayesian hierarchical copula model

Author

Liqun Diao, Haoxin Zhuang, and Grace Y. Yi

Subjects
Statistics and Probability, Sampling scheme, MCMC, dependence modeling, Computation, 05 social sciences, Bayesian probability, Estimator, Markov chain Monte Carlo, 01 natural sciences, Copula (probability theory), 010104 statistics & probability, symbols.namesake, 0502 economics and business, symbols, Bayesian hierarchical modeling, copula, 0101 mathematics, Statistics, Probability and Uncertainty, Algorithm, Bayesian hierarchical model, 050205 econometrics, Mathematics
Abstract

Dependent data with hierarchical structures arises commonly from a variety of application, and analysis of such data is often challenging due to the complexity in modeling dependence structures and the computation intensity. In this paper, we propose a Bayesian hierarchical copula model (BHCM) to accommodate hierarchical structures of dependent data, where the subject-level dependence is modeled by the copula-based model and the hierarchical structure is described using random dependence parameters. We introduce a layer-by-layer sampling scheme for conducting Bayesian inferences. Our proposed BHCM enjoys the flexibility of modeling various complex association structures, while retaining manageable computation. Extensive simulation studies show that our proposed estimators outperform conventional likelihood-based estimators in a variety of finite sample settings. We apply the BHCM to analyze the Vertebral Column dataset arising from UCI Machine Learning Repository.

Published
2020

5. A Vine Copula Model for Climate Trend Analysis using Canadian Temperature Data.

Author

HAOXIN ZHUANG, LIQUN DIAO, and YI, GRACE Y.

Subjects
*ATMOSPHERIC models, *ATMOSPHERIC temperature, *CLIMBING plants, *COPULA functions, *CLIMATE change, *SURFACE temperature, *TREND analysis
Abstract

Climate change is widely recognized as one of the most challenging, urgent and complex problem facing humanity. There are rising interests in understanding and quantifying climate changing. We analyze the climate trend in Canada using Canadian monthly surface air temperature, which is longitudinal data in nature with long time span. Analysis of such data is challenging due to the complexity of modeling and associated computation burdens. In this paper, we divide this type of longitudinal data into time blocks, conduct multivariate regression and utilize a vine copula model to account for the dependence among the multivariate error terms. This vine copula model allows separate specification of within-block and between-block dependence structure and has great flexibility of modeling complex association structures. To release the computational burden and concentrate on the structure of interest, we construct composite likelihood functions, which leave the connecting structure between time blocks unspecified. We discuss different estimation procedures and issues regarding model selection and prediction. We explore the prediction performance of our vine copula model by extensive simulation studies. An analysis of the Canada climate dataset is provided. [ABSTRACT FROM AUTHOR]

Published
2021

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