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Journal of Data Science


A Vine Copula Model for Climate Trend Analysis using Canadian Temperature Data
Volume 19, Issue 1 (2021), pp. 37–55
Pub. online: 28 January 2021      Type: Statistical Data Science     
Received
1 July 2020
Accepted
1 December 2020
Published
28 January 2021

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.

Supplementary material

 Supplementary Material

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Keywords
climate change composite likelihood longitudinal data prediction

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