Date of Award
8-2017
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Mathematical Sciences
Committee Member
Dr. Robert Lund, Committee Chair
Committee Member
Dr. Peter Kiessler
Committee Member
Dr. Colin Gallagher
Committee Member
Dr. Xiaoqian Sun
Abstract
Climatological time series are often periodically and spatially correlated. High dimensionality issues arise when modeling periodically and spatially correlated time series data "“ often, even simple multivariate models have more parameters than data points. This dissertation develops parsimonious methods for fitting periodically and spatially correlated multivariate time series data. Parsimonious VAR (vector autoregressive) and PVAR (periodic VAR) models are pursued here. The layered procedure introduced by Lund et al. (1995) is adopted as a basic scheme, which removes periodic correlation from the data in the first layer, and fits a stationary VAR model in the second layer. The method is applied to a daily maximum temperature data set of seven cities in southeastern U.S.. In addition, a portmanteau test is proposed for diagnosing serial correlations in periodic multivariate residuals. The performance of the test is examined in simulated data.
Recommended Citation
Xia, Yanbo, "Parsimonious Space-Time Temperature Series Modeling" (2017). All Dissertations. 2009.
https://tigerprints.clemson.edu/all_dissertations/2009