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http://dx.doi.org/10.5351/KJAS.2017.30.3.311

Principal component regression for spatial data  

Lim, Yaeji (Department of Statistics, Pukyong National University)
Publication Information
The Korean Journal of Applied Statistics / v.30, no.3, 2017 , pp. 311-321 More about this Journal
Abstract
Principal component analysis is a popular statistical method to reduce the dimension of the high dimensional climate data and to extract meaningful climate patterns. Based on the principal component analysis, we can further apply a regression approach for the linear prediction of future climate, termed as principal component regression (PCR). In this paper, we develop a new PCR method based on the regularized principal component analysis for spatial data proposed by Wang and Huang (2016) to account spatial feature of the climate data. We apply the proposed method to temperature prediction in the East Asia region and compare the result with conventional PCR results.
Keywords
principal component analysis; empirical orthogonal functions; principal component regression; spatial data; temperature prediction;
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