Principal component regression for spatial data
نویسندگان
چکیده
منابع مشابه
Regularized Principal Component Analysis for Spatial Data
Abstract: In many atmospheric and earth sciences, it is of interest to identify dominant spatial patterns of variation based on data observed at p locations with n repeated measurements. While principal component analysis (PCA) is commonly applied to find the patterns, the eigenimages produced from PCA may be noisy or exhibit patterns that are not physically meaningful when p is large relative ...
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ژورنال
عنوان ژورنال: Korean Journal of Applied Statistics
سال: 2017
ISSN: 1225-066X
DOI: 10.5351/kjas.2017.30.3.311