首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Computational approaches to estimation in the principal component analysis of a stochastic process
Authors:Ana M Aguilera  Ramn Gutirrez  Francisco A Ocaa  Mariano J Valderrama
Institution:Ana M. Aguilera,Ramón Gutiérrez,Francisco A. Ocaña,Mariano J. Valderrama
Abstract:After performing a review of the classical procedures for estimation in the principal component analysis (PCA) of a second order stochastic process, two alternative procedures have been developed to approach such estimates. The first is based on the orthogonal projection method and uses cubic interpolating splines when the data are discrete. The second is based on the trapezoidal method. The accuracy of both procedures is tested by simulating approximated sample-functions of the Brownian motion and the Brownian bridge. The real principal factors of these stochastic processes, which can be evaluated directly, are compared with those estimated by means of the two mentioned algorithms. An application for estimation in the PCA of tourism evolution in Spain from real data is also included.
Keywords:principal components  Karhunen-Loè  ve expansion  orthogonal projection  trapezoidal algorithm  Brownian motion  Brownian bridge
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号