乐胖代购免代理版

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The problem we tackle in this paper consists of learning a linear regression function from multivariate input and output streaming data in an incremental fashion while also performing dimensionality reduction and variable selection. When input and output streams are high\u2010dimensional and correlated, it is plausible to assume the existence of hidden factors that explain a large proportion of the covariance between them. The methods we propose build on recursive partial least squares (PLS) regression. The hidden factors are dynamically inferred and tracked over time and, within each factor, the most important streams are recursively identified by means of sparse matrix decompositions. Moreover, the recursive regression model is able to adapt to sudden changes in the data generating mechanism and also identifies the number of latent factors. Extensive simulation results illustrate how the methods perform and compare with alternative penalized regression models for streaming data. We also apply the algorithm to solve a multivariate version of theenhanced index tracking<\/jats:italic>problem in computational finance. Copyright \u00a9 2010 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 3: 170\u2010193, 2010<\/jats:p>","DOI":"10.1002\/sam.10074","type":"journal-article","created":{"date-parts":[[2010,5,12]],"date-time":"2010-05-12T21:30:12Z","timestamp":1273699812000},"page":"170-193","source":"Crossref","is-referenced-by-count":8,"title":["Sparse partial least squares regression for on\u2010line variable selection with multivariate data streams"],"prefix":"10.1002","volume":"3","author":[{"given":"Brian","family":"McWilliams","sequence":"first","affiliation":[]},{"given":"Giovanni","family":"Montana","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2010,5,12]]},"reference":[{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2005.10.021"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2005.07.003"},{"key":"e_1_2_8_4_2","doi-asserted-by":"crossref","unstructured":"Y.ZhuandD.Shasha Statstream: statistical monitoring of thousands of data streams in real time In Proceedings of the 28th VLDB Conference Hong Kong China 2002.","DOI":"10.1016\/B978-155860869-6\/50039-1"},{"issue":"1","key":"e_1_2_8_5_2","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","year":"1996","journal-title":"J Roy Stat Soc B"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1214\/009053604000000067"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.1214\/07-AOAS131"},{"key":"e_1_2_8_8_2","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.1080.0986"},{"key":"e_1_2_8_9_2","doi-asserted-by":"crossref","unstructured":"J.Brodie I.Daubechies C.De Mol C.Giannone andI.Loris Sparse and stable Markowitz portfolios. 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