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We demonstrate, on both simulated and real data sets, how this approach can be used to perform linear, logistic and censored regression with functional predictors. In addition, we show how functional principal components can be used to gain insight into the relationship between the response and functional predictors. Finally, we extend the methodology to apply generalized linear models and principal components to standard missing data problems.<\/jats:p>","DOI":"10.1111\/1467-9868.00342","type":"journal-article","created":{"date-parts":[[2003,3,12]],"date-time":"2003-03-12T14:37:22Z","timestamp":1047479842000},"page":"411-432","source":"Crossref","is-referenced-by-count":319,"title":["Generalized Linear Models with Functional Predictors"],"prefix":"10.1093","volume":"64","author":[{"given":"Gareth M.","family":"James","sequence":"first","affiliation":[{"name":"University of Southern California , Los Angeles , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2002,8,12]]},"reference":[{"key":"2023040109112512000_","doi-asserted-by":"crossref","first-page":"95","DOI":"10.2307\/2986114","article-title":"Logistic regression for correlated binary data","volume":"43","author":"Le Cessie","year":"1994","journal-title":"Appl. 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