caa a22 4500 606241876 CHVBK 20210128101315.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s11336-013-9373-x doi (NATIONALLICENCE)springer-10.1007/s11336-013-9373-x Generalized Functional Extended Redundancy Analysis [Elektronische Daten] [Heungsun Hwang, Hye Suk, Yoshio Takane, Jang-Han Lee, Jooseop Lim] Functional extended redundancy analysis (FERA) was recently developed to integrate data reduction into functional linear models. This technique extracts a component from each of multiple sets of predictor data in such a way that the component accounts for the maximum variance of response data. Moreover, it permits predictor and/or response data to be functional. FERA can be of use in describing overall characteristics of each set of predictor data and in summarizing the relationships between predictor and response data. In this paper, we extend FERA into the framework of generalized linear models (GLM), so that it can deal with response data generated from a variety of distributions. Specifically, the proposed method reduces each set of predictor functions to a component and uses the component for explaining exponential-family responses. As in GLM, we specify the random, systematic, and link function parts of the proposed method. We develop an iterative algorithm to maximize a penalized log-likelihood criterion that is derived in combination with a basis function expansion approach. We conduct two simulation studies to investigate the performance of the proposed method based on synthetic data. In addition, we apply the proposed method to two examples to demonstrate its empirical usefulness. The Psychometric Society, 2013 functional data analysis nationallicence functional extended redundancy analysis nationallicence generalized linear models nationallicence data reduction nationallicence exponential family responses nationallicence penalized likelihood nationallicence Hwang Heungsun Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, H3A 1B1, Montreal, Quebec, Canada aut Suk Hye Department of Psychology, Arizona State University, Box 871104, 85287-1104, Tempe, AZ, USA aut Takane Yoshio Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, H3A 1B1, Montreal, Quebec, Canada aut Lee Jang-Han Chung-Ang University, Seoul, Korea aut Lim Jooseop Concordia University, Montreal, Quebec, Canada aut Psychometrika Springer US; http://www.springer-ny.com 80/1(2015-03-01), 101-125 0033-3123 80:1<101 2015 80 11336 https://doi.org/10.1007/s11336-013-9373-x text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11336-013-9373-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Hwang Heungsun Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, H3A 1B1, Montreal, Quebec, Canada aut NATIONALLICENCE 700 1- Suk Hye Department of Psychology, Arizona State University, Box 871104, 85287-1104, Tempe, AZ, USA aut NATIONALLICENCE 700 1- Takane Yoshio Department of Psychology, McGill University, 1205 Dr. Penfield Avenue, H3A 1B1, Montreal, Quebec, Canada aut NATIONALLICENCE 700 1- Lee Jang-Han Chung-Ang University, Seoul, Korea aut NATIONALLICENCE 700 1- Lim Jooseop Concordia University, Montreal, Quebec, Canada aut NATIONALLICENCE 773 0- Psychometrika Springer US; http://www.springer-ny.com 80/1(2015-03-01), 101-125 0033-3123 80:1<101 2015 80 11336