A robust partial least squares approach for function-on-function regression
Author:
Affiliation:
1. Department of Statistics, Marmara University, 34722, Kadikoy-Istanbul, Turkey
2. Department of Actuarial Studies and Business Analytics, Level 7, 4 Eastern Road, Macquarie University, Sydney, New South Wales 2109, Australia
Publisher
Institute of Mathematical Statistics
Subject
Statistics and Probability
Reference61 articles.
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3. Aguilera, A. M., Escabias, M., Preda, C. and Saporta, G. (2010). Using basis expansions for estimating functional PLS regression: Applications with chemometric data. Chemometrics and Intelligent Laboratory Systems 104, 289–305.
4. Aguilera, A. M., Escabias, M., Preda, C. and Saporta, G. (2016). Penalized versions of functional PLS regression. Chemometrics and Intelligent Laboratory Systems 154, 80–92.
5. Aguilera, A. M., Ocana, F. A. and Valderrama, M. J. (1999). Forecasting with unequally spaced data by a functional principal component approach. Test 8, 233–254.
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