Finite sample theory for high-dimensional functional/scalar time series with applications
Author:
Affiliation:
1. Department of Statistics, London School of Economics, London, WC2A 2AE, U.K
2. Institute of Statistics and Big Data, Renmin University of China, Beijing, 100872, P.R. China
Publisher
Institute of Mathematical Statistics
Subject
Statistics and Probability,Statistics, Probability and Uncertainty
Reference39 articles.
1. Fan, Y., James, G. M. and Radchenko, P. (2015). Functional additive regression. Ann. Statist. 43 2296–2325.
2. Panaretos, V. M. and Tavakoli, S. (2013). Fourier analysis of stationary time series in function space. Ann. Statist. 41 568–603.
3. Aue, A., Norinho, D. D. and Hörmann, S. (2015). On the prediction of stationary functional time series. J. Amer. Statist. Assoc. 110 378–392.
4. Bathia, N., Yao, Q. and Ziegelmann, F. (2010). Identifying the finite dimensionality of curve time series. Ann. Statist. 38 3352–3386.
5. Bosq, D. (2000). Linear processes in function spaces. Lecture Notes in Statistics 149. Springer-Verlag, New York Theory and applications.
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