Additive Function-on-Function Regression | PERPUSTAKAAN UNIVERSITAS KATOLIK PARAHYANGAN

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Additive Function-on-Function Regression

Kim, Janet S. - Nama Orang; Ruppert, David - Nama Orang; Maity, Arnab - Nama Orang; Staicu, Ana-Maria - Nama Orang; Carroll, Raymond J. - Nama Orang;

We study additive function-on-function regression where the mean response at a particular time point depends on the time point itself, as well as the entire covariate trajectory. We develop a computationally efficient estimation methodology based on a novel combination of spline bases with an eigenbasis to represent the trivariate kernel function. We discuss prediction of a new response trajectory, propose an inference procedure that accounts for total variability in the predicted response curves, and construct pointwise prediction intervals. The estimation/inferential procedure accommodates realistic scenarios, such as correlated error structure as well as sparse and/or irregular designs. We investigate our methodology in finite sample size through simulations and two real data applications. Supplementary material for this article is available online.

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Barcode		Tipe Koleksi	Nomor Panggil	Lokasi	Status
art124955	null	Artikel		Gdg9-Lt3	Tersedia namun tidak untuk dipinjamkan - No Loan

Informasi Detail

Judul Seri: JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.27 No.1 March 2018
No. Panggil: -
Penerbit: : .,
Deskripsi Fisik: p. 234-244
Bahasa: English
ISBN/ISSN: -
Klasifikasi: -
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: FUNCTIONAL DATA ANALYSIS
PREDICTION
NONLINEAR MODELS
ORTHOGONAL PROJECTION
EIGENBASIS
PENALIZED B-SPLINES
Info Detail Spesifik: https://doi.org/10.1080/10618600.2017.1356730
Pernyataan Tanggungjawab: Janet S.Kim, Ana-Maria Staicu, Arnab Maity, Raymond J. Carroll, David Ruppert

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