A Matrix-Free Likelihood Method for Exploratory Factor Analysis of High-Dimensional Gaussian Data | PERPUSTAKAAN UNIVERSITAS KATOLIK PARAHYANGAN

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A Matrix-Free Likelihood Method for Exploratory Factor Analysis of High-Dimensional Gaussian Data

Dai, Fan - Nama Orang; Maitra, Ranjan - Nama Orang; Dutta, Somak - Nama Orang;

This technical note proposes a novel profile likelihood method for estimating the covariance parameters in exploratory factor analysis of high-dimensional Gaussian datasets with fewer observations than number of variables. An implicitly restarted Lanczos algorithm and a limited-memory quasi-Newton method are implemented to develop a matrix-free framework for likelihood maximization. Simulation results show that our method is substantially faster than the expectation-maximization solution without sacrificing accuracy. Our method is applied to fit factor models on data from suicide attempters, suicide ideators, and a control group. Supplementary materials for this article are available online.

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Informasi Detail

Judul Seri: JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS; Vol.29 No.3 September 2020
No. Panggil: -
Penerbit: : .,
Deskripsi Fisik: p. 675 - 680
Bahasa: English
ISBN/ISSN: -
Klasifikasi: -
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: FMRI
EM ALGORITHM
PROFILE LIKELIHOOD
LANCZOS ALGORITHM
L-BFGS-B
Info Detail Spesifik: -
Pernyataan Tanggungjawab: Fan Dai, Somak Dutta, Ranjan Maitra

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