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A Mode-Jumping Algorithm for Bayesian Factor Analysis

Exploratory factor analysis is a dimension-reduction technique commonly used in psychology, finance, genomics, neuroscience, and economics. Advances in computational power have opened the door for fully Bayesian treatments of factor analysis. One open problem is enforcing rotational identifability of the latent factor loadings, as the loadings are not identified from the likelihood without further restrictions. Nonidentifability of the loadings can cause posterior multimodality, which can produce misleading posterior summaries. The positive-diagonal, lower-triangular (PLT) constraint is the most commonly used restriction to guarantee identifiability, in which the upper m × m submatrix of the loadings is constrained to be a lower-triangular matrix with positive-diagonal elements. The PLT constraint can fail to guarantee identifiability if the constrained submatrix is singular. Furthermore, though the PLT constraint addresses identifiability-related multimodality, it introduces additional mixing issues. We introduce a new Bayesian sampling algorithm that efficiently explores the multimodal posterior surface and addresses issues with PLT-constrained approaches. Supplementary materials for this article are available online.

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Informasi Detail

Judul Seri

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION; Vol.117 No.537 March 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 277-290

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MIXING
IDENTIFIABILITY
MARKOV CHAIN MONTE CARLO
FACTOR MODEL
ORTHOGONAL ROTATION
POSTERIOR MULTIMODALITY

Info Detail Spesifik

https://doi.org/10.1080/01621459.2020.1773833

Pernyataan Tanggungjawab

Albert Xingyi Man, Steven Andrew Culpepper

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