Quotation Hosszejni, Darjus, Frühwirth-Schnatter, Sylvia. 2022. On Statistical Inference in Factor Analysis: Polynomial-Time Verification of the Anderson-Rubin Condition. ISBA World Meeting, Montreal, Canada, 26.06.-01.07.


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Abstract

In sparse factor models, identifiability of the variance decomposition has received little attention perhaps due to its difficulty. Anderson and Rubin (1956) famously establish identifiability under a rank assumption on a large number of submatrices of the factor loading matrix. This number is potentially exponential in the input dimensions and identifiability is therefore infeasible to verify for computers. In our paper, the computational complexity is reduced to the speedy inspection of just one special rotation, the generalized lower triangular (GLT) form. As part of exploratory factor analysis, our method is deployed to avoid nonsensical models independently of observation ordering in both Bayesian and frequentist contexts. Furthermore, a fully Bayesian sampling procedure is developed, which leverages on the GLT rotation while estimating the unknown number of latent factors. The procedure is applied to financial and economic data. Joint work with Sylvia Frühwirth-Schnatter.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Paper presented at an academic conference or symposium
Language English
Title On Statistical Inference in Factor Analysis: Polynomial-Time Verification of the Anderson-Rubin Condition
Event ISBA World Meeting
Year 2022
Date 26.06.-01.07.
Country Canada
Location Montreal

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People
Hosszejni, Darjus (Details)
Frühwirth-Schnatter, Sylvia (Details)
Organization
Institute for Statistics and Mathematics IN (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
1104 Applied mathematics (Details)
1162 Statistics (Details)
5323 Econometrics (Details)
5701 Applied statistics (Details)
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