Vintage Factor Analysis with Varimax Performs Statistical Inference

نویسندگان

چکیده

Abstract Psychologists developed Multiple Factor Analysis to decompose multivariate data into a small number of interpretable factors without any priori knowledge about those [Thurstone, 1935]. In this form factor analysis, the Varimax rotation redraws axes through multidimensional make them sparse and thus more [Kaiser, 1958]. Charles Spearman many others objected rotations because seem be rotationally invariant 1947, Anderson Rubin, 1956]. These objections are still reported in all contemporary statistics textbooks. However, vintage analysis has survived is widely popular because, empirically, often makes easier interpret. We argue that interpret fact, performs statistical inference. show Principal Components (PCA) with provides unified spectral estimation strategy for broad class semi-parametric models, including Stochastic Blockmodel natural variation Latent Dirichlet Allocation (i.e., \topic modeling"). addition, we Thurstone's employed sparsity diagnostics implicitly assess key leptokurtic condition statistically identifiable these models. Taken together, shows know-how Vintage inference, reversing nearly century thinking on topic. illustrate techniques use two large bibliometric examples (a citation network text corpus). With eigensolver, PCA both fast stable. Combined straightforward diagnostics, approach suitable wide array modern applications.

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ژورنال

عنوان ژورنال: Journal of The Royal Statistical Society Series B-statistical Methodology

سال: 2023

ISSN: ['1467-9868', '1369-7412']

DOI: https://doi.org/10.1093/jrsssb/qkad029