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Exploratory factor analysis—Parameter estimation and scores prediction with high-dimensional data

Rolf Sundberg, Uwe Feldmann
2016 Journal of Multivariate Analysis  
. • For large p, factor scores can be estimated with high precision (n need not be large). • For large p, an old iteration method converges fast and with no inadmissible values. a b s t r a c t In an approach  ...  aiming at high-dimensional situations, we first introduce a distribution-free approach to parameter estimation in the standard random factor model, that is shown to lead to the same estimating equations  ...  Acknowledgment We are grateful to an anonymous referee for valuable and constructive criticism.  ... 
doi:10.1016/j.jmva.2016.02.013 fatcat:onux6lsiozhvxazj2oria6rrfe

Bayesian exploratory factor analysis

Gabriella Conti, Sylvia Frühwirth-Schnatter, James J. Heckman, Rémi Piatek
2014 Journal of Econometrics  
Bayesian Exploratory Factor Analysis * This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches.  ...  The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements. JEL Classification: C11, C38, C63  ...  Classical Exploratory Factor Analysis is widely used to boil down high dimensional data on psychological traits to interpretable scales.  ... 
doi:10.1016/j.jeconom.2014.06.008 pmid:25431517 pmcid:PMC4242469 fatcat:7xtvkc2opbfd3axcypiak3lpdu

A Deep Learning Algorithm for High-Dimensional Exploratory Item Factor Analysis [article]

Christopher J. Urban, Daniel J. Bauer
2021 arXiv   pre-print
In this work, we investigate a deep learning-based VI algorithm for exploratory item factor analysis (IFA) that is computationally fast even in large data sets with many latent factors.  ...  We provide a real data application that recovers results aligning with psychological theory across random starts.  ...  Other marginal likelihood-based parameter estimation methods for exploratory IFA avoid approximating high-dimensional integrals and are therefore more computationally efficient.  ... 
arXiv:2001.07859v4 fatcat:5hitjvjpurdrxfshevgtskqlzq

Exploratory Tobit Factor Analysis for Multivariate Censored Data

Wagner A. Kamakura, Michel Wedel
2001 Multivariate Behavioral Research  
In addition, the factor model parameters lend themselves to substantive interpretation and graphical display. The models are estimated with simulated maximum likelihood.  ...  Such models are particularly useful in the exploratory analysis of multivariate censored data and the identification of latent variables from behavioral data.  ...  With our model one can deal with exploratory and confirmatory ML factor analysis of data with mixed type variables, in which a (large) number of observations equal to zero.  ... 
doi:10.1207/s15327906mbr3601_03 fatcat:jwpfqivgavcdbjoksv7jsaysxa

Exploratory Factor Analysis of Data on a Sphere [article]

Fan Dai and Karin S. Dorman and Somak Dutta and Ranjan Maitra
2021 arXiv   pre-print
We develop exploratory factor analysis of the projected normal distribution to explain the variability in such data using a few easily interpreted latent factors.  ...  Data on high-dimensional spheres arise frequently in many disciplines either naturally or as a consequence of preliminary processing and can have intricate dependence structure that needs to be understood  ...  ACKNOWLEDGMENTS The research was supported in part by the United States Department of Agriculture (USDA)/National Institute of Food and Agriculture (NIFA), Hatch projects IOW03617 and IOW03717.  ... 
arXiv:2111.04940v1 fatcat:ftpxt6jtz5h6bjlgn26gz4gxuu

Simultaneous Parameter Estimation in Exploratory Factor Analysis: An Expository Review

Steffen Unkel, Nickolay T. Trendafilov
2010 International Statistical Review  
The classical exploratory factor analysis (EFA) finds estimates for the factor loadings matrix and the matrix of unique factor variances which give the best fit to the sample correlation matrix with respect  ...  Common factor scores can be obtained as a function of these estimates and the data.  ...  Acknowledgements The authors are grateful to an anonymous reviewer and the Editor for their helpful comments on the first draft of this paper.  ... 
doi:10.1111/j.1751-5823.2010.00120.x fatcat:bm55wle4w5d3pmlgew6besblza

High-dimensional Exploratory Item Factor Analysis by A Metropolis–Hastings Robbins–Monro Algorithm

Li Cai
2009 Psychometrika  
A Metropolis-Hastings Robbins-Monro (MH-RM) algorithm for high-dimensional maximum marginal likelihood exploratory item factor analysis is proposed.  ...  It is shown that when the dimensionality is high, MH-RM has advantages over existing methods such as numerical quadrature based EM algorithm.  ...  JML computations iterate between two stages that are similar to the first and last stages in MH-RM: (1) replacing the unobserved factor scores with modal estimates given current item parameters, and (2  ... 
doi:10.1007/s11336-009-9136-x fatcat:52s3nwrd6vb6hkl2s3uzzmyzpu

Exploratory Factor Analysis of Wireline Logs Using a Float-Encoded Genetic Algorithm

Norbert Péter Szabó, Mihály Dobróka
2017 Mathematical Geosciences  
With suitably chosen genetic operators, 82 the factor loadings and scores are estimated in a convergent iterative procedure.  ...  Factor analysis (FA) is applicable to reduce 64 the dimensionality of statistical problems and extract non-measurable information 65 from large-scale data sets (Lawley and Maxwell 1962).  ...  Estimated parameters are: theoretical data calculated from the factor scores (TH), first scaled factor (FAC TO R 1 ), second and third factors (FAC TO R 2 , FAC TO R 3 ), shale volume estimated by Larionov  ... 
doi:10.1007/s11004-017-9714-x fatcat:niwnyivoi5gdxg7zsci2vj2fhm

Fitting Exploratory Factor Analysis Models with High Dimensional Psychological Data

W. Holmes Finch, Maria E. Hernández Finch
2021 Journal of Data Science  
The purpose of the current study was to investigate and compare some alternative approaches to fitting EFA in the case of small samples and high dimensional data.  ...  Objectives: Exploratory Factor Analysis (EFA) is a very popular statistical technique for identifying potential latent structure underlying a set of observed indicator variables.  ...  Methods for high dimensional factor analysis A number of statistical methods have been suggested for use in exploratory latent variable modeling with high dimensional datasets.  ... 
doi:10.6339/jds.201607_14(3).0008 fatcat:vs3kf3yxxrb4beka4rthqovuty

Exploratory factor analysis with structured residuals for brain network data

Erik-Jan van Kesteren, Rogier A. Kievit
2020 Network Neuroscience  
Dimension reduction is widely used and often necessary to make network analyses and their interpretation tractable by reducing high dimensional data to a small number of underlying variables.  ...  Techniques such as Exploratory Factor Analysis (EFA) are used by neuroscientists to reduce measurements from a large number of brain regions to a tractable number of factors.  ...  of the high-dimensional raw data by the low-dimensional factors.  ... 
doi:10.1162/netn_a_00162 pmid:33688604 pmcid:PMC7935039 fatcat:hrqi527uirbltexo6b4x2t3xme

Exploratory Factor Analysis with Structured Residuals for Brain Imaging Data [article]

Erik-Jan van Kesteren, Rogier A. Kievit
2020 bioRxiv   pre-print
Dimension reduction is widely used and often necessary to reduce high dimensional data to a small number of underlying variables -- factors or components -- to make data analyses and their interpretation  ...  Here, a) we show the adverse consequences of ignoring such a priori structure in standard factor analysis, b) we propose a technique for Exploratory factor analysis with structured residuals (EFAST) which  ...  Acknowledgements We would like to thank Yves Rosseel for valuable input and the development of key tools, Linda Geerligs for providing the functional connectivity data, and Jonathan Helm and Øystein Sørensen  ... 
doi:10.1101/2020.02.06.933689 fatcat:yisfvxs2fbdcdmlusmvpgbrlfe

Exploratory Structural Equation Modeling: An Integration of the Best Features of Exploratory and Confirmatory Factor Analysis

Herbert W. Marsh, Alexandre J.S. Morin, Philip D. Parker, Gurvinder Kaur
2014 Annual Review of Clinical Psychology  
Keywords exploratory and confirmatory factor analysis, exploratory structural equation models, exploratory structural equation model within confirmatory factor analysis, multiple-indicator multiple-cause  ...  (MIMIC) models, multitrait-multimethod models, bifactor models Abstract Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), path analysis, and structural equation modeling (SEM)  ...  , Andrew Martin, Benjamin Nagengast, Alexander Robitzsch, and Ulrich Trautwein) for helpful comments at earlier stages of this research.  ... 
doi:10.1146/annurev-clinpsy-032813-153700 pmid:24313568 fatcat:j6e57fni6bgb3oy2qpffwqkdg4

Modeling multiple phenotypes in wheat using data‐driven genomic exploratory factor analysis and Bayesian network learning

Mehdi Momen, Madhav Bhatta, Waseem Hussain, Haipeng Yu, Gota Morota
2021 Plant Direct  
This study shows that data-driven exploratory factor analysis can reveal smaller dimensional common latent phenotypes that are likely to give rise to numerous observed field phenotypes without relying  ...  The objectives of this study were to illustrate the manner in which data-driven exploratory factor analysis can map observed phenotypes into a smaller number of latent variables and infer a genomic latent  ...  Exploratory factor analysis estimates the phenotypefactor relationship from the data by allowing cross-loading.  ... 
doi:10.1002/pld3.304 pmid:33532691 pmcid:PMC7833463 fatcat:lllpeyy2kvbc5gj6ccoqhadhuq

Inferring constructs of effective teaching from classroom observations: An application of Bayesian exploratory factor analysis without restrictions

J. R. Lockwood, Terrance D. Savitsky, Daniel F. McCaffrey
2015 Annals of Applied Statistics  
We make inferences about these constructs using a novel approach to Bayesian exploratory factor analysis (EFA) that, unlike commonly used approaches for identifying factor loadings in Bayesian EFA, is  ...  constructs of high-quality teaching.  ...  SUPPLEMENTARY MATERIAL Supplement to "Inferring constructs of effective teaching from classroom observations: An application of Bayesian exploratory factor analysis with-  ... 
doi:10.1214/15-aoas833 fatcat:qgy5yjuuvndkrlkh55yh5a2wuq

Determining Dimensionality of Exercise Readiness Using Exploratory Factor Analysis

Kelley Strohacker, Rebecca A Zakrajsek
2016 Journal of Sports Science and Medicine  
The purpose of this study was to assess construct dimensionality of exercise readiness using exploratory factor analysis.  ...  A principal axis factor analysis was conducted with 41 items using oblique rotation (promax).  ...  Whitney Breslin for her assistance with survey data collection. All procedures in the current study comply with U.S. laws and local institutional review board guidelines for scientific research.  ... 
pmid:27274659 pmcid:PMC4879435 fatcat:iswjeblksnalhfln7qsdudzfoi
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