TY - JOUR

T1 - Graphical tool of sparse factor analysis

AU - Yamamoto, Michio

AU - Hirose, Kei

AU - Nagata, Haruhisa

N1 - Funding Information:
We thank the Editor and an anonymous reviewer for their constructive comments that helped to improve the quality of this article. This work was partially supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Numbers 26730016 and 15K15949.
Publisher Copyright:
© 2017, The Behaviormetric Society.

PY - 2017/1/1

Y1 - 2017/1/1

N2 - Factor analysis is a popular statistical model for analyzing correlation structures among observed variables. It is well known that this model has a rotational indeterminacy. Traditionally, the model parameters are estimated by the maximum likelihood method; then, factor rotation methods are applied to obtain an interpretable factor loading matrix. Recently, several sparse estimation procedures via penalization have been developed. Sparse estimation via penalization is an alternative to the factor rotation; it leads to an interpretable and sufficiently sparse solution. In this paper, we give an overview of several sparse factor analysis models, followed by a discussion of a relation between ordinary factor rotation and penalized maximum likelihood approaches. Then, we introduce a novel analyzing tool wherein a user can select a model that is easy to interpret and also possesses desirable values of goodness-of-fit indices based on the graphical representation of solution path.

AB - Factor analysis is a popular statistical model for analyzing correlation structures among observed variables. It is well known that this model has a rotational indeterminacy. Traditionally, the model parameters are estimated by the maximum likelihood method; then, factor rotation methods are applied to obtain an interpretable factor loading matrix. Recently, several sparse estimation procedures via penalization have been developed. Sparse estimation via penalization is an alternative to the factor rotation; it leads to an interpretable and sufficiently sparse solution. In this paper, we give an overview of several sparse factor analysis models, followed by a discussion of a relation between ordinary factor rotation and penalized maximum likelihood approaches. Then, we introduce a novel analyzing tool wherein a user can select a model that is easy to interpret and also possesses desirable values of goodness-of-fit indices based on the graphical representation of solution path.

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U2 - 10.1007/s41237-016-0007-3

DO - 10.1007/s41237-016-0007-3

M3 - Article

AN - SCOPUS:85060613111

SN - 0385-7417

VL - 44

SP - 229

EP - 250

JO - Behaviormetrika

JF - Behaviormetrika

IS - 1

ER -