%A Shikui TU, Lei XU %T Parameterizations make different model selections: Empirical findings from factor analysis %0 Journal Article %D 0 %J Front. Electr. Electron. Eng. %J Frontiers of Electrical and Electronic Engineering %@ 2095-2732 %R 10.1007/s11460-011-0150-2 %P 256-274 %V %N %U {https://journal.hep.com.cn/fee/EN/10.1007/s11460-011-0150-2 %8 2011-06-05 %X
How parameterizations affect model selection performance is an issue that has been ignored or seldom studied since traditional model selection criteria, such as Akaike’s information criterion (AIC), Schwarz’s Bayesian information criterion (BIC), difference of negative log-likelihood (DNLL), etc., perform equivalently on different parameterizations that have equivalent likelihood functions. For factor analysis (FA), in addition to one traditional model (shortly denoted by FA-a), it was previously found that there is another parameterization (shortly denoted by FA-b) and the Bayesian Ying-Yang (BYY) harmony learning gets different model selection performances on FA-a and FA-b. This paper investigates a family of FA parameterizations that have equivalent likelihood functions, where each one (shortly denoted by FA-