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A new approach for selecting the number of factors

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  • Chen, Yin-Ping
  • Huang, Hsin-Cheng
  • Tu, I-Ping

Abstract

In factor analysis, it is critical to determine the number of factors. A new approach to select the number of factors based on unbiased risk estimation is introduced. This approach utilizes a concept, called generalized degrees of freedom (GDF), originally proposed for model selection in regression. A data perturbation technique is applied for estimating GDF. Simulation experiments show that the proposed method performs better than some commonly used methods.

Suggested Citation

  • Chen, Yin-Ping & Huang, Hsin-Cheng & Tu, I-Ping, 2010. "A new approach for selecting the number of factors," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 2990-2998, December.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:12:p:2990-2998
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    References listed on IDEAS

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    Cited by:

    1. Kim, Hyun Hak & Swanson, Norman R., 2018. "Mining big data using parsimonious factor, machine learning, variable selection and shrinkage methods," International Journal of Forecasting, Elsevier, vol. 34(2), pages 339-354.
    2. Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(3), pages 695-746, August.
    3. Tu, I-Ping & Huang, Su-Yun & Hsieh, Dai-Ni, 2019. "The generalized degrees of freedom of multilinear principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 26-37.
    4. Jonathan Bradley & Noel Cressie & Tao Shi, 2015. "Comparing and selecting spatial predictors using local criteria," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 1-28, March.
    5. Jonathan Bradley & Noel Cressie & Tao Shi, 2015. "Rejoinder on: Comparing and selecting spatial predictors using local criteria," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(1), pages 54-60, March.
    6. Bada, Oualid & Kneip, Alois, 2014. "Parameter cascading for panel models with unknown number of unobserved factors: An application to the credit spread puzzle," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 95-115.

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