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Data-cloning SMC2: A global optimizer for maximum likelihood estimation of latent variable models

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  • Duan, Jin-Chuan
  • Fulop, Andras
  • Hsieh, Yu-Wei

Abstract

A data-cloning SMC2 algorithm is proposed as a general-purpose, global optimization routine for the maximum likelihood estimation of models with latent variables. In the SMC2 phase, the method first marginalizes out the latent variable(s) by applying one layer of SMC at a fixed parameter value and then searches for the optimal parameters through another layer of SMC. The data-cloning phase is deployed to ensure global convergence by dampening multi-modality and to reduce the Monte Carlo error associated with SMC. This new method has broad applicability and is massively parallelizable through leveraging modern multi-core CPU or GPU computing.

Suggested Citation

  • Duan, Jin-Chuan & Fulop, Andras & Hsieh, Yu-Wei, 2020. "Data-cloning SMC2: A global optimizer for maximum likelihood estimation of latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    • Handle: RePEc:eee:csdana:v:143:y:2020:i:c:s0167947319301963
    DOI: 10.1016/j.csda.2019.106841
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    References listed on IDEAS

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    Cited by:

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    2. Duan, Jin-Chuan, 2021. "Sharing Credit Data While Respecting Privacy—A Digital Platform for Fairer Financing of MSMEs," ADBI Working Papers 1280, Asian Development Bank Institute.
    3. Beirne, John & Villafuerte, James & Zhang, Bryan (ed.), 2022. "Fintech and COVID-19: Impacts, Challenges, and Policy Priorities for Asia," ADBI Books, Asian Development Bank Institute, number 29, Décembre.

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