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A note on the weighting-type estimations of the zero-inflated Poisson regression model with missing data in covariates

Author

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  • Lukusa, Martin T.
  • Phoa, Frederick Kin Hing

Abstract

A two-step weighting type method is proposed when some covariates in the zero-inflated Poisson model are missing at random. Semiparametric estimator and parametric estimator weighting-types are proposed accordingly. Their limit behaviors are studied theoretically and numerically.

Suggested Citation

  • Lukusa, Martin T. & Phoa, Frederick Kin Hing, 2020. "A note on the weighting-type estimations of the zero-inflated Poisson regression model with missing data in covariates," Statistics & Probability Letters, Elsevier, vol. 158(C).
    • Handle: RePEc:eee:stapro:v:158:y:2020:i:c:s0167715219303001
    DOI: 10.1016/j.spl.2019.108654
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    References listed on IDEAS

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    1. Chen, Xue-Dong & Fu, Ying-Zi, 2011. "Model selection for zero-inflated regression with missing covariates," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 765-773, January.
    2. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    3. Newey, Whitney K., 1994. "Kernel Estimation of Partial Means and a General Variance Estimator," Econometric Theory, Cambridge University Press, vol. 10(2), pages 1-21, June.
    4. T. Martin Lukusa & Shen-Ming Lee & Chin-Shang Li, 2016. "Semiparametric estimation of a zero-inflated Poisson regression model with missing covariates," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(4), pages 457-483, May.
    5. Wang, Suojin & Wang, C. Y., 2001. "A note on kernel assisted estimators in missing covariate regression," Statistics & Probability Letters, Elsevier, vol. 55(4), pages 439-449, December.
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    Cited by:

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    2. Phuoc-Loc Tran & Shen-Ming Lee & Truong-Nhat Le & Chin-Shang Li, 2025. "Large-sample properties of multiple imputation estimators for parameters of logistic regression with covariates missing at random separately or simultaneously," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 77(2), pages 251-287, April.

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