TY - GEN

T1 - Semiparametric Weighting Estimations of a Zero-Inflated Poisson Regression with Missing in Covariates

AU - Lukusa, M. T.

AU - Phoa, F. K.H.

N1 - Funding Information:
Acknowledgments The authors gratefully thank Miss Ula Tzu-Ning Kung for improving the English in this paper. We are also thankful to the referee for the constructive comments and suggestions that have improved considerably the quality of this manuscript. This work was supported by the Career Development Award of Academia Sinica (Taiwan) grant number 103-CDA-M04 and the Ministry of Science and Technology (Taiwan) grant numbers 105-2118-M-001-007-MY2, 107-2118-M-001-011-MY3 and 107-2321-B-001-038.
Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

PY - 2020

Y1 - 2020

N2 - We scrutinize the problem of missing covariates in the zero-inflated Poisson regression model. Under the assumption that some covariates for modeling the probability of the zero and the nonzero states are missing at random, the complete-case estimator is known to be biased and inefficient. Although the inverse probability weighting estimator is unbiased, it remains inefficient. We propose four types of semiparametric weighting estimations where the conditional probabilities and the conditional expected score functions are estimated either by using the generalized additive models (GAMs) and the Nadaraya kernel smoother method. In addition, we allow the conditional probabilities and the conditional expectations to be either of the same types or of different types. Moreover, a Monte Carlo experiment is used to investigate the merit of the proposed method.

AB - We scrutinize the problem of missing covariates in the zero-inflated Poisson regression model. Under the assumption that some covariates for modeling the probability of the zero and the nonzero states are missing at random, the complete-case estimator is known to be biased and inefficient. Although the inverse probability weighting estimator is unbiased, it remains inefficient. We propose four types of semiparametric weighting estimations where the conditional probabilities and the conditional expected score functions are estimated either by using the generalized additive models (GAMs) and the Nadaraya kernel smoother method. In addition, we allow the conditional probabilities and the conditional expectations to be either of the same types or of different types. Moreover, a Monte Carlo experiment is used to investigate the merit of the proposed method.

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U2 - 10.1007/978-3-030-57306-5_30

DO - 10.1007/978-3-030-57306-5_30

M3 - Conference contribution

AN - SCOPUS:85097284603

SN - 9783030573058

T3 - Springer Proceedings in Mathematics and Statistics

SP - 329

EP - 339

BT - Nonparametric Statistics - 4th ISNPS 2018

A2 - La Rocca, Michele

A2 - Liseo, Brunero

A2 - Salmaso, Luigi

PB - Springer

T2 - 4th Conference of the International Society for Nonparametric Statistics, ISNPS 2018

Y2 - 11 June 2018 through 15 June 2018

ER -