Superiority of Bayesian Imputation to Mice in Logit Panel Data Models
نویسندگان
چکیده
Non-responses leading to missing data are common in most studies and causes inefficient biased statistical inferences if ignored. When faced with data, many choose employ complete case analysis approach estimate the parameters of model. This however compromises on susceptibility estimates reduced bias minimum variance as expected. Several classical model based techniques imputing values have been mentioned literature. Bayesian missingness is deemed superior amongst other through its natural self-lending settings where treated unobserved random variables that a distribution which depends observed data. paper digs up superiority imputation Multiple Imputation Chained Equations (MICE) when estimating logistic panel models single fixed effects. The study validates conditional maximum likelihood for nonlinear binary choice logit presence observations. A Monte Carlo simulation was designed determine magnitude root mean square errors (RMSE) arising from MICE Full imputation. results show (ML) estimator presented this less more efficient performed curb non-responses.
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ژورنال
عنوان ژورنال: Open Journal of Statistics
سال: 2023
ISSN: ['2161-7198', '2161-718X']
DOI: https://doi.org/10.4236/ojs.2023.133017