Wavelet estimation of density for censored data with censoring indicator missing at random

Zou, Yuye; Liang, Han-Ying

doi:10.1080/02331888.2017.1336170

Cited by 5 publications

(1 citation statement)

References 17 publications

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“…We conducted model validation studies using both simulation and real life COVID-19 patient data analysis and presented summary statistical results. Our novel method for bias reduction is robust as it relies on the penalization theory of the likelihood function to achieve the efficiency in the parameter estimates for the missing at random data mechanism [6,[31][32][33]. The fact that we penalized the log-likelihood function by the LogF(1, 1) prior and then, derived the closed form of the bias using the Cox and Snell's equation [19] shows how broadbased the novel method is.…”

Section: Discussionmentioning

confidence: 99%

Bias reduction in the logistic model parameters with the LogF(1,1) penalty under MAR assumption

Al-Shaaibi

Wesonga

2022

Front. Appl. Math. Stat.

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In this paper, we present a novel validated penalization method for bias reduction to estimate parameters for the logistic model when data are missing at random (MAR). Specific focus was given to address the data missingness problem among categorical model covariates. We penalize a logit log-likelihood with a novel prior distribution based on the family of the LogF(m,m) generalized distribution. The principle of expectation-maximization with weights was employed with the Louis' method to derive an information matrix, while a closed form for the exact bias was derived following the Cox and Snell's equation. A combination of simulation studies and real life data were used to validate the proposed method. Findings from the validation studies show that our model's standard errors are consistently lower than those derived from other bias reduction methods for the missing at random data mechanism. Consequently, we conclude that in most cases, our method's performance in parameter estimation is superior to the other classical methods for bias reduction when data are MAR.

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