Empirical likelihood methods based on influence functions

Yao, Wen; Zhao, Zhenlu; Zheng, Ming

doi:10.4310/sii.2012.v5.n3.a8

Cited by 12 publications

(10 citation statements)

References 24 publications

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“…Yu et al. 13 proposed an EL function based on influence functions of parameters of interest. Motivated by their study, we propose a new influence function-based EL method to construct confidence intervals for sensitivity.…”

Section: Influence Function-based Empirical Likelihood (Ifel) Methodsmentioning

confidence: 99%

“…We propose a new influence function-based EL (IFEL) method following Yu et al. 13 The idea is to replace the estimating function in the EL with an influence function of the sensitivity. The corresponding empirical log-likelihood ratio statistic converges to a standard chi-square distribution, which makes inference for sensitivity more convenient.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian and influence function-based empirical likelihoods for inference of sensitivity in diagnostic tests

Hai

Min

Qin

et al. 2020

Stat Methods Med Res

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In medical diagnostic studies, a diagnostic test can be evaluated based on its sensitivity under a desired specificity. Existing methods for inference on sensitivity include normal approximation-based approaches and empirical likelihood (EL)-based approaches. These methods generally have poor performance when the specificity is high, and some require choosing smoothing parameters. We propose a new influence function-based empirical likelihood method and Bayesian empirical likelihood methods to overcome such problems. Numerical studies are performed to compare the finite sample performance of the proposed approaches with existing methods. The proposed methods are shown to perform better in terms of both coverage probability and interval length. A real data set from Alzheimer’s Disease Neuroimaging Initiative (ANDI) is analyzed.

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Section: Influence Function-based Empirical Likelihood (Ifel) Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian and influence function-based empirical likelihoods for inference of sensitivity in diagnostic tests

Hai

Min

Qin

et al. 2020

Stat Methods Med Res

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“…Thus, an extra step is required to estimate the unknown scale constant for inference on 𝑃 2 by using density estimation or the bootstrap method. Yu et al (2012) proposed EL methods based on influence functions of parameters of interest. Hai et al (2020) proposed an influence function-based EL method for inference of sensitivity of two-stage diagnostic tests.…”

Section: Therapeutics; Canadian Institutes Of Health Researchmentioning

confidence: 99%

“…Yu et al. (2012) proposed EL methods based on influence functions of parameters of interest. Hai et al.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian and influence function‐based empirical likelihoods for inference of sensitivity to the early diseased stage in diagnostic tests

et al. 2023

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In practice, a disease process might involve three ordinal diagnostic stages: the normal healthy stage, the early stage of the disease, and the stage of full development of the disease. Early detection is critical for some diseases since it often means an optimal time window for therapeutic treatments of the diseases. In this study, we propose a new influence function-based empirical likelihood method and Bayesian empirical likelihood methods to construct confidence/credible intervals for the sensitivity of a test to patients in the early diseased stage given a specificity and a sensitivity of the test to patients in the fully diseased stage. Numerical studies are performed to compare the finite sample performances of the proposed approaches with existing methods. The proposed methods are shown to outperform existing methods in terms of coverage probability. A real dataset from the Alzheimer's Disease Neuroimaging Initiative (ANDI) is used to illustrate the proposed methods.

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Empirical likelihood‐based confidence intervals for mean medical cost with censored data

Jeyarajah

Qin

2017

Statistics in Medicine

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In this paper, we propose empirical likelihood methods based on influence function and jackknife techniques for constructing confidence intervals for mean medical cost with censored data. We conduct a simulation study to compare the coverage probabilities and interval lengths of our proposed confidence intervals with that of the existing normal approximation-based confidence intervals and bootstrap confidence intervals. The proposed methods have better finite-sample performances than existing methods. Finally, we illustrate our proposed methods with a relevant example.

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Empirical likelihood methods based on influence functions

Cited by 12 publications

References 24 publications

Bayesian and influence function-based empirical likelihoods for inference of sensitivity in diagnostic tests

Bayesian and influence function-based empirical likelihoods for inference of sensitivity in diagnostic tests

Bayesian and influence function‐based empirical likelihoods for inference of sensitivity to the early diseased stage in diagnostic tests

Empirical likelihood‐based confidence intervals for mean medical cost with censored data

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