Semiparametric efficient inferences for generalised partially linear models

Rahman, Jafer; Si, Luo; Fan, Yawen; Liu, Xiaohui

doi:10.1080/10485252.2020.1790557

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Accurate estimation of the incubation-period distribution, or incubation distribution, is crucial (especially in regions where the epidemic is severe) for determining the length of appropriate quarantine periods for suspected individuals. In the literature, estimating incubation distributions has attracted much attention (Sartwell, 1950;Kalbfleisch and Lawless, 1989;Struthers and Farewell, 1989;Kalbfleisch and Lawless, 1991;Farewell et al, 2005;Wilkening, 2008), while studies for COVID-19 are still ongoing; see Backer et al (2020), Guan et al (2020), Lauer et al (2020), Li et al (2020), Linton et al (2020), Liu et al (2021), Qin et al (2020), Rahman et al (2020), Wang et al (2020b), and Liu et al (2022), among others. The current results are based mostly on clinical experience or empirical statistical analysis of contact-tracing data, but such data may be inaccurate because of the patient's recall bias or the interviewer's personal judgment on the possible date of exposure rather than the actual date.…”

Section: Introductionmentioning

confidence: 99%

Hypothesis Test on a Mixture Forward-Incubation-Time Epidemic Model With Application to COVID-19 Outbreak

Wang¹,

Li²,

Liu³

et al. 2025

STAT SINICA

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The distribution of the incubation period of the novel coronavirus disease that emerged in 2019 (COVID-19) has crucial clinical implications for understanding this disease and devising effective disease-control measures. Qin et al. (2020) designed a cross-sectional and forward follow-up study to collect the duration times between a specific observation time and the onset of COVID-19 symptoms for a number of individuals. They further proposed a mixture forward-incubation-time epidemic model, which is a mixture of an incubationperiod distribution and a forward time distribution, to model the collected duration times and to estimate the incubation-period distribution of COVID-19. In this paper, we provide sufficient conditions for the identifiability of the unknown parameters in the mixture forward-incubation-time epidemic model when the incubation period follows a two-parameter distribution. Under the same setup,

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Section: Introductionmentioning

confidence: 99%

Hypothesis Test on a Mixture Forward-Incubation-Time Epidemic Model With Application to COVID-19 Outbreak

Wang¹,

Li²,

Liu³

et al. 2025

STAT SINICA

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“…Leng, Liang & Martinson (2011) developed a least squares approximation-based variable selection procedure for GPLMs to study the significant predictors of condom use in HIV-infected adults. Rahman et al (2020) considered semiparametric efficient inference in the GPLM and developed a bias-corrected estimating procedure and a bias-corrected empirical log-likelihood ratio for point estimation and confidence regions for the parameters of interest.…”

Section: Introductionmentioning

confidence: 99%

Pretest and shrinkage estimators in generalized partially linear models with application to real data

Hossain

Mandal

2022

Can J Statistics

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Semiparametric models hold promise to address many challenges to statistical inference that arise from real‐world applications, but their novelty and theoretical complexity create challenges for estimation. Taking advantage of the broad applicability of semiparametric models, we propose some novel and improved methods to estimate the regression coefficients of generalized partially linear models (GPLM). This model extends the generalized linear model by adding a nonparametric component. Like in parametric models, variable selection is important in the GPLM to single out the inactive covariates for the response. Instead of deleting inactive covariates, our approach uses them as auxiliary information in the estimation procedure. We then define two models, one that includes all the covariates and another that includes the active covariates only. We then combine these two model estimators optimally to form the pretest and shrinkage estimators. Asymptotic properties are studied to derive the asymptotic biases and risks of the proposed estimators. We show that if the shrinkage dimension exceeds two, the asymptotic risks of the shrinkage estimators are strictly less than those of the full model estimators. Extensive Monte Carlo simulation studies are conducted to examine the finite‐sample performance of the proposed estimation methods. We then apply our proposed methods to two real data sets. Our simulation and real data results show that the proposed estimators perform with higher accuracy and lower variability in the estimation of regression parameters for GPLM compared with competing estimation methods.

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A generalized Liu-type estimator for logistic partial linear regression model with multicollinearity

Dai¹,

Wang

2023

MATH

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<abstract><p>This paper is concerned with proposing a generalized Liu-type estimator (GLTE) to address the multicollinearity problem of explanatory variable of the linear part in the logistic partially linear regression model. Using the profile likelihood method, we propose the GLTE as a general class of Liu-type estimator, which includes the profile likelihood estimator, the ridge estimator, the Liu estimator and the Liu-type estimator as special cases. The conditional superiority of the proposed GLTE over the other estimators is derived under the asymptotic mean square error matrix (MSEM) criterion. Moreover, the optimal choices of biasing parameters and function of biasing parameter are given. Numerical simulations demonstrate that the proposed GLTE performs better than the existing estimators. An application on a set of real data arising from the study of Indian Liver Patient is shown for illustrating our theoretical results.</p></abstract>

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Semiparametric efficient inferences for generalised partially linear models

Cited by 3 publications

References 22 publications

Hypothesis Test on a Mixture Forward-Incubation-Time Epidemic Model With Application to COVID-19 Outbreak

Hypothesis Test on a Mixture Forward-Incubation-Time Epidemic Model With Application to COVID-19 Outbreak

Pretest and shrinkage estimators in generalized partially linear models with application to real data

A generalized Liu-type estimator for logistic partial linear regression model with multicollinearity

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