test_moissonnage_selune
type: Agent

Statistical inference in logistic regression model with immune fraction
type: Distribution
access URL: https://theses.hal.science/tel-00829844
format: HTML
issued: 2026-05-10T21:20:51.049430
modified: 2026-05-10T21:20:51.013154

Statistical inference in logistic regression model with immune fraction
type: Dataset
contact point: Organization
  fn: CCSD
keyword: regression-logistique
keyword: dengue
keyword: immune-fraction
keyword: logistic-regression
keyword: maximum-de-vraisemblance
keyword: bootstrap
keyword: confidance-bands
keyword: simulations
keyword: identifiabilite
keyword: fraction-immune
keyword: consistancy
keyword: normalite-asymptotique
keyword: consistance
keyword: maximum-likelihood
keyword: theses
keyword: bandes-de-confiance
keyword: asymptotic-normality
keyword: identifiability
keyword: mathmath-gmmathematics-mathgeneral-mathematics-mathgm
keyword: infoeu-reposemanticsdoctoralthesis
landing page: https://theses.hal.science/tel-00829844
description: Generalized linear models are a generalization of linear regression models, and are widely used in the field of life. The logistic regression model, one of this class of models, widely used in biomedical studies remains the most appropriate regression model when it comes to model discrete variable, binary in nature. In this thesis, we investigate the problem of statistical inference in the logistic regression model, in the presence of immune individuals in the study population.At first, we consider the problem of estimation in the logistic regression model in the presence of immune individuals that enters in the case of zero-inflated regression models. A subject is said to be immune if he cannot experience the outcome of interest. The immune status is unknown unless the event of interest has been observed. We develop a maximum like lihood estimation procedure for this problem, based on the joint modeling of the binary response of interest and the cure status. We investigate the identifiability of the resulting model. Then, we establish the existence, consistency and asymptotic normality of the proposed estimator, and we conduct a simulation study to investigate its finite-sample behavior. In a second time, we focus on the construction of simultaneous confidence bands for the probability of infection in the logistic regression model with immune fraction.We propose three methods of construction of confidence bands for the regression function. The first method (Scheffe's method) uses the asymptotic normality of the maximum like lihood estimator, and an approximation by the chi-squared distribution to approximate the necessary quantile for the construction of bands. The second method uses also the asymptotic normality of the maximum like lihood estimator and is based on a classical equality by (Landau & Sheep 1970). The third method (bootstrap method) is based on simulations, to estimate the appropriate quantile of the law of a supremum of a Gaussian process. Finally, w…
identifier: NNT: 2012LAROS375
issued: 2026-05-10T21:20:51.045280
language: fr
modified: 2026-05-10T21:20:51.045285