Result: Estimation Under the Lehmann Regression Model with Interval-Censored Data
Title:
Estimation Under the Lehmann Regression Model with Interval-Censored Data
Authors:
Source:
Communications in statistics. Simulation and computation. 41(8-10):1489-1500
Publisher Information:
Colchester: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 1/4 p
Original Material:
INIST-CNRS
Subject Terms:
Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Analyse mathématique, Mathematical analysis, Calcul des variations et contrôle optimal, Calculus of variations and optimal control, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Inférence non paramétrique, Nonparametric inference, Inférence linéaire, régression, Linear inference, regression, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Algorithme, Algorithm, Algoritmo, Analyse discriminante, Discriminant analysis, Análisis discriminante, Analyse donnée, Data analysis, Análisis datos, Analyse multivariable, Multivariate analysis, Análisis multivariable, Complexité calcul, Computational complexity, Complejidad computación, Donnée censurée, Censored data, Fonction vraisemblance, Likelihood function, Función verosimilitud, Maximum vraisemblance, Maximum likelihood, Maxima verosimilitud, Modèle Cox, Cox model, Modelo Cox, Modèle régression, Regression model, Modelo regresión, Méthode Newton Raphson, Newton Raphson method, Método Newton Raphson, Méthode calcul, Computing method, Método cálculo, Méthode paramétrique, Parametric method, Método paramétrico, Méthode statistique, Statistical method, Método estadístico, Pathologie du sein, Breast disease, Seno patología, Régression statistique, Statistical regression, Regresión estadística, Simulation numérique, Numerical simulation, Simulación numérica, 49M15, 62H30, 62Jxx, 62N01, Cancer sein, Breast cancer, Classification automatique(statistiques), Donnée groupée, Grouped data, Estimation paramétrique, Parametric estimation, Vraisemblance profil, Profile likelihood, Algorithms, Cox regression, Interval-censored data, Primary 62G07, Secondary 62J99
Document Type:
Academic journal
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Integrative Medicine, Beth Israel Medical Center, New York, United States
Department of Mathematical Sciences, Binghamton University, New York, United States
Department of Mathematical Sciences, Binghamton University, New York, United States
ISSN:
0361-0918
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics
Accession Number:
edscal.26164003
Database:
PASCAL Archive
Further Information
We consider the estimation problem under the Lehmann model with interval-censored data, but focus on the computational issues. There are two methods for computing the semi-parametric maximum likelihood estimator (SMLE) under the Lehmann model (or called Cox model): the Newton-Raphson (NR) method and the profile likelihood (PL) method. We show that they often do not get close to the SMLE. We propose several approach to overcome the computational difficulty and apply our method to a breast cancer research data set.