Treffer: Analysis of Survival Data with Group Lasso
Title:
Analysis of Survival Data with Group Lasso
Authors:
Source:
Communications in statistics. Simulation and computation. 41(8-10):1593-1605
Publisher Information:
Colchester: Taylor & Francis, 2012.
Publication Year:
2012
Physical Description:
print, 2 p.1/4
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, Analyse multivariable, Multivariate analysis, 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, Analyse numérique, Numerical analysis, Análisis numérico, Analyse survie, Survival analysis, Calcul variationnel, Variational calculus, Cálculo de variaciones, Covariable, Covariate, Donnée censurée, Censored data, Fonction survie, Survival function, Función sobrevivencia, Modèle Cox, Cox model, Modelo Cox, Méthode optimisation, Optimization method, Método optimización, Méthode pénalité, Penalty method, Método penalidad, Méthode statistique, Statistical method, Método estadístico, Optimisation, Optimization, Optimización, Programmation mathématique, Mathematical programming, Programación matemática, Régression statistique, Statistical regression, Regresión estadística, Simulation numérique, Numerical simulation, Simulación numérica, Survie, Survival, Sobrevivencia, Théorie prédiction, Prediction theory, 49XX, 62H30, 62Jxx, 62N02, 62N99, 62Nxx, 65K10, 65Kxx, Classification automatique(statistiques), Donnée groupée, Grouped data, Méthode sélection, Selection method, Puce à ADN, Microarray, Sélection variable, Variable selection, Discrete covariate, Gene expression, Primary 62N01, Secondary 62P10
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
Department of Statistics and Information Science, Dongguk University, Korea, Republic of
Biostatisties and Bioinformatics Center, Samsung Cancer Research Institute, Samsung Medical Center, Korea, Republic of
Department of Biostatistics and Biuinformatics, Duke University, Durham, North Carolina, United States
R&D Center, Komipharm International Co., Ltd, Korea, Republic of
Department of Statistics, University of Seoul, Seoul, Korea, Republic of
Biostatisties and Bioinformatics Center, Samsung Cancer Research Institute, Samsung Medical Center, Korea, Republic of
Department of Biostatistics and Biuinformatics, Duke University, Durham, North Carolina, United States
R&D Center, Komipharm International Co., Ltd, Korea, Republic of
Department of Statistics, University of Seoul, Seoul, Korea, Republic of
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.26164010
Database:
PASCAL Archive
Weitere Informationen
Identification of influential genes and clinical covariates on the survival of patients is crucial because it can lead us to better understanding of underlying mechanism of diseases and better prediction models. Most of variable selection methods in penalized Cox models cannot deal properly with categorical variables such as gender and family history. The group lasso penalty can combine clinical and genomic covariales effectively. In this article, we introduce an optimization algorithm for Cox regression with group lasso penalty. We compare our method with other methods on simulated and real microarray data sets.