Available on ORBi since
25 January 2020
Article (Scientific journals)
Goodness-of-fit tests for censored regression based on artificial data points
Gonzalez, Wenceslao González; Heuchenne, Cédric  ; Sanchez, César Sánchez et al.
2020 • In TEST, 29, p. 599-615
Peer reviewed
 

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Keywords :
Bootstrap; Goodness-of-fit tests; Kernel method; Least squares estimation; Nonparametric regression; Right censoring; Survival analysis
Abstract :
[en] Suppose we have a location-scale regression model where the location is the conditional mean and the scale is the conditional standard deviation; the response is possibly right-censored, the covariate is fully observed, and the error is independent of the covariate. We propose new goodness-of-fit testing procedures for the conditional mean and variance based on an integrated regression function technique which uses artificial data points. We obtain the weak convergence of the resulting processes and study their finite sample behavior via simulations. Finally, we analyze a data set about unemployment in Galicia. © 2019, Sociedad de Estadística e Investigación Operativa.
Disciplines :
Mathematics
Author, co-author :
Gonzalez, Wenceslao González
Heuchenne, Cédric ;  Université de Liège - ULiège > HEC Liège : UER > UER Opérations: Statistique appl. à la gest. et à l'économie
Sanchez, César Sánchez
Beretta, Alessandro ;  Université de Liège - ULiège > HEC Liège : UER > UER Opérations
Language :
English
Title :
Goodness-of-fit tests for censored regression based on artificial data points
Publication date :
2020
Journal title :
TEST
ISSN :
1133-0686
eISSN :
1863-8260
Publisher :
Springer New York LLC
Volume :
29
Pages :
599-615
Peer reviewed :
Peer reviewed
Funders :
FEDER - Fonds Européen de Développement Régional

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