About an adaptively weighted Kaplan-Meier estimate.
The minimum averaged mean squared error nonparametric adaptive weights use data from m possibly different populations to infer about one population of interest. The definition of these weights is based on the properties of the empirical distribution function. We use the Kaplan-Meier estimate to let the weights accommodate right-censored data and use them to define the weighted Kaplan-Meier estimate. The proposed estimate is smoother than the usual Kaplan-Meier estimate and converges uniformly in probability to the target distribution. Simulations show that the performances of the weighted Kaplan-Meier estimate on finite samples exceed that of the usual Kaplan-Meier estimate. A case study is also presented.
Plante
Jean-François
JF
Service d'enseignement des méthodes quantitatives de gestion, HEC Montréal, 3000 chemin de la Côte-Sainte-Catherine, Montréal, Quebec, Canada. jfplante@hec.ca
D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D016030:Kidney Transplantation / Q000401:mortality; D016018:Least-Squares Analysis; D015993:Life Tables; D016013:Likelihood Functions; D008297:Male; D055641:Mathematical Concepts; D015233:Models, Statistical; D011336:Probability; D018709:Statistics, Nonparametric; D016019:Survival Analysis; D014481:United States / Q000453:epidemiology