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Robust Inference for Event Probabilities with Non‐Markov Event Data
Author(s) -
Glidden David V.
Publication year - 2002
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2002.00361.x
Subject(s) - inference , event (particle physics) , markov chain , computer science , nonparametric statistics , estimator , markov model , statistics , econometrics , data mining , mathematics , artificial intelligence , machine learning , physics , quantum mechanics
Summary. Multistate event data, in which a single subject is at risk for multiple events, is common in biomedical applications. This article considers nonparametric estimation of the vector of probabilities of state membership at time t. Estimators, derived under the Markov assumption, have been shown (Datta and Satten, 2001, Statistics and Probability Letters 55 , 403–411) to be consistent for data that is non‐Markov. Inference, however, must take into account possibly non‐Markov transitions when constructing confidence bands for event curves. We develop robust confidence bands for these curves, evaluate them via simulation, and illustrate the method on two datasets.