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Maximum approximate Bernstein likelihood estimation in proportional hazard model for interval‐censored data
Author(s) -
Guan Zhong
Publication year - 2020
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8801
Subject(s) - statistics , mathematics , hazard ratio , proportional hazards model , interval (graph theory) , confidence interval , rate of convergence , hazard , convergence (economics) , event (particle physics) , regression , function (biology) , econometrics , computer science , combinatorics , physics , computer network , channel (broadcasting) , chemistry , organic chemistry , quantum mechanics , economics , economic growth , evolutionary biology , biology
Maximum approximate Bernstein likelihood estimates of the baseline density function and the regression coefficients in the proportional hazard regression models based on interval‐censored event time data result in smooth estimates of the survival functions which enjoys an almost n 1/2 ‐rate of convergence faster than the n 1/3 ‐rate for the existing estimates. The proposed method was shown by a simulation to have better finite sample performance than its main competitors. Some examples including real data are used to illustrate the usage of the proposed method.