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On computation of semiparametric maximum likelihood estimators with shape constraints
Author(s) -
Wang Yudong,
Ye ZhiSheng,
Cao Hongyuan
Publication year - 2021
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/biom.13266
Subject(s) - estimator , computation , semiparametric regression , semiparametric model , nonparametric statistics , expectation–maximization algorithm , computer science , range (aeronautics) , algorithm , constraint (computer aided design) , computational complexity theory , maximization , mathematical optimization , mathematics , maximum likelihood , statistics , materials science , geometry , composite material
Large sample theory of semiparametric models based on maximum likelihood estimation (MLE) with shape constraint on the nonparametric component is well studied. Relatively less attention has been paid to the computational aspect of semiparametric MLE. The computation of semiparametric MLE based on existing approaches such as the expectation‐maximization (EM) algorithm can be computationally prohibitive when the missing rate is high. In this paper, we propose a computational framework for semiparametric MLE based on an inexact block coordinate ascent (BCA) algorithm. We show theoretically that the proposed algorithm converges. This computational framework can be applied to a wide range of data with different structures, such as panel count data, interval‐censored data, and degradation data, among others. Simulation studies demonstrate favorable performance compared with existing algorithms in terms of accuracy and speed. Two data sets are used to illustrate the proposed computational method. We further implement the proposed computational method in R package BCA1SG , available at CRAN.