scholarly journals AIC for the non-concave penalized likelihood method

2018 ◽  
Vol 71 (2) ◽  
pp. 247-274 ◽  
Author(s):  
Yuta Umezu ◽  
Yusuke Shimizu ◽  
Hiroki Masuda ◽  
Yoshiyuki Ninomiya
Keyword(s):  
Penalized Likelihood ◽  
Likelihood Method

scholarly journals Penalized likelihood estimation of a trivariate additive probit model

Biostatistics ◽  
2017 ◽  
Vol 18 (3) ◽  
pp. 569-585 ◽  
Author(s):  
Panagiota Filippou ◽  
Giampiero Marra ◽  
Rosalba Radice
Keyword(s):  
Penalized Likelihood ◽  
Trust Region ◽  
Correlation Coefficients ◽  
Likelihood Estimation ◽  
Probit Model ◽  
R Package ◽  
Likelihood Method ◽  
Spatial Effects ◽  
Trust Region Algorithm ◽  
Smoothing Parameter Selection

SUMMARY This article proposes a penalized likelihood method to estimate a trivariate probit model, which accounts for several types of covariate effects (such as linear, nonlinear, random, and spatial effects), as well as error correlations. The proposed approach also addresses the difficulty in estimating accurately the correlation coefficients, which characterize the dependence of binary responses conditional on covariates. The parameters of the model are estimated within a penalized likelihood framework based on a carefully structured trust region algorithm with integrated automatic multiple smoothing parameter selection. The relevant numerical computation can be easily carried out using the SemiParTRIV() function in a freely available R package. The proposed method is illustrated through a case study whose aim is to model jointly adverse birth binary outcomes in North Carolina.

Penalized likelihood estimation of the proportional hazards model for survival data with interval censoring

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jun Ma ◽  
Dominique-Laurent Couturier ◽  
Stephane Heritier ◽  
Ian C. Marschner
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Penalized Likelihood ◽  
Interval Censoring ◽  
Basis Functions ◽  
Likelihood Method ◽  
Baseline Hazard ◽  
Computational Performance ◽  
Hazards Model ◽  
Study Results

Abstract This paper considers the problem of semi-parametric proportional hazards model fitting where observed survival times contain event times and also interval, left and right censoring times. Although this is not a new topic, many existing methods suffer from poor computational performance. In this paper, we adopt a more versatile penalized likelihood method to estimate the baseline hazard and the regression coefficients simultaneously. The baseline hazard is approximated using basis functions such as M-splines. A penalty is introduced to regularize the baseline hazard estimate and also to ease dependence of the estimates on the knots of the basis functions. We propose a Newton–MI (multiplicative iterative) algorithm to fit this model. We also present novel asymptotic properties of our estimates, allowing for the possibility that some parameters of the approximate baseline hazard may lie on the parameter space boundary. Comparisons of our method against other similar approaches are made through an intensive simulation study. Results demonstrate that our method is very stable and encounters virtually no numerical issues. A real data application involving melanoma recurrence is presented and an R package ‘survivalMPL’ implementing the method is available on R CRAN.

On hazard-based penalized likelihood estimation of accelerated failure time model with partly interval censoring

2020 ◽  
Vol 29 (12) ◽  
pp. 3804-3817
Author(s):  
Jinqing Li ◽  
Jun Ma
Keyword(s):  
Failure Time ◽  
Penalized Likelihood ◽  
Interval Censoring ◽  
Accelerated Failure Time Model ◽  
Likelihood Method ◽  
Accelerated Failure Time ◽  
Time Model ◽  
Accelerated Failure ◽  
Aids Study ◽  
Failure Time Model

In survival analysis, the semiparametric accelerated failure time model is an important alternative to the widely used Cox proportional hazard model. The existing methods for accelerated failure time models include least-squares, log rank-based estimating equations and approximations to the nonparametric error distribution. In this paper, we propose another fitting method for the accelerated failure time model, formulated from the hazard function of the exponential error term. Our method can handle partly interval-censored data which contains event time, as well as left, right and interval censoring time. We adopt the maximum penalized likelihood method to estimate all the parameters in the model, including the nonparametric component. The penalty function is used to regularize the nonparametric component of the accelerated failure time model. Asymptotic properties of the penalized likelihood estimate are developed. A simulation study is conducted to investigate the performance of the proposed method and an application of this method to an AIDS study is presented as an example.

scholarly journals A Penalized-Likelihood Method to Estimate the Distribution of Selection Coefficients from Phylogenetic Data

Genetics ◽  
2014 ◽  
Vol 197 (1) ◽  
pp. 257-271 ◽  
Author(s):  
Asif U. Tamuri ◽  
Nick Goldman ◽  
Mario dos Reis
Keyword(s):  
Penalized Likelihood ◽  
Likelihood Method ◽  
Phylogenetic Data
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