Fungi Evolution Revisited: Application of the Penalized Likelihood Method to a Bayesian Fungal Phylogeny Provides a New Perspective on Phylogenetic Relationships and Divergence Dates of Ascomycota Groups

2005 ◽  
Vol 60 (6) ◽  
pp. 726-735 ◽  
Author(s):  
Ana Carolina B. Padovan ◽  
Gerdine F.O. Sanson ◽  
Adriana Brunstein ◽  
Marcelo R.S. Briones
Keyword(s):  
Phylogenetic Relationships ◽  
Penalized Likelihood ◽  
Likelihood Method ◽  
Fungal Phylogeny ◽  
Divergence Dates ◽  
New Perspective

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.

scholarly journals Primate phylogenetic relationships and divergence dates inferred from complete mitochondrial genomes

2014 ◽  
Vol 75 ◽  
pp. 165-183 ◽  
Author(s):  
Luca Pozzi ◽  
Jason A. Hodgson ◽  
Andrew S. Burrell ◽  
Kirstin N. Sterner ◽  
Ryan L. Raaum ◽  
...  
Keyword(s):  
Phylogenetic Relationships ◽  
Mitochondrial Genomes ◽  
Divergence Dates ◽  
Complete Mitochondrial Genomes

scholarly journals Occurrence of gilled fungi in Puducherry, India

2021 ◽  
Vol 13 (7) ◽  
pp. 18878-18887
Author(s):  
Vadivelu Kumaresan ◽  
Chakravarthy Sariha ◽  
Thokur Sreepathy Murali ◽  
Gunasekaran Senthilarasu
Keyword(s):  
Maximum Likelihood ◽  
Phylogenetic Relationships ◽  
Maximum Likelihood Method ◽  
Schizophyllum Commune ◽  
Abundant Species ◽  
Southern India ◽  
Likelihood Method

Thirty-three species of gilled fungi belonging to 23 genera and 14 families were recorded from Puducherry, southern India. Agaricaceae were represented by eight species, followed by Psathyrellaceae (5), Lyophyllaceae & Marasmiaceae (3 each), Hymenogastraceae, Pleurotaceae, Pluteaceae, & Polyporaceae (2 each), and Biannulariaceae, Bolbitiaceae, Omphalotaceae, Schizophyllaceae, Strophariaceae, & Tricholomataceae (1 each). Fourteen species of agarics are new reports from Puducherry. Chlorophyllum rhacodes, Lactocollybia epia, Leucoagaricus meleagris, and Schizophyllum commune were widely distributed. Phylogenetic relationships of the abundant species C. rhacodes, L. epia, and L. meleagris were inferred by maximum likelihood method.

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.

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
Sign in / Sign up

Export Citation Format

Share Document