Sensitivity analysis for Bayesian hierarchical models
Sensitivity Analysis for Bayesian Hierarchical Models Page 1 Bayesian Analysis (2015) 10,
Number 2, pp. 321–349 Sensitivity Analysis for Bayesian Hierarchical Models Ma lgorzata …
Number 2, pp. 321–349 Sensitivity Analysis for Bayesian Hierarchical Models Ma lgorzata …
Assessing survival time of heart failure patients: using Bayesian approach
T Ashine, G Muleta, K Tadesse - Journal of Big Data, 2021 - Springer
Heart failure is a failure of the heart to pump blood with normal efficiency and a globally
growing public health issue with a high death rate all over the world, including Ethiopia. The …
growing public health issue with a high death rate all over the world, including Ethiopia. The …
Bayesian regularization for flexible baseline hazard functions in Cox survival models
Abstract Fully Bayesian methods for Cox models specify a model for the baseline hazard
function. Parametric approaches generally provide monotone estimations. Semi‐parametric …
function. Parametric approaches generally provide monotone estimations. Semi‐parametric …
Assessing the survival time of women with breast cancer in Northwestern Ethiopia: using the Bayesian approach
C Gashu, AE Aguade - BMC women's health, 2024 - Springer
Background Despite the significant weight of difficulty, Ethiopia's survival rate and mortality
predictors have not yet been identified. Finding out what influences outpatient breast cancer …
predictors have not yet been identified. Finding out what influences outpatient breast cancer …
Bayesian local influence for spatial autoregressive models with heteroscedasticity
This paper studies Bayesian local influence analysis for the spatial autoregressive models
with heteroscedasticity (heteroscedastic SAR models). Two local diagnostic procedures …
with heteroscedasticity (heteroscedastic SAR models). Two local diagnostic procedures …
Bayesian semiparametric approach to quantile nonlinear dynamic factor analysis models with mixed ordered and nonignorable missing data
M Tuerde, N Tang - Statistics, 2022 - Taylor & Francis
In classical nonlinear dynamic factor analysis models (NDFAMs), we assume manifest
variables follow the normal distribution. However, in some applications, the normality …
variables follow the normal distribution. However, in some applications, the normality …
Bayesian local influence analysis of skew-normal spatial dynamic panel data models
Y Ju, N Tang, X Li - Journal of Statistical Computation and …, 2018 - Taylor & Francis
The existing studies on spatial dynamic panel data model (SDPDM) mainly focus on the
normality assumption of response variables and random effects. This assumption may be …
normality assumption of response variables and random effects. This assumption may be …
[HTML][HTML] Bayesian influence analysis of generalized partial linear mixed models for longitudinal data
NS Tang, XD Duan - Journal of Multivariate Analysis, 2014 - Elsevier
This paper develops a Bayesian local influence approach to assess the effects of minor
perturbations to the prior, sampling distribution and individual observations on the statistical …
perturbations to the prior, sampling distribution and individual observations on the statistical …
Bayesian case influence measures for statistical models with missing data
We examine three Bayesian case influence measures including the φ-divergence, Cook's
posterior mode distance, and Cook's posterior mean distance for identifying a set of …
posterior mode distance, and Cook's posterior mean distance for identifying a set of …
The new Neyman type A beta Weibull model with long-term survivors
EM Hashimoto, GM Cordeiro, EMM Ortega - Computational Statistics, 2013 - Springer
For the first time, we propose a flexible cure rate survival model by assuming that the
number of competing causes of the event of interest follows the Neyman type A distribution …
number of competing causes of the event of interest follows the Neyman type A distribution …