Bayesian semi-parametricsBayesian conditional density estimationHeteroscedastic linear regressionIn this paper we consider Bayesian estimation of restricted conditional moment models with the linear regression as a particular example. A common practice in the Bayesian literature for linear regression and other ...
In particular, when the response variable y t in a regression model is a binary variable that only takes the values 0 and 1, a natural extension of the linear regression function 胃 T x t is to relate how the parameter of the Bernoulli distribution of y t depends on 胃 T x t . ...
(1) Parameters are elements of the linear form xb__t. Bayesian Weibull PH regression MCMC iterations = 12,500 Random-walk Metropolis-Hastings sampling Burn-in = 2,500 MCMC sample size = 10,000 No. of subjects = 148 Number of obs = 206 No. of failures = 37 No. at risk = 1703 Acc...
It was however shown in previous studies, that a more advanced non-parametric approach which com- bines Bayesian linear regression and GPs is able to achieve higher performance [16] assuming that some of the parameters are known. In our work, we show that knowledge of such parameters prior ...
correlation robust tidy statistical-tests bayesian-inference meta-analysis parametric bayesian-statistics contingency-table robust-statistics effectsize statistical-details Updated Feb 24, 2025 R Irev-Dev / curated-code-cad Star 275 Code Issues Pull requests A list of the various code-cad projects ...
(2013) from the Bayesian stance. In Mullahy (2015), regression models for economic share data were estimated, with the shares taking zero values with nontrivial probability. Alternative regression models in the field of econometrics and applicable when zero values are present are discussed in ...
This is particularly true in the Bayesian decision-theoretic testing [15]; the rejection of the null hypothesis based on the posterior odds ratio across the two hypotheses is transformed to a comparison of the BF and the threshold, expressed as the product of a prior odds ratio and the cost...
A comparison of Bayesian and non-linear regression methods for robust estimation of pharmacokinetics in DCE-MRI and how it affects cancer diagnosis 2017, Computerized Medical Imaging and Graphics Show abstract 1H magnetic resonance spectroscopic imaging of deuterated glucose and of neurotransmitter metabolis...
We incorporate heteroskedasticity into Bayesian Additive Regression Trees (BART) by modeling the log of the error variance parameter as a linear function of prespecified covariates. Under this scheme, the Gibbs sampling procedure for the original sum-of- trees model is easily modified, and the parame...
Currently, this package gives the user a choice from 83 Bayesian models for data analysis. They include 47 Bayesian nonparametric (BNP) infinite-mixture regression models; 5 BNP infinite-mixture models for density estimation; and 31 normal random effects models (HLMs), including normal linear ...