269 citations · 335 across the 12 of their papers we have counts for
13 papers
Sparse Horseshoe Estimation via Expectation-Maximisation
Shu Yu Tew, Daniel F. Schmidt, Enes Makalic
The horseshoe prior is known to possess many desirable properties for Bayesian estimation of sparse parameter vectors, yet its density function lacks an analytic form. As such, it…
Introduction to minimum message length inference
Enes Makalic, Daniel F. Schmidt
The aim of this manuscript is to introduce the Bayesian minimum message length principle of inductive inference to a general statistical audience that may not be familiar with info…
Minimum message length inference of the Weibull distribution with complete and censored data
Enes Makalic, Daniel F. Schmidt
The Weibull distribution, with shape parameter and scale parameter , is one of the most popular parametric distributions in survival analysis with complete or censored d…
Maximum likelihood estimation of the Weibull distribution with reduced bias
Enes Makalic, Daniel F. Schmidt
In this short note, we derive a new bias adjusted maximum likelihood estimate for the shape parameter of the Weibull distribution with complete data and type I censored data. The p…
MML Probabilistic Principal Component Analysis
Enes Makalic, Daniel F. Schmidt
Principal component analysis (PCA) is perhaps the most widely used method for data dimensionality reduction. A key question in PCA is deciding how many factors to retain. This manu…
An efficient algorithm for sampling from for generating random correlation matrices
Enes Makalic, Daniel F. Schmidt
In this note, we develop a novel algorithm for generating random numbers from a distribution with a probability density function proportional to , and $k \…
A Minimum Message Length Criterion for Robust Linear Regression
Chi Kuen Wong, Enes Makalic, Daniel F. Schmidt
This paper applies the minimum message length principle to inference of linear regression models with Student-t errors. A new criterion for variable selection and parameter estimat…
Log-Scale Shrinkage Priors and Adaptive Bayesian Global-Local Shrinkage Estimation
Daniel F. Schmidt, Enes Makalic
Global-local shrinkage hierarchies are an important innovation in Bayesian estimation. We propose the use of log-scale distributions as a novel basis for generating familes of prio…
Bayesian Sparse Global-Local Shrinkage Regression for Selection of Grouped Variables
Zemei Xu, Daniel F. Schmidt, Enes Makalic +2
Most estimates for penalised linear regression can be viewed as posterior modes for an appropriate choice of prior distribution. Bayesian shrinkage methods, particularly the horses…
Minimum message length inference of the Poisson and geometric models using heavy-tailed prior distributions
Chi Kuen Wong, Enes Makalic, Daniel F. Schmidt
Minimum message length is a general Bayesian principle for model selection and parameter estimation that is based on information theory. This paper applies the minimum message leng…
High-Dimensional Bayesian Regularised Regression with the BayesReg Package
Enes Makalic, Daniel F. Schmidt
Bayesian penalized regression techniques, such as the Bayesian lasso and the Bayesian horseshoe estimator, have recently received a significant amount of attention in the statistic…
A simple sampler for the horseshoe estimator
Enes Makalic, Daniel F. Schmidt
In this note we derive a simple Bayesian sampler for linear regression with the horseshoe hierarchy. A new interpretation of the horseshoe model is presented, and extensions to log…
Genome disorder and breast cancer susceptibility
Conor Smyth, Iva Špakulova, Owen Cotton-Barratt +12
Many common diseases have a complex genetic basis in which large numbers of genetic variations combine with environmental and lifestyle factors to determine risk. However, quantify…