research
∙
02/28/2023
Finite sample inference for empirical Bayesian methods
In recent years, empirical Bayesian (EB) inference has become an attract...
share
research
∙
01/26/2023
Ontology-based Solution for Building an Intelligent Searching System on Traffic Law Documents
In this paper, an ontology-based approach is used to organize the knowle...
research
∙
03/19/2021
Order selection with confidence for finite mixture models
The determination of the number of mixture components (the order) of a f...
research
∙
09/22/2020
An l_1-oracle inequality for the Lasso in mixture-of-experts regression models
Mixture-of-experts (MoE) models are a popular framework for modeling het...
research
∙
08/22/2020
Approximation of probability density functions via location-scale finite mixtures in Lebesgue spaces
The class of location-scale finite mixtures is of enduring interest both...
research
∙
07/23/2020
A binary-response regression model based on support vector machines
The soft-margin support vector machine (SVM) is a ubiquitous tool for pr...
research
∙
09/12/2019
Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models
Mixtures-of-Experts models and their maximum likelihood estimation (MLE)...
research
∙
05/14/2019
Approximate Bayesian computation via the energy statistic
Approximate Bayesian computation (ABC) has become an essential part of t...
research
∙
03/05/2019
Concentration-based confidence intervals for U-statistics
Concentration inequalities have become increasingly popular in machine l...
research
∙
03/01/2019
Approximation by finite mixtures of continuous density functions that vanish at infinity
Given sufficiently many components, it is often cited that finite mixtur...
research
∙
02/09/2019
Asymptotic normality of the time-domain generalized least squares estimator for linear regression models
In linear models, the generalized least squares (GLS) estimator is appli...
research
∙
01/26/2019
On strict sub-Gaussianity, optimal proxy variance and symmetry for bounded random variables
We investigate the sub-Gaussian property for almost surely bounded rando...
research
∙
11/28/2018
Partial Evaluation of Logic Programs in Vector Spaces
In this paper, we introduce methods of encoding propositional logic prog...
research
∙
11/18/2018
The fully-visible Boltzmann machine and the Senate of the 45th Australian Parliament in 2016
After the 2016 double dissolution election, the 45th Australian Parliame...
research
∙
05/11/2018
False Discovery Rate Control Under Reduced Precision Computation
The mitigation of false positives is an important issue when conducting ...
research
∙
04/23/2018
Positive data kernel density estimation via the logKDE package for R
Kernel density estimators (KDEs) are ubiquitous tools for nonparametric ...
research
∙
04/23/2018
Log-transformed kernel density estimation for positive data
Kernel density estimators (KDEs) are ubiquitous tools for nonpara- metri...
research
∙
04/23/2018
Randomized Mixture Models for Probability Density Approximation and Estimation
Randomized neural networks (NNs) are an interesting alternative to conve...
research
∙
03/01/2018
Model-Based Clustering and Classification of Functional Data
The problem of complex data analysis is a central topic of modern statis...
research
∙
09/14/2017
A Novel Algorithm for Clustering of Data on the Unit Sphere via Mixture Models
A new maximum approximate likelihood (ML) estimation algorithm for the m...
research
∙
07/12/2017
An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling
Mixture-of-experts (MoE) models are a powerful paradigm for modeling of ...
research
∙
06/29/2017
Fast Processing of Large Graph Applications Using Asynchronous Architecture
Graph algorithms and techniques are increasingly being used in scientifi...
research
∙
05/12/2017
Iteratively-Reweighted Least-Squares Fitting of Support Vector Machines: A Majorization--Minimization Algorithm Approach
Support vector machines (SVMs) are an important tool in modern data anal...
research
∙
11/12/2016
An Introduction to MM Algorithms for Machine Learning and Statistical
MM (majorization--minimization) algorithms are an increasingly popular t...
research
∙
02/11/2016
