Search
Search Results
-
Approximated Gaussian Random Field Under Different Parameterizations for MCMC
Fitting spatial models with a Gaussian random field as spatial random effect poses computational challenges for Markov Chain Monte Carlo (MCMC)...
-
Latent Dirichlet Allocation and Hidden Markov Models to Identify Public Perception of Sustainability in Social Media Data
To help guide a just transition to a sustainable society and onboard the local communities, researchers can identify events of public interest...
-
Models of Network Delay
In this paper several mathematical models for end-to-end network delay are derived, where exponential wait times at intermediate network routers are...
-
Statistical Modelling for Big and Little Data
While the difference between “Data Science” and “Statistics” disciplines is, at best, blurred, many people associate machine learning methods and big...
-
Addressing Covariate Lack in Unit-Level Small Area Models Using GAMLSS
The primary goal of this study is to estimate the Theil index using a unit-level Small Area Estimation (SAE) model. This has lead two primary...
-
Bayesian Hidden Markov Models for Early Warning
We show how Bayesian hidden Markov models may be employed to build early warning systems of particular risky events. The adopted model formulation...
-
Shape Analysis of AF Segments for Rapid Assessment of Mohs Layers for BCC Presence by AF-Raman Microscopy
An automated method to detect Basal Cell Carcinoma (BCC) relies on Autofluorescence (AF) imaging guiding Raman microscopy to obtain biochemical...
-
Estimating Dose and Time of Exposure from a Protein-Based Radiation Biomarker
In order to analyze the potential damage to the human body caused by exposure to ionizing radiation, one needs to have an estimation of the dose of...
-
An Updated Wilcoxon–Mann–Whitney Test
The Wilcoxon–Mann–Whitney test, also known as the Wilcoxon rank–sum test and the Mann-Whitney U test, is a non–parametric method used to compare...
-
Estimating a Lower Bound of the Population Size in Capture-Recapture Experiments with Right Censored Data
In this work, we present a new non-parametric approach for estimating a lower bound of the population size in capture-recapture experiments with...
-
Functional Copula Graphical Regression Model for Analysing Brain-Body Rhythm
In physiology, organ functions can be modelled as networks with individual regulatory mechanisms, forming a broader system through continuous...
-
Derivatives of the Log of a Determinant
We present an efficient way to calculate effective model dimensions, using automated differentiation of the Cholesky algorithm. The method is...
-
Shrinkage in a Bayesian Panel Data Model with Time-Varying Coefficients
We consider regression models for panel data, where regression effects and within subject dependence are allowed to vary over time. We adopt a...
-
Integrating Single Index Effects in Generalized Additive Models
Linearly combining the elements of a vector of covariates to get a scalar-valued feature is common practice in regression modelling. In this work, we...
-
Parametric and Non-parametric Bayesian Imputation for Right Censored Survival Data
A common feature of much survival data is censoring due to incompletely observed lifetimes. Survival analysis methods have been designed to take...
-
Optimizing Variable Selection in Multi-Omics Datasets: A Focus on Exclusive Lasso
Multi-omics datasets pose significant challenges due to their structured nature, where highly correlated variables are grouped within a complex,...
-
A Bayesian Markov-Switching for Smooth Modelling of Extreme Value Distributions
Markov-switching models are attractive for analysing time series that exhibit different stochastic processes along different periods, and where the...
-
An Underrated Prior Distribution for Proportions. The Logistic–Normal for Dynamical Football Predictions
The result of a football match in terms of Home–Win, Draw or Away–Win can be modelled by considering the observed outcome as a realization of a...
-
On Scan Statistics Through the Finite Markov Chain Imbedding Approach
This chapter provides a short review of the finite Markov chain imbedding approach for studying the distributions of discrete scan statistics,...
-
Shocks, Scans, and Reliability Systems
This chapter summarizes the close connection between one of the widely studied shock models known as δ-shock model and runs/scans. Under discrete...