Statistics: Recent submissions
Now showing items 21-40 of 139
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Semantic image segmentation based on spatial relationships and inexact graph matching
(2020)We propose a method for semantic image segmentation, combining a deep neural network and spatial relationships between image regions, encoded in a graph representation of the scene. Our proposal is based on inexact graph ... -
An Integrated Framework for Estimating the Number of Classes with Application for Species Estimation
(Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2021)The two most common approaches for estimating the number of distinct classes within a population are either to use sampling data directly with combinatorial arguments or to extrapolate historical discovery data. However, ... -
Image Restoration Using Deep Learning
(Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2020)In this thesis, we propose several convolutional neural network (CNN) architectures with fewer parameters compared to state-of-the-art deep structures to restore original images from degraded versions. Employing fewer ... -
A Heuristic Policy for Maintaining Multiple Multi-State Systems
(2020)This work is concerned with the optimal allocation of limited maintenance resources among a collection of competing multi-state systems, and the dynamic of each multi-state system is modelled by a Markov chain. Determining ... -
Matching-adjusted indirect comparisons: identifying method variations and implementing models in R
(Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2020)In the framework of evidence-based medicine, comparative effectiveness research is a fundamental activity to the development of pharmaceutical products and medical treatments. For a given medical condition, several competing ... -
Efficient and scalable inference for generalized student - T process models
(Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2020)Gaussian Processes are a popular, nonparametric modelling framework for solving a wide range of regression problems. However, they are suffering from 2 major shortcomings. On the one hand, they require efficient, approximate ... -
Competing risks of default and prepayment of mortgage market
(Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2020)Using a large data set on the Single family home loans from The Federal Home Loan Mortgage Corporation (FHLMC), sponsored by the US government, this research studies the economic factors affecting the competing risks of ... -
Modelling the distribution of grouped survival data via dependant neutral-to-the-right priors
(Trinity College Dublin. School of Computer Science & Statistics. Discipline of Statistics, 2020)With each update of its browser, Firefox receives reports of the time of discovery of a large number of bugs associated with that update. This process yields survival data which is separated by update into groups and often ... -
A Bayesian approach to modeling mortgage default and prepayment
(2019)In this paper we present a Bayesian competing risk proportional hazards model to describe mortgage defaults and prepayments. We develop Bayesian inference for the model using Markov chain Monte Carlo methods. Implementation ... -
Estimating redshift distributions using Hierarchical Logistic Gaussian processes
(2019)This work uses hierarchical logistic Gaussian processes to infer true redshift distributions of samples of galaxies, through their cross-correlations with spatially overlapping spectroscopic samples. We demonstrate that ... -
Vine Copula Approximation: A Generic Method for Coping with Conditional Dependence
(2018)Pair-copula constructions (or vine copulas) are structured, in the layout of vines, with bivariate copulas and conditional bivariate copulas. The main contribution of the current work is an approach to the long-standing ... -
Model selection with application to gamma process and inverse Gaussian process
(2016)The gamma process and the inverse Gaussian process are widely used in condition-based maintenance. Both are suitable for modelling monotonically increasing degradation processes. Hence, one challenge for practitioners ... -
Degradation-Based Maintenance Using Stochastic Filtering for Systems under Imperfect Maintenance
(2015)The stationaryWiener process is widely used in modeling degradation processes, mainly due to the existence of an analytical expression of the first hitting time distribution. However, it is only appropriate for modelling ... -
A Stochastic EM Algorithm for Progressively Censored Data Analysis
(2014)Progressive censoring technique is useful in lifetime data analysis. Simple approaches to progressive data analysis are crucial for its widespread adoption by reliability engineers. This study develops an efficient yet ... -
A Condition-Based Maintenance Strategy for Heterogeneous Populations
(2014)This paper develops a maintenance strategy, called inspection-replacement policy, to cope with heterogeneous populations. Burn-in is the procedure by which most of the defective products in a heterogeneous population can ... -
Lower Confidence Limit for Reliability Based on Grouped Data with a Quantile Filling Algorithm
(2014)The purpose of this article is to derive a lower confidence limit for reliability given a grouped data set. This is done by using a quantile filling algorithm which generates pseudo failure data from grouped data. A general ... -
A Bivariate Maintenance Policy for Multi-State Repairable Systems with Monotone Process
(2013)In this paper, a sequential failure limit maintenance policy for a repairable system is studied. The system is assumed to have states, including one working state and failure states, and the multiple failure states are ... -
Continuous-Observation Partially Observable Semi-Markov Decision Processes for Machine Maintenance
(2017)Partially observable semi-Markov decision processes (POSMDPs) provide a rich framework for planning under both state transition uncertainty and observation uncertainty. In this paper, we widen the literature on POSMDP by ... -
An Ameliorated Improvement Factor Model for Imperfect Maintenance and Its Goodness of Fit
(2017)Maintenance actions can be classified, according to their efficiency, into three categories: perfect maintenance, imperfect maintenance, and minimal maintenance. To date, the literature on imperfect maintenance is voluminous, ... -
Weighted Clustering Ensemble: A Review
(2022)Clustering ensemble has emerged as a powerful tool for improving both the robustness and the stability of results from individual clustering methods. Weighted clustering ensemble arises naturally from clustering ensemble. ...
