Clustering of individuals with dementia based on MRI features

Dementia, characterized by a significant decline in cognitive abilities, encompasses various neurodegenerative disorders. This includes Alzheimer's disease (AD), frontotemporal dementia (FTD) and their clinical manifestation named primary progressive aphasia (PPA), mainly involving language difficulties. In general these diseases exhibit some...

Risk Aversion and Guided Exploration in Safety-Constrained Reinforcement Learning

In traditional reinforcement learning (RL) problems, agents can explore environments to learn optimal policies through trials and errors that are sometimes unsafe. However, unsafe interactions with environments are unacceptable in many safety-critical problems, for instance in robot navigation tasks. Even though RL agents can be trained in...

Evaluating the effect of emotions on academic achievement in mathematics: A quantile regression and Bayesian quantile regression approach

Quantile regression is a useful method to analyse data such that the estimates are more robust to outliers and the conditional distributions are more reliable for asymmetric distributions with respect to the commonly used ordinary least squares regression. Besides this, the quantile regression analysis might also include extra information on the...

Integrating the flow regime and water quality effects into a niche-based metacommunity dynamics model for river ecosystems

Aquatic community dynamics are closely dominated by flow regime and water quality conditions, which are increasingly threatened by dam regulation, water diversion, and nutrition pollution. However, further understanding of the ecological impacts of flow regime and water quality conditions on aquatic multi-population dynamics has rarely been...

Gradient boosting for extreme quantile regression

Extreme quantile regression provides estimates of conditional quantiles outside the range of the data. Classical quantile regression performs poorly in such cases since data in the tail region are too scarce. Extreme value theory is used for extrapolation beyond the range of observed values and estimation of conditional extreme quantiles....

Statistical post processing of extreme weather forecasts

In this thesis we develop several statistical methods to estimate high conditional quantiles to use for statistical post-processing of weather forecasts. We propose methodologies that combine theory from extreme value statistics and machine learning algorithms in order to estimate high conditional quantiles in large covariate spaces. In...

Dynamic dependence and predictability between volume and return of Non-Fungible Tokens (NFTs): The roles of market factors and geopolitical risks

We examine the dependence between volume and returns for the NFT market and three sub-markets (Cryptokitties, Cryptopunks, and Decentraland) using both quantile cross-spectral coherency and quantile regression techniques. Results from both techniques show significant evidence of dependence between NFT return and volume. Dependence between volume...

Day Ahead Market price scenario generation using a Combined Quantile Regression Deep Neural Network and a Non-parametric Bayesian Network: A framework for risk-based Demand Response

In this manuscript we propose a methodology to generate electricity price scenarios from probabilistic forecasts. Using a Combined Quantile Regression Deep Neural Network, we forecast hourly marginal price distribution quantiles for the DAM on which we fit parametric distributions. A Non-parametric Bayesian Network (BN) is applied to sample from...

Probabilistic DAM price forecasting using a combined Quantile Regression Deep Neural Network with less-crossing quantiles

In this paper we propose a Quantile Regression Deep Neural Network capable of forecasting multiple quantiles in one model using a combined quantile loss function, and apply it to probabilistically forecast the prices of 8 European Day Ahead Markets. We show that the proposed loss function significantly reduces the quantile crossing problem to ...

Predictive maintenance for utility scale solar parks: A machine learning approach towards early fault detection for PV inverters

The growing demand and improvements in manufacturing capabilities, supported by government subsidies, has allowed the increase in the installed capacity of utility scale solar parks. Due to the remoteness in their location, the costs associated with dispatching personnel for maintenance is extremely high. A major contribution towards unscheduled...

Understanding the relationship between extreme rainfall scaling and dew point temperature

Due to global warming, temperatures are expected to rise and, with it, the increase in moisture holding capacity of the atmosphere. Changes in precipitation extremes with temperature are governed by the Clausius-Clapeyron relationship (CC), which states that precipitation increases 7% per degree of warming. While global models and observations...

Improving precipitation forecasts using extreme quantile regression

Aiming to estimate extreme precipitation forecast quantiles, we propose a nonparametric regression model that features a constant extreme value index. Using local linear quantile regression and an extrapolation technique from extreme value theory, we develop an estimator for conditional quantiles corresponding to extreme high probability...

Estimating predictive hydrological uncertainty by dressing deterministic and ensemble forecasts; a comparison, with application to Meuse and Rhine

Two statistical post-processing approaches for estimation of predictive hydrological uncertainty are compared: (i) ‘dressing’ of a deterministic forecast by adding a single, combined estimate of both hydrological and meteorological uncertainty and (ii) ‘dressing’ of an ensemble streamflow forecast by adding an estimate of hydrological...

Non-parametric extreme quantile estimation for the common shaped tail model: Forecasting extreme precipitation by post-processing precipitation from a numerical weather prediction model

The estimation of extreme quantile curves of a family of conditional distributions is a non-trivial problem, due to the data-sparseness in the tail of the distribution. This thesis considers the problem of post-processing extreme precipitation forecasts in Friesland from the numerical weather prediction model HARMONIE. Assuming forecasts are...