Restoration of Missing Data using a Human Adaptive Framework: The Cleansing Algorithm

Improving data quality is of the utmost importance for any data-driven company, as data quality is unmistakably tied to business analytics and processes. One method to improve upon data quality is to restore missing and wrong data entries.  </p><p class="MsoNormal">The goal of this research is construct an algorithm such...

Application of Time Series Analysis to Estimate Drawdown From Multiple Well Fields

In 2018–2020, meteorological droughts over Northwestern Europe caused severe declines in groundwater heads with significant damage to groundwater-dependent ecosystems and agriculture. The response of the groundwater system to different hydrological stresses is valuable information for decision-makers. In this paper, a reproducible, data-driven...

A time-reversed model identification approach to time series forecasting

In this thesis, I introduce a novel model identification approach to time series forecasting. For linear stationary processes, such as AR processes, the direction of time is independent of the model parameters. By combining theoretical principles of time-reversibility in time series with conventional modeling approaches such as information...

The AIC-BIC dilemma: An in-depth look

In research there is often a need to choose between multiple competing models. Two popular criteria for model selection are the AIC and BIC. The AIC excels in estimating the best model for the unknown data generating process. The BIC on the other hand is consistent in finding the true model. It is clear that for model selection these two...

Frequentistische en Bayesiaanse statistiek in de forensische statistiek

In de statistiek zijn er verschillende methodes voor het uitvoeren van model selectie. Het verschil in deze methodes komt voort uit het verschil in stromingen. Voor niet-geneste model selectie zijn de meest ganbare stromingen de Bayes Factor en de likelihood ratio. D. M. Ommen en C. P. Saunders presenteerden theoretische resultaten voor de...

Making Learners (More) Monotone

Learning performance can show non-monotonic behavior. That is, more data does not necessarily lead to better models, even on average. We propose three algorithms that take a supervised learning model and make it perform more monotone. We prove consistency and monotonicity with high probability, and evaluate the algorithms on scenarios where...

Hydrological Interpretation of a Statistical Measure of Basin Complexity

This paper studies how streamflow predictability varies with basin characteristics. We introduce an index of basin complexity that is based on a model of least statistical complexity that is needed to reliably predict daily streamflow of the basin. We then relate it with climate, vegetation and soil characteristics of the basin. Daily...

Long-term Effect of MPH on White Matter Tracts of Adults with ADHD: An Application of Consistent Model Selection followed by TBSS

We focused on analysis of long-term medication influenced white matter tracts based on diffusion-weighted MRI brain images of patients with ADHD disorder. We applied a framework of consistent model selection with Tract-based spatial statistics (TBSS) to give proper and consistent modelling of fiber-crossing in white matter. An orientation atlas...

Dynamic contrast-enhanced MRI of the patellar bone: How to quantify perfusion

Purpose: To identify the optimal combination of pharmacokinetic model and arterial input function (AIF) for quantitative analysis of blood perfusion in the patellar bone using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Materials and Methods: This method design study used a random subset of five control subjects from an...

Categorical based feature modeling on a zero inflated performance measure

ASML produces TwinScan NXT machines that are used for the production of microchips. The machines ensure that an accurate pattern of DUV-light passes a lens and that it is projected as accurate as possible on the wafer. To ensure that the focal point of the converged DUV-light falls exactly onto the wafer, the leveling functionality is of great...

Sworn testimony of the model evidence: Gaussian Mixture Importance (GAME) sampling

What is the “best” model? The answer to this question lies in part in the eyes of the beholder, nevertheless a good model must blend rigorous theory with redeeming qualities such as parsimony and quality of fit. Model selection is used to make inferences, via weighted averaging, from a set of K candidate models, (Formula presented.), and help...

Deriving proper uniform priors for regression coefficients, Parts I, II, and III

It is a relatively well-known fact that in problems of Bayesian model selection, improper priors should, in general, be avoided. In this paper we will derive and discuss a collection of four proper uniform priors which lie on an ascending scale of informativeness. It will turn out that these priors lead us to evidences that are closely...

A toolbox to find the best mechanistic model to predict the behavior of environmental systems

Reliable prediction of the long-term behavior of environmental systems such as Municipal Solid Waste (MSW) landfills is challenging. While many driving forces influence this behavior, characterization of them is limited by measurement techniques. Therefore, a model structure for reliable prediction needs to optimally combine all measured...

Analysis of Diffusion MRI: Disentangling the Entangled Brain

The white matter of the brain contains all the connections between different parts of the grey matter. Many diseases especially affect the brain’s white matter. For instance, the white matter tracts are destroyed in neurodegenerative diseases, such as Alzheimer’s disease. Accordingly, there is a large interest in features of the white matter to...

Complexity regularized hydrological model selection

This paper uses a recently proposed measure of hydrological model complexity in a model selection exercise. It demonstrates that a robust hydrological model is selected by penalizing model complexity while maximizing a model performance measure. This especially holds when limited data is available. Here by a robust model, we mean a model that...

Quantile hydrologic model selection and model structure deficiency assessment: 2. Applications

Quantile hydrologic model selection and structure deficiency assessment is applied in three case studies. The performance of quantile model selection problem is rigorously evaluated using a model structure on the French Broad river basin data set. The case study shows that quantile model selection encompasses model selection strategies based on...

Quantile hydrologic model selection and model structure deficiency assessment: 1. Theory

A theory for quantile based hydrologic model selection and model structure deficiency assessment is presented. The paper demonstrates that the degree to which a model selection problem is constrained by the model structure (measured by the Lagrange multipliers of the constraints) quantifies structural deficiency. This leads to a formal...