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Article
Open AccessThe impact of imputation quality on machine learning classifiers for datasets with missing values
Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imput...
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Article
Open AccessA pipeline to further enhance quality, integrity and reusability of the NCCID clinical data
The National COVID-19 Chest Imaging Database (NCCID) is a centralized UK database of thoracic imaging and corresponding clinical data. It is made available by the National Health Service Artificial Intelligenc...
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Article
Navigating the development challenges in creating complex data systems
Data science systems (DSSs) are a fundamental tool in many areas of research and are now being developed by people with a myriad of backgrounds. This is coupled with a crisis in the reproducibility of such DSS...
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Article
Open AccessAβ-induced vulnerability propagates via the brain’s default mode network
The link between brain amyloid-β (Aβ), metabolism, and dementia symptoms remains a pressing question in Alzheimer’s disease. Here, using positron emission tomography ([18F]florbetapir tracer for Aβ and [18F]FDG t...
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Article
Open AccessInference on Covariance Operators via Concentration Inequalities: k-sample Tests, Classification, and Clustering via Rademacher Complexities
We propose a novel approach to the analysis of covariance operators making use of concentration inequalities. First, non-asymptotic confidence sets are constructed for such operators. Then, subsequent applicat...
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Chapter and Conference Paper
Functional data analysis of neuroimaging signals associated with cerebral activity in the brain cortex
We consider the problem of performing principal component analysis of functional data observed over two-dimensional manifolds. The method is illustrated via the analysis of neuroimaging signals associated with...
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Chapter and Conference Paper
Tests for separability in nonparametric covariance operators of random surfaces
We consider the problem of testing for separability in nonparametric covariance operators of multidimensional functional data is considered. We cast the problem in a tensor product of Hilbert space framework, ...
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Article
Comments on: Extensions of some classical methods in change point analysis
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Article
Parallel sequential Monte Carlo samplers and estimation of the number of states in a Hidden Markov Model
The majority of modelling and inference regarding Hidden Markov Models (HMMs) assumes that the number of underlying states is known a priori. However, this is often not the case and thus determining the approp...
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Article
Open AccessMutation rate plasticity in rifampicin resistance depends on Escherichia coli cell–cell interactions
Variation of mutation rate at a particular site in a particular genotype, in other words mutation rate plasticity (MRP), can be caused by stress or ageing. However, mutation rate control by other factors is le...
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Article
Distribution of Statistics of Hidden State Sequences Through the Sum-Product Algorithm
We compute exact distributions of statistics of hidden state sequences in general settings. Distributions are computed for undirected and directed graphical models that are represented using conditional random...
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Chapter and Conference Paper
Power Analysis for Functional Change Point Detection
Change point detection in sequences of functional data is examined where the functional observations are dependent. The theoretical properties for tests for at most one change are derived with a special focus ...
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Article
The Role of Additional Information in Option Pricing: Estimation Issues for the State Space Model
We consider two competing financial state space models and investigate whether additional information in the form of option price data is helpful to the estimation of either the unobservable state variable (vo...
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Chapter and Conference Paper
Reproducibility Analysis of Event-Related fMRI Experiments Using Laguerre Polynomials
In this study, we introduce the use of orthogonal causal Laguerre polynomials for analyzing data collected in event-related functional magnetic resonance imaging (fMRI) experiments. This particular family of p...
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Article
On the logic of hypothesis testing in functional imaging
Statistics is nowadays the customary language of functional imaging. It is common to express an experimental setting as a set of null hypotheses over complex models and to present results as maps of p-values deri...
