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A Metropolis-class sampler for targets with non-convex support
We aim to improve upon the exploration of the general-purpose random walk Metropolis algorithm when the target has non-convex support
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Efficient stochastic optimisation by unadjusted Langevin Monte Carlo
Stochastic approximation methods play a central role in maximum likelihood estimation problems involving intractable likelihood functions, such as...
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Deep Learning for Constrained Utility Maximisation
This paper proposes two algorithms for solving stochastic control problems with deep learning, with a focus on the utility maximisation problem. The...
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Computing highest density regions for continuous univariate distributions with known probability functions
We examine the problem of computing the highest density region (HDR) in a computational context where the user has access to a density function and...
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Correction for Optimisation Bias in Structured Sparse High-Dimensional Variable Selection
In sparse high-dimensional data, the selection of a model can lead to an overestimation of the number of nonzero variables. Indeed, the use of an...
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Convergence rates for optimised adaptive importance samplers
Adaptive importance samplers are adaptive Monte Carlo algorithms to estimate expectations with respect to some target distribution which adapt ...
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Variational Bayes on manifolds
Variational Bayes (VB) has become a widely-used tool for Bayesian inference in statistics and machine learning. Nonetheless, the development of the...
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Analysis of stochastic gradient descent in continuous time
Stochastic gradient descent is an optimisation method that combines classical gradient descent with random subsampling within the target functional....
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Information criteria bias correction for group selection
The main contribution of this paper lies in the extension towards group lasso of a Mallows’ Cp-like information criterion used in finetuning the...
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Penalized wavelet estimation and robust denoising for irregular spaced data
Nonparametric univariate regression via wavelets is usually implemented under the assumptions of dyadic sample size, equally spaced fixed sample...
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COVID-19: Optimal Design of Serosurveys for Disease Burden Estimation
We provide a methodology by which an epidemiologist may arrive at an optimal design for a survey whose goal is to estimate the disease burden in a...
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Old and New Perspectives on Optimal Scaling
Processing in machine learning qualitative variables having a very large number of modalities is an opportunity to revisit the theory of optimal...
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Bernstein–von Mises Theorem and Misspecified Models: A Review
This is a review of asymptotic and non-asymptotic behaviour of Bayesian methods under model specification. In particular we focus on consistency,...
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Semi-automated simultaneous predictor selection for regression-SARIMA models
Deciding which predictors to use plays an integral role in deriving statistical models in a wide range of applications. Motivated by the challenges...
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On Weighted Least Squares Estimators for Chirp Like Model
In this paper we have considered the chirp like model which has been recently introduced, and it has a very close resemblance with a chirp model. We...
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Imprecise Discrete-Time Markov Chains
I present a short and easy introduction to a number of basic definitions and important results from the theory of imprecise Markov chains in discrete...
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Shrinkage Methods
Expression ( 6.51 ) indicates how prediction ability is governed by bias and variance. As models...
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Statistical Challenges in Mutational Signature Analyses of Cancer Sequencing Data
Cancer is a disease driven and characterised by mutations in the DNA. Different categorisations of DNA mutations have allowed the identification of...
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Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process
Statistical testing is classically used as an exploratory tool to search for association between a phenotype and many possible explanatory variables....
