17 citations · 29 across the 22 of their papers we have counts for
26 papers
Bayesian Markov-Switching Partial Reduced-Rank Regression
Maria F. Pintado, Matteo Iacopini, Luca Rossini +1
Reduced-Rank (RR) regression is a powerful dimensionality reduction technique but it overlooks any possible group configuration among the responses by assuming a low-rank structure…
MINERVA: Mutual Information Neural Estimation for Supervised Feature Selection
Taurai Muvunza, Egor Kraev, Pere Planell-Morell +1
Existing feature filters rely on statistical pair-wise dependence metrics to model feature-target relationships, but this approach may fail when the target depends on higher-order…
A statistical test for network similarity
Pierre Miasnikof, Alexander Y. Shetopaloff
In this article, we revisit and expand our prior work on graph similarity. As with our earlier work, we focus on a view of similarity which does not require node correspondence bet…
A clusterability test for directed graphs
Mario R. Guarracino, Pierre Miasnikof, Alexander Y. Shestopaloff +3
In this article, we extend a statistical test of graph clusterability, the test, to directed graphs with no self loops. The test, originally designed for undirected graphs,…
ClusterLOB: Enhancing Trading Strategies by Clustering Orders in Limit Order Books
Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff +1
In the rapidly evolving world of financial markets, understanding the dynamics of limit order book (LOB) is crucial for unraveling market microstructure and participant behavior. W…
Generalized Factor Neural Network Model for High-dimensional Regression
Zichuan Guo, Mihai Cucuringu, Alexander Y. Shestopaloff
We tackle the challenges of modeling high-dimensional data sets, particularly those with latent low-dimensional structures hidden within complex, non-linear, and noisy relationship…
The Market Maker's Dilemma: Navigating the Fill Probability vs. Post-Fill Returns Trade-Off
Jakob Albers, Mihai Cucuringu, Sam Howison +1
Using data from a live trading experiment on the Binance Bitcoin perpetual, we examine the effects of (i) basic order book mechanics and (ii) the persistence of price changes from…
A unifying framework for generalised Bayesian online learning in non-stationary environments
Gerardo Duran-Martin, Leandro Sánchez-Betancourt, Alexander Y. Shestopaloff +1
We propose a unifying framework for methods that perform probabilistic online learning in non-stationary environments. We call the framework BONE, which stands for generalised (B)a…
Low Volatility Stock Portfolio Through High Dimensional Bayesian Cointegration
Parley R Yang, Alexander Y Shestopaloff
We employ a Bayesian modelling technique for high dimensional cointegration estimation to construct low volatility portfolios from a large number of stocks. The proposed Bayesian f…
Bayesian Partial Reduced-Rank Regression
Maria F. Pintado, Matteo Iacopini, Luca Rossini +1
Reduced-rank (RR) regression may be interpreted as a dimensionality reduction technique able to reveal complex relationships among the data parsimoniously. However, RR regression m…
Stock Volume Forecasting with Advanced Information by Conditional Variational Auto-Encoder
Parley R Yang, Alexander Y Shestopaloff
We demonstrate the use of Conditional Variational Encoder (CVAE) to improve the forecasts of daily stock volume time series in both short and long term forecasting tasks, with the…
Outlier-robust Kalman Filtering through Generalised Bayes
Gerardo Duran-Martin, Matias Altamirano, Alexander Y. Shestopaloff +5
We derive a novel, provably robust, and closed-form Bayesian update rule for online filtering in state-space models in the presence of outliers and misspecified measurement models.…
Bayesian Analysis of High Dimensional Vector Error Correction Model
Parley R Yang, Alexander Y Shestopaloff
Vector Error Correction Model (VECM) is a classic method to analyse cointegration relationships amongst multivariate non-stationary time series. In this paper, we focus on high dim…
Dynamic Time Warping for Lead-Lag Relationships in Lagged Multi-Factor Models
Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff +1
In multivariate time series systems, lead-lag relationships reveal dependencies between time series when they are shifted in time relative to each other. Uncovering such relationsh…
Low-rank extended Kalman filtering for online learning of neural networks from streaming data
Peter G. Chang, Gerardo Durán-Martín, Alexander Y Shestopaloff +2
We propose an efficient online approximate Bayesian inference algorithm for estimating the parameters of a nonlinear function from a potentially non-stationary data stream. The met…
Uncertainty Quantification in Bayesian Reduced-Rank Sparse Regressions
Maria F. Pintado, Matteo Iacopini, Luca Rossini +1
Reduced-rank regression recognises the possibility of a rank-deficient matrix of coefficients. We propose a novel Bayesian model for estimating the rank of the coefficient matrix,…
Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models
Yichi Zhang, Mihai Cucuringu, Alexander Y. Shestopaloff +1
In multivariate time series systems, key insights can be obtained by discovering lead-lag relationships inherent in the data, which refer to the dependence between two time series…
Fragmentation, Price Formation, and Cross-Impact in Bitcoin Markets
Jakob Albers, Mihai Cucuringu, Sam Howison +1
In light of micro-scale inefficiencies induced by the high degree of fragmentation of the Bitcoin trading landscape, we utilize a granular data set comprised of orderbook and trade…
Graph Distances and Clustering
Pierre Miasnikof, Alexander Y. Shestopaloff, Leonidas Pitsoulis +1
With a view on graph clustering, we present a definition of vertex-to-vertex distance which is based on shared connectivity. We argue that vertices sharing more connections are clo…
A Statistical Density-Based Analysis of Graph Clustering Algorithm Performance
Pierre Miasnikof, Alexander Y. Shestopaloff, Anthony J. Bonner +2
Measuring graph clustering quality remains an open problem. To address it, we introduce quality measures based on comparisons of intra- and inter-cluster densities, an accompanying…
Replica Conditional Sequential Monte Carlo
Alexander Y. Shestopaloff, Arnaud Doucet
We propose a Markov chain Monte Carlo (MCMC) scheme to perform state inference in non-linear non-Gaussian state-space models. Current state-of-the-art methods to address this probl…
New reconstruction and data processing methods for regression and interpolation analysis of multidimensional big data
Yuri K. Shestopaloff, Alexander Y. Shestopaloff
The problems of computational data processing involving regression, interpolation, reconstruction and imputation for multidimensional big datasets are becoming more important these…
Sampling latent states for high-dimensional non-linear state space models with the embedded HMM method
Alexander Y. Shestopaloff, Radford M. Neal
We propose a new scheme for selecting pool states for the embedded Hidden Markov Model (HMM) Markov Chain Monte Carlo (MCMC) method. This new scheme allows the embedded HMM method…
Efficient Bayesian inference for stochastic volatility models with ensemble MCMC methods
Alexander Y. Shestopaloff, Radford M. Neal
In this paper, we introduce efficient ensemble Markov Chain Monte Carlo (MCMC) sampling methods for Bayesian computations in the univariate stochastic volatility model. We compare…