Journal of the Royal Statistical Society. Series A (Statistics in Society), Vol. 181, No. 3 (2018), pp. 635-647 (13 pages) Statistical agencies are increasingly adopting synthetic data methods for ...
Bayesian nonparametric mixture models represent a powerful statistical framework that extends traditional mixture modelling by allowing the number of mixture components to be inferred from the data ...
The Stiefel manifold Vp,d is the space of all d × p orthonormal matrices, with the d−1 hypersphere and the space of all orthogonal matrices constituting special cases. In modeling data lying on the ...
The covariance matrix of asset returns is the key input for many problems in finance and economics. This paper introduces a Bayesian nonparametric method to estimate the ex post covariance matrix from ...
Kernel density estimation (KDE) and nonparametric methods form a cornerstone of contemporary statistical analysis. Unlike parametric approaches that assume a specific functional form for the ...
You are invited to attend the following M.A.Sc. (Quality Systems Engineering) thesis examination. Data clustering is a fundamental unsupervised learning approach that impacts several domains such as ...
In the Big Data era, many scientific and engineering domains are producing massive data streams, with petabyte and exabyte scales becoming increasingly common. Besides the explosive growth in volume, ...
On June 25, 2018, The Institute of Statistical Mathematics (ISM) associated with the Research Organization of Information and Systems (ROIS) and the Japan Statistical Society (JSS) have jointly ...
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