On December 2nd, Dr. Bahman Nasseroleslami from the School of Medicine presented his recent work entitled ‘Non-Parametric Rank Statistics for Spectral Power and Coherence.’ An abstracts for Bahman’s talk is below. Despite advances in multivariate spectral analysis of neural signals, the statistical inference of measures such as spectral power and coherence in practical and real-life scenarios … Read more

On November 25th, Sultan Imangaliyev presented his recent work which uses interpretable machine learning methods predict individual patient’s risk of cervical cancer. An abstract for Sultan’s talk is below. Despite the possibility of prevention with cytological screening, cervical cancer remains a significant cause of more than half a million cases per year, killing more than … Read more

On November 18th, Philip Cooney presented recent work ‘Hazard Changepoint Analysis with Collapsing Changepoint Models’. An abstract for the talk is below: There are a wide variety of applications for statistical models which assess how the parameters underlying a data generating process may change over time. One function which is subject to change is the … Read more

On November 11th, the Seminar Series continued with Joshua presenting recent work on ‘Clustering Big Data with Mixed Features’. An abstract for the talk is below. Clustering large, mixed data is a central problem in data mining. Many approaches adopt the idea of k-means, and hence are sensitive to initialisation, detect only spherical clusters, and … Read more

On September 9th, the Reading Group discussed ‘A modern maximum-likelihood theory for high-dimensional logistic regression.’  by Pragya Sur and Emmanuel J. Candès. The abstract for the paper is below: Students in statistics or data science usually learn early on that when the sample size n is large relative to the number of variables p, fitting … Read more

On September 9th, the Reading Group discussed ‘The Bayesian Bootstrap’, by Donald B. Rubin. The abstract for the paper is below: “The Bayesian bootstrap is the Bayesian analogue of the bootstrap. Instead of simulating the sampling distribution of a statistic estimating a parameter, the Bayesian bootstrap simulates the posterior distribution of the parameter; operationally and … Read more

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