CASUS Annual Workshop | Małgorzata Bogdan | University of Wrocław
Channel: CASUS.science
Date: 13/10/2020

"Statistical methods for reduction of dimension of ”fat” data sets"
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09.10.2020
2nd CASUS Annual Workshop 2020
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Abstract:
"Large data sets are now routinely collected in many fields of industry or science. Many of these data sets are ''fat'' – i.e. the number of measurements for one individual substantially exceeds the number of records in such data set. ''Fat'' data sets create opportunities for identifying new patterns but their analysis brings many statistical challenges.  One of the most dangerous and often neglected phenomena is the problem of  ''false discoveries'', which arise due to the random fluctuations in the data. Statistical methods for dimensionality reduction address this problem in a structured way and often provide some guarantees concerning the control over false discoveries. In this talk we will present several methods for dimensionality reduction developed by the Wroclaw team in collaboration with colleagues from many research institutions in Europe and USA. We will cover some novel extensions of the Bayesian Information Criterion and  of the Sorted L-One Penalized Estimator (SLOPE), both for supervised and unsupervised learning, some modifications of the knockoff methodology for controlling the False Discovery Rate (FDR) and the new method for unsupervised dimensionality reduction by the multiple latent component clustering."