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Showing posts from August, 2023

UNSW Stats Seminar 18 August

  18 August, 4 PM AET Luke Yates Postdoctoral Research Fellow, University of Tasmania Hybrid:   Anita B. Lawrence Centre 4082 Zoom link:   https://unsw.zoom.us/j/88495626621 Title: New tools for time series analysis of 'omics' data Abstract: It is peak hour in downtown Transcriptome, where millions of messenger RNA are en route from their local DNA carrying important instructions for protein synthesis and other civic services. A typical transcriptomics (RNA-seq) data set is a snapshot of this busy scene, comprising a sample of extracts of these instruction sequences. A key goal in molecular biology is to determine which sequences (i.e., sets of transcripts or genes) change their expression in response to different treatment conditions to discover molecular mechanisms for biological traits. In this talk, I will give a brief background on the sampling, technical, and statistical processes involved in generating such data sets before focusing on new and existing methods for their

Seminar 8 August @ 1 pm AEST

  Harmonizable Fractional Stable Motion: simultaneous estimators for both parameters   Date: 8 August 2023, Tuesday Time: 1pm AEST Statistics and Stochastic colloquium (part of the Colloquium Series of the Department of Mathematics and Statistics) at La Trobe University jointly organized with  the Kyushu - La Trobe Seminar . Contact the organizers: Andriy Olenko a.olenko@latrobe.edu.au Speaker: Prof Antoine Ayache   ( Lille University, France ) Abstract: There are two classical very different extensions of the well-known Gaussian fractional Brownian motion to non-Gaussian frameworks of heavy-tailed stable distributions: the harmonizable fractional stable motion (HFSM) and the linear fractional stable motion (LFSM). As far as we know, while several articles in the literature, some of which appeared a long time ago, have proposed statistical estimators for the parameters of LFSM, no estimator has yet been proposed in the framework of HFSM. Among other things, what makes statistic