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Contributed Papers

Statistical Methods for Time Series Data and Change Point Detection

Sun, Aug 3, 4:00 PM - 5:50 PM Room CC-103A Music City Center

Hui ShenChair

Section on Statistical Learning and Data Science

5 Presentations

Data-driven Tapered Spectral Density Features from Time Series Data

4:05 PM - 4:20 PM

Co-authors: Alex Bank, Ronak Mehta, Alex Bank, Azadeh Yazdan (University of Washington), Zaid Harchaoui (University of Washington)

signal processing feature representation self-supervised learning optogenetics time series multitaper

WITHDRAWN: Deep Learning Versus Machine Learning in Predicting the Future Seasonal Time Series Data

4:20 PM - 4:35 PM

Co-authors: Mian Adnan (University At Albany), Mian Adnan (University At Albany)

Activation Functions Feed Forward Neural Network Monte Carlo Simulation Projection Pursuit Regression

Enhancing Subthreshold Signal Detection: A Multiscale Approach with Adaptive Noise Modeling

4:35 PM - 4:50 PM

Co-authors: Horahenage Dixon Vimalajeewa, Ursula U Muller (Texas A&M University), Horahenage Dixon Vimalajeewa, Brani Vidakovic (Texas A&M University, Statistics Department)

Stochastic resonance Multiscale signal processing Fisher information Wavelet transforms

Hidden Markov models for prediction smoothing in time series classification models

4:50 PM - 5:05 PM

Co-authors: Warren Brettenny (Matrix Design Africa), Warren Brettenny (Matrix Design Africa), David Friskin (Matrix Design Africa), Judy Monyebodi (Matrix Design Africa)

prediction smoothing time series classification hidden markov model

Optimal Change Point Detection in Longitudinal Data: A Two-Step Approach

5:05 PM - 5:20 PM

Co-authors: Md Jobayer Hossain (Nemours Biomedical Research, A.I. DuPont Children's Hospital), Md Jobayer Hossain (Nemours Biomedical Research, A.I. DuPont Children's Hospital)

Change Point Non-linear Curves Longitudinal Data RE-EM tree piecewise linear mixed effects model segmented methods

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