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Recent approaches for flexible and efficient analysis of non-stationary time series

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Friday, February 18, 2022
3:30 pm - 4:30 pm
Raquel Prado, Professor, Statistics, University of California, Santa Cruz
Statistical Science Seminar Series

We present two model-based approaches for analyzing large-dimensional time series data with non-stationary features. During the first part of the talk we present an approach that allows for flexible analysis of multivariate non-stationary time series via dynamic models on the partial autocorrelation domain. We discuss various aspects of these models, including the use of shrinkage priors to deal with overfitting issues, as well as hierarchical extensions. We summarize methods for posterior inference within this class of models, focusing on algorithms for efficient approximate filtering and smoothing. We illustrate the performance of these models and methods in the analysis of simulated and real data arising from environmental and neuroscience applications. In the second part of the talk we discuss fast inference for time-varying quantiles using a dynamic quantile linear model that utilizes the extended asymmetric Laplace family of distributions. The model allows us to incorporate the potential current and lagged effects of covariates on the time-varying quantiles using a transfer function component. We discuss approaches for inference within this class of models, including an efficient importance sampling variational Bayes algorithm for fast approximate inference in large-dimensional settings. We show how these models can be used to estimate the effects of climatological indexes on the estimation of the upper quantiles of integrated water transport (IVT) magnitude data over time, which plays a key role in schemes aiming to automatically detect atmospheric rivers.
Seminars will be held weekly on Fridays 3:30 - 4:30 pm on Zoom. After the seminar, there will be a (virtual) meet-and-greet session to interact with the speaker. Please use the chat on Zoom to ask questions to the speaker. A moderator will collect questions throughout the talk and ask the speaker at appropriate times.

Meeting ID: 923 9738 2385
Passcode: 425966

Type: LECTURE/TALK