Changepoints in non-Gaussian autocorrelated time series

Music City Center 

We present a method to identify multiple changepoints in non-Gaussian (discrete or continuous) autocorrelated time series. A transformation technique is used to derive the likelihood of a non-Gaussian series with an assumed latent autocorrelated Gaussian series, enabling penalized likelihood methods to handle changepoint identification in the non-Gaussian scenarios. Here we present the results when the marginal distribution is a continuous proportion, modeled via a Beta distribution. Simulations demonstrating the efficacy of the methods will be shown and we explore an interesting dataset from the world of sports, the proportion of home runs hit by Major League Baseball batters from 1920-2023.