54: I-ACRE: An Autoregressive Conditional Extremes Model for Incomplete Panel Data

Thomas M. Menino Convention & Exhibition Center 

The detection of planets outside of our solar System, called exoplanets, is crucial for understanding the universe and learning about Earth's formation. Like Earth, exoplanets orbit a star, and require careful measurements to detect them. One measurement astronomers use for detection is the estimate of a star's velocity with respect to an observer, called its radial velocity (RV). RVs are naturally measured over time and across different wavelengths of light and can be analyzed as a panel using the line-by-line RV method. This method is used to both detect exoplanets and measure their mass but is hampered by outliers and missing observations. Indeed, anomalous behavior in data streams often indicates astrophysical or instrumental deviations unrelated to an exoplanet. To aid in predicting these anomalies, we propose the incomplete autoregressive conditional Fréchet model with time-varying parameters for regional extremes (I-ACRE). As model predictions follow a Fréchet distribution with a closed-form expression, this allows for confidence intervals, whose expected coverage levels (e.g., 90%) closely align with observed coverage levels in out-of-sample predictive tests. Unlike other models used to study extreme values, I-ACRE flexibly and quickly accommodates panel data with missing values by feeding forward predictions. By quantifying the probability of future extremes, I-ACRE provides a diagnostic framework for flagging anomalous observations that can limit the success of exoplanet detection algorithms.

extreme value theory

tail index dynamics

exoplanet detection

panel data

missing data 

Astrostatistics Interest Group