The dynamics of ideology drift among U.S. Supreme Court justices: A functional data analysis

Abstract

We study the U.S. Supreme Court dynamics by analyzing the temporal evolution of the underlying policy positions of the Supreme Court Justices as reflected by their actual voting data, using functional data analysis methods. The proposed fully flexible nonparametric method makes it possible to dissect the time-dynamics of policy positions at the level of individual Justices, as well as providing a comprehensive view of the ideology evolution over the history of Supreme Court since its establishment. In addition to quantifying individual Justice’s policy positions, we uncover average changes over time and also the major patterns of change over time. Additionally, our approach allows for representing highly complex dynamic trajectories by a few principal components which complements other models of analyzing and predicting court behavior.

Spatiotemporal Variation in Cave Percolation Waters: A Functional Approach

“…FPCA is an FDA method that analyses functions instead of observations, capturing temporal structure to reduce the effects of temporal autocorrelation (Zhou and Müller, 2022). It is still a relatively novel technique for the analysis of longitudinal time series analysis, with hydrological studies so far only using FDA techniques on stream discharge and streamflow data (Ternynck et al, 2016).…”