450 – SPEED: Bayesian Models and Inference, Part 2
Application of a Hierarchical Model to Paleoenvironmental Time Series with Latent Times
Aaron Springford
Queen's University
David J. Thomson
Queen's University at Kingston
Observation times in time series data are usually assumed to be known and evenly spaced. These assumptions enable many methods for statistical inference of the measured process, such as ARMA models and spectral estimation using the Fast Fourier Transform. Thankfully, the measurement process is usually controlled by the experimenter, limiting the effect of these assumptions by design. However, for paleoenvironmental studies based on core data, measurement times must be inferred from depth and dating information (using radioisotopes, for example). Inference of measurement times is required for depths with and without dating information. We have previously described a method to estimate chronologies -- the relationship between depth and time -- that provides posterior distributions of sampling times (Springford, 2013). In this paper we extend the results of Springford (2013) to examine the effect of sampling time uncertainty on time series analysis estimates.