Journal of Data Science

Abstract: A new set of methods are developed to perform cluster analysis of functions, motivated by a data set consisting of hydraulic gradients at several locations distributed across a wetland complex. The methods build on previous work on clustering of functions, such as Tarpey and Kinateder (2003) and Hitchcock et al. (2007), but explore functions generated from an additive model decomposition (Wood, 2006) of the original time series. Our decomposition targets two aspects of the series, using an adaptive smoother for the trend and circular spline for the diurnal variation in the series. Different measures for comparing locations are discussed, including a method for efficiently clustering time series that are of different lengths using a functional data approach. The complicated nature of these wetlands are highlighted by the shifting group memberships depending on which scale of variation and year of the study are considered.

Abstract: State lotteries employ sales projections to determine appropri ate advertised jackpot levels for some of their games. This paper focuses on prediction of sales for the Lotto Texas game of the Texas Lottery. A novel prediction method is developed in this setting that utilizes functional data analysis concepts in conjunction with a Bayesian paradigm to produce predictions and associated precision assessments.