A Latent-Class Model for Clustering Incomplete Linear and Circular Data in Marine Studies
Francesco Lagona, Marco Picone
Abstract
Identification of representative regimes of wave height and direction under different wind conditions is complicated by issues that relate to the specification of the joint distribution of variables that are defined on linear and circular supports and the occurrence of missing values. We take a latent-class approach and jointly model wave and wind data by a finite mixture of conditionally independent Gamma and von Mises distributions. Maximum-likelihood estimates of parameters are obtained by exploiting a suitable EM algorithm that allows for missing data. The proposed model is validated on hourly marine data obtained from a buoy and two tide gauges in the Adriatic Sea.
Topics & Concepts
BuoyCluster analysisLatent class modelMaximum likelihoodvon Mises distributionClass (philosophy)Missing dataWind speedMathematicsLatent variableWind waveStatisticsData miningComputer scienceMeteorologyvon Mises yield criterionGeologyFinite element methodGeographyEngineeringArtificial intelligenceMarine engineeringStructural engineeringOceanographyBayesian Methods and Mixture ModelsOcean Waves and Remote SensingOceanographic and Atmospheric Processes