This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 161
Type: Topic Contributed
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract - #307397
Title: Modeling Categorized and Aggregated Dive Depth Data from Marine Mammals
Author(s): Megan Higgs*+ and Jay Ver Hoef
Companies: Montana State University and NOAA
Address: 2-242 Wilson Hall, Bozeman, MT, 59717, USA
Keywords: ordinal ; Bayesian ; hierarchical ; temporal

Ordinal variables are pervasive in ecological and environmental data, often arising from practical constraints imposed at data collection. Variables hypothetically measured on a continuous scale are instead recorded as ordered categories. Ironically, this simplification complicates a statistical analysis. Models based on cumulative probabilities are developing sophistication to account for temporal, spatial, and/or cluster dependence, conceptually connecting the ordinal response to a latent continuous random variable through a vector of cut-points used to clip the continuous distribution. Dependence is induced through correlation among the continuous variables. We provide extensions for data acquired from Time-Depth Recorders affixed to Harbor seals by the NOAA National Marine Mammal Lab, where dive depth is recorded as a depth category, and data are aggregated over 6-hr intervals.

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