A Comparison of Statistical Tools for Identifying Modality in Body Mass Distributions

10.6339/JDS.2014.12(1).1201

Ling Xu；Edward J. Bedrick；Timothy Hanson；Carla Restrepo

Bayesian ； body-size data ； excess mass test ； kernel density estimate ； mixture model

Journal of Data Science

12卷1期（2014 / 01 / 01）

175 - 196

英文

The assessment of modality or ”bumps” in distributions is of interest to scientists in many areas. We compare the performance of four statistical methods to test for departures from unimodality in simulations, and further illustrate the four methods using well-known ecological datasets on body mass published by Holling in 1992 to illustrate their advantages and disadvantages. Silverman's kernel density method was found to be very conservative. The excess mass test and a Bayesian mixture model approach showed agreement among the data sets, whereas Hall and York's test provided strong evidence for the existence of two or more modes in all data sets. The Bayesian mixture model also provided a way to quantify the uncertainty associated with the number of modes. This work demonstrates the inherent richness of animal body mass distributions but also the difficulties for characterizing it, and ultimately understanding the processes underlying them.