Estimating overlap of daily activity patterns from camera trap data

DOI10.1198/jabes.2009.08038zbMath1306.62327OpenAlexW2085102787MaRDI QIDQ2260095

M. Linkie, Martin S. Ridout

Publication date: 5 March 2015

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1198/jabes.2009.08038

zbMATH Keywords

kernel density estimationcircular dataoverlap coefficientclouded leopard (neofelis diardi)golden cat (catopuma temminckii)Sumatran tiger (panthera tigris)trigonometric sum distribution

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12)

Related Items (7)

Joint temporal point pattern models for proximate species occurrence in a fixed area using camera trap data ⋮ Kernel method for overlapping coefficients estimation ⋮ A two-species occupancy model with a continuous-time detection process reveals spatial and temporal interactions ⋮ Statistical development of animal density estimation using random encounter modelling ⋮ Distributions for spherical data based on nonnegative trigonometric sums ⋮ An Efficient Method to Determine the Degree of Overlap of Two Multivariate Distributions ⋮ overlap

Uses Software

• KernSmooth
• CircStats
• circular

Cites Work

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• Nonparametric estimation of the coefficient of overlapping-theory and empirical application
• Automatic bandwidth selection for circular density estimation
• Nonparametric estimation of an affinity measure between two absolutely continuous distributions with hypotheses testing applications
• Nonparametric estimation of Matusita's measure of affinity between absolutely continuous distributions
• Confidence interval estimation of overlap: equal means case.
• A nonparametric measure of the overlapping coefficient.
• Directional log-spline distributions
• Statistical Analysis of Circular Data
• Circular Distributions Based on Nonnegative Trigonometric Sums
• Decision Rules, Based on the Distance, for Problems of Fit, Two Samples, and Estimation

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