Mathematical models for non-parametric inferences from line transect data
Biometrics
By: K.P. Burnham and David R. Anderson
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Abstract
A general mathematical theory of line transects is developed which supplies a framework for nonparametric density estimation based on either right angle or sighting distances. The probability of observing a point given its right angle distance (y) from the line is generalized to an arbitrary function g(y). Given only that g(0) = 1, it is shown there are nonparametric approaches to density estimation using the observed right angle distances. The model is then generalized to include sighting distances (r). Let f(y I r) be the conditional distribution of right angle distance given sighting distance. It is shown that nonparametric estimation based only on sighting distances requires we know the transformation of r given by f(0 I r).
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Mathematical models for non-parametric inferences from line transect data |
| Series title | Biometrics |
| DOI | 10.2307/2529501 |
| Volume | 32 |
| Issue | 2 |
| Year Published | 1976 |
| Language | English |
| Publisher | International Biometric Society |
| Contributing office(s) | Patuxent Wildlife Research Center |
| Description | 12 p. |
| First page | 325 |
| Last page | 336 |