<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:contributor>David R. Anderson</dc:contributor>
  <dc:creator>K.P. Burnham</dc:creator>
  <dc:date>1976</dc:date>
  <dc:description>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).</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>10.2307/2529501</dc:identifier>
  <dc:language>en</dc:language>
  <dc:publisher>International Biometric Society</dc:publisher>
  <dc:title>Mathematical models for non-parametric inferences from line transect data</dc:title>
  <dc:type>article</dc:type>
</oai_dc:dc>