Orador Convidado:

Prof.  Russell Alpizar-Jara, Departamento de Matemática e CIMA, Escola de Ciências e Tecnologia, Universidade de Évora

Resumo:

Population density estimation in distance sampling requires fitting a probability density function denoted by f (y|θ), where y represents the perpendicular(ou radial) distance from a detected animal (or object) to a transect line (or point), and θ represents the vector parameter indexing this family of probability density functions. The most popular approach to estimate f (·), is based on a semi–parametric methodology proposed by [1]. The main idea is to find the maximum likelihood estimator for θ using a parametric functional form combined with a series expansion. After a general introduction of this methodology, we present some developments conducted at University of Évora ([2] to [6]), usually motivated by interdisciplinary collaboration, and highlight some caveats and challenges for future work.

Resumo (pdf):

Em anexo.