A comparison of the alr and ilr transformations for kernel density estimation of compositional data

In a seminal paper, Aitchison and Lauder (1985) introduced classical kernel density estimation techniques in the context of compositional data analysis. Indeed, they gave two options for the choice of the kernel to be used in the kernel estimator. One of these kernels is based on the use the alr transformation on the simplex SD jointly with the normal distribution on RD-1. However, these authors themselves recognized that this method has some deficiencies. A method for overcoming these dificulties based on recent developments for compositional data analysis and multivariate kernel estimation theory, combining the ilr transformation with the use of the normal density with a full bandwidth matrix, was recently proposed in Martín-Fernández, Chacón and Mateu- Figueras (2006). Here we present an extensive simulation study that compares both methods in practice, thus exploring the finite-sample behaviour of both estimators

Projecte de ruta, patrimoni cultural i natural del municipi de Llançà: "Llançà. Un Passeig pel temps descobrint el Patrimoni"

En aquest Projecte el que es planteja és abraçar el màxim d’elements patrimonials del municipi i crear una ruta, per conèixer, estudiar i descobrir quins són els elements més característics, emblemàtics, i més rellevants per després poder incloure’ls en la nostra proposta. En aquesta ruta, acabaran entrant doncs, tant el patrimoni inventariable, com el patrimoni intangible, sempre i quan tinguin una singularitat que la facin atractiva i interessant per al visitant. El que es vol és que aquest projecte sigui una alternativa real a les diferents rutes que es presenten al municipi de Llançà

A deterministic model for lake clarity; application to management of Lake Tahoe (California-Nevada), USA

This dissertation has as its goal the quantitative evaluation of the application of coupled hydrodynamic, ecological and clarity models, to address the deterministic prediction of water clarity in lakes and reservoirs. Prediction of water clarity is somewhat unique, insofar as it represents the integrated and coupled effects of a broad range of individual water quality components. These include the biological components such as phytoplankton, together with the associated cycles of nutrients that are needed to sustain their popuiations, and abiotic components such as suspended particles that may be introduced by streams, atmospheric deposition or sediment resuspension. Changes in clarity induced by either component will feed back on the phytoplankton dynamics, as incident light also affects biological growth. Thus ability to successfully model changes in clarity will by necessity have to achieve the correct modeling of these other water quality parameters. Water clarity is also unique in that it may be one of the earliest and most easily detected wamings of the acceleration of the process of eutrophication in a water body.