Add to ORKGWe propose a Poisson-compound gamma approach for species richness estimation. Based on the denseness and nesting properties of the gamma mixture, we fix the shape parameter of each gamma component at a unified value, and estimate the mixture using nonparametric maximum likelihood. A least-squares crossvalidation procedure is proposed for the choice of the common shape parameter. The performance of the resulting estimator of N is assessed using numerical studies and genomic data. Some key words: Crossvalidation; Nesting property of gamma mixtures; Nonparametric maximum likelihood estima-tion; Poisson-compound gamma model; Species richness estimation. 1
A Bayesian non-parametric methodology has been recently proposed to deal with the issue of predictio...
Ecological data such as biomasses often present a high proportion of zeros with possible skewed posi...
In Bayesian nonparametric inference, random discrete probability measures are commonly used as prior...
We propose a Poisson-compound gamma approach for species richness estimation. Based on the denseness...
We propose a class of penalized nonparametric maximum likelihood estimators (NPMLEs) for the species...
In Bayesian nonparametric inference, random discrete probability measures are commonly used as prior...
Article first published online: 25 AUG 2015International audienceWe consider the estimation of the n...
summary:The compound Poisson-gamma variable is the sum of a random sample from a gamma distribution ...
Fits of species-abundance distributions to empirical data are increasingly used to evaluate models o...
Fits of species-abundance distributions to empirical data are increasingly used to evaluate models o...
We consider the estimation of the total number N of species based on the abundances of species that ...
We propose an alternative method for multi species abundance estimation decreases the bias in the es...
This thesis addresses species richness estimation for benthic data by describing the clustering of i...
Fig. 1 | Conceptual scheme illustrating the Poisson sampling of a community with species abundances ...
Ecological data such as biomasses often present a high proportion of zeros with possible skewed posi...
A Bayesian non-parametric methodology has been recently proposed to deal with the issue of predictio...
Ecological data such as biomasses often present a high proportion of zeros with possible skewed posi...
In Bayesian nonparametric inference, random discrete probability measures are commonly used as prior...
We propose a Poisson-compound gamma approach for species richness estimation. Based on the denseness...
We propose a class of penalized nonparametric maximum likelihood estimators (NPMLEs) for the species...
In Bayesian nonparametric inference, random discrete probability measures are commonly used as prior...
Article first published online: 25 AUG 2015International audienceWe consider the estimation of the n...
summary:The compound Poisson-gamma variable is the sum of a random sample from a gamma distribution ...
Fits of species-abundance distributions to empirical data are increasingly used to evaluate models o...
Fits of species-abundance distributions to empirical data are increasingly used to evaluate models o...
We consider the estimation of the total number N of species based on the abundances of species that ...
We propose an alternative method for multi species abundance estimation decreases the bias in the es...
This thesis addresses species richness estimation for benthic data by describing the clustering of i...
Fig. 1 | Conceptual scheme illustrating the Poisson sampling of a community with species abundances ...
Ecological data such as biomasses often present a high proportion of zeros with possible skewed posi...
A Bayesian non-parametric methodology has been recently proposed to deal with the issue of predictio...
Ecological data such as biomasses often present a high proportion of zeros with possible skewed posi...
In Bayesian nonparametric inference, random discrete probability measures are commonly used as prior...