Dependent Species Sampling Models for Spatial Density Estimation
نویسندگان
چکیده
منابع مشابه
On estimation of conditional density models with two-phase sampling
Suppose that the conditional density of a response variable given a vector of explanatory variables is parametrically modelled, and that data are collected by a two-phase sampling design. First, a simple random sample is drawn from the population. The stratum membership in a finite number of strata of the response and explanatory variables is recorded for each unit. Second, a subsample is drawn...
متن کاملSpatial Neutral to the Right Species Sampling Mixture Models
The field of Bayesian nonparametric statistics essentially involves the idea of assigning prior and posterior distributions over spaces of probability measures or more general measures. That is, similar to the classical parametric Bayesian idea of assigning priors to an unknown parameter say θ which lies in a Euclidean space, one views, for instance, an unknown cumulative distribution function ...
متن کاملWavelet Based Estimation of the Derivatives of a Density for m-Dependent Random Variables
Here, we propose a method of estimation of the derivatives of probability density based wavelets methods for a sequence of m−dependent random variables with a common one-dimensional probability density function and obtain an upper bound on Lp-losses for the such estimators.
متن کاملSpatial Models for Line Transect Sampling
This article develops methods for fitting spatial models to line transect data. These allow animal density to be related to topographical, environmental, habitat, and other spatial variables, helping wildlife managers to identify the factors that affect abundance. They also enable estimation of abundance for any subarea of interest within the surveyed region, and potentially yield estimates of ...
متن کاملA comparison of algorithms for maximum likelihood estimation of Spatial GLM models
In spatial generalized linear mixed models, spatial correlation is assumed by adding normal latent variables to the model. In these models because of the non-Gaussian spatial response and the presence of latent variables the likelihood function cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. The main purpose of this paper is to introduce two n...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bayesian Analysis
سال: 2017
ISSN: 1936-0975
DOI: 10.1214/16-ba1006