کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1145916 | 1489686 | 2013 | 13 صفحه PDF | دانلود رایگان |
In this paper, we introduce a unified skew Gaussian-log Gaussian model and propose a general class of spatial sampling models that can account for both heavy tails and skewness. This class includes some models proposed previously in the literature. The likelihood function involves analytically intractable integrals and direct maximization of the marginal likelihood is numerically difficult. We obtain maximum likelihood estimates of the model parameters, using a stochastic approximation of the EM algorithm (SAEM). The predictive distribution at unsampled sites is approximated based on Markov chain Monte Carlo samples. The identifiability of the parameters and the performance of the proposed model is investigated by a simulation study. The usefulness of our methodology is demonstrated by analyzing a Pb data set in a region of north Iran.
Journal: Journal of Multivariate Analysis - Volume 114, February 2013, Pages 16–28