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Estimation and prediction for spatial generalized linear mixed models with parametric links via reparameterized importance sampling
Spatial generalized linear mixed models (SGLMMs) are popular for analyzing non-Gaussian spatial data. These models assume a prescribed link function that relates the underlying spatial field with the mean response. There are...
Geostatistics Laplace approximation Markov Chain Monte Carlo model selection Multiple importance sampling Reverse logistic regression /dk/atira/pure/subjectarea/asjc/2600/2613 name=Statistics and Probability /dk/atira/pure/subjectarea/asjc/1900/1903 name=Computers in Earth Sciences /dk/atira/pure/subjectarea/asjc/2300/2308 name=Management, Monitoring, Policy and Law
Estimation and prediction for spatial generalized linear mixed models with parametric links via reparameterized importance sampling
Spatial generalized linear mixed models (SGLMMs) are popular for analyzing non-Gaussian spatial data. These models assume a prescribed link function that relates the underlying spatial field with the mean response. There are...
Geostatistics Laplace approximation Markov Chain Monte Carlo model selection Multiple importance sampling Reverse logistic regression /dk/atira/pure/subjectarea/asjc/2600/2613 name=Statistics and Probability /dk/atira/pure/subjectarea/asjc/1900/1903 name=Computers in Earth Sciences /dk/atira/pure/subjectarea/asjc/2300/2308 name=Management, Monitoring, Policy and Law
Selection of proposal distributions for multiple importance sampling
In general, the naive importance sampling (IS) estimator does not work well in examples involving simultaneous inference on several targets, because the importance weights can take arbitrarily large values, making the estimator...
Selection of proposal distributions for multiple importance sampling
In general, the naive importance sampling (IS) estimator does not work well in examples involving simultaneous inference on several targets, because the importance weights can take arbitrarily large values, making the estimator...