Parametric and semiparametric model estimation and selection in geostatistics
This dissertation is focused on geostatistical models, which are useful in many scientific disciplines, such as climatology, ecology and environmental monitoring. In the first part, we consider variable selection in spatial linear models with Gaussian process errors. Penalized maximum likelihood estimation (PMLE) that enables simultaneous variable selection and parameter estimation is developed and for ease of computation, PMLE is approximated by one-step sparse estimation (OSE). To further improve computational efficiency particularly with large sample sizes, we propose penalized maximum ...
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