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- W3148907592 abstract "In recent times, the rise of `big data' has brought along major computational challenges in all the main disciplines of scientific research, including the field of spatial statistics. Some of these challenges include parametric estimation and quantification of estimation uncertainty that, when building statistical models using big data, pose an important computational load. Many methods have been proposed to address these challenges such as dimension reduction, approximation by Markov random fields, tapering of the covariance matrix, and subsampling based approaches. In this thesis a new textit{divide-and-conquer} approach is proposed that we call texttt{farmer} for providing effect size and standard error estimates in spatial models of big data. According to the proposed approach, all observations are divided into blocks that are mutually exclusive according to their position. For each block, the model parameters are estimated and recombined using a fixed or random meta-model to take into account the (possible) spatial dependence. This generalized method can be applied to a wide range of spatial models. For example, consider a linear Gaussian spatial model. In a simulation study, the texttt{farmer} estimators were compared with estimators based on methods with similar sampling ideas. In the context of the Gaussian model, two applications with real data are presented. The proposed method appears computationally efficient compared to equivalent methods and has lower bias in the estimates. Furthermore, the proposed approach provides a more realistic estimate of standard errors. Finally, we propose an application of the method to generalized linear spatial models for simulated and real counting data." @default.
- W3148907592 created "2021-04-13" @default.
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- W3148907592 date "2019-12-02" @default.
- W3148907592 modified "2023-09-24" @default.
- W3148907592 title "A divide and conquer approach for large spatial dataset" @default.
- W3148907592 hasPublicationYear "2019" @default.
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