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Please use this identifier to cite or link to this item: http://hdl.handle.net/10805/2424

Title: SPATIAL REGRESSION IN LARGE DATASETS: PROBLEM SET SOLUTION
Authors: TABASSO, MYRIAM
Tutor: ARBIA, GIUSEPPE
Keywords: spatial econometric models, large datasets, spatial data mining,sparsity effect, sample size effect, computational complexity, CART
Issue Date: 4-Apr-2014
Abstract: In this dissertation we investigate a possible attempt to combine the Data Mining methods and traditional Spatial Autoregressive models, in the context of large spatial datasets. We start to considere the numerical difficulties to handle massive datasets by the usual approach based on Maximum Likelihood estimation for spatial models and Spatial Two-Stage Least Squares. So, we conduct an experiment by Monte Carlo simulations to compare the accuracy and computational complexity for decomposition and approximation techniques to solve the problem of computing the Jacobian in spatial models, for various regular lattice structures. In particular, we consider one of the most common spatial econometric models: spatial lag (or SAR, spatial autoregressive model). Also, we provide new evidences in the literature, by examining the double effect on computational complexity of these methods: the influence of "size effect" and "sparsity effect". To overcome this computational problem, we propose a data mining methodology as CART (Classification and Regression Tree) that explicitly considers the phenomenon of spatial autocorrelation on pseudo-residuals, in order to remove this effect and to improve the accuracy, with significant saving in computational complexity in wide range of spatial datasets: realand simulated data.
URI: http://hdl.handle.net/10805/2424
Research interests: spatial econometrics on large datasets, spatial data mining, spatial statistics
Personal skills keywords: analysis of spatial data
spatial econometric models
data mining analysis
Appears in PhD:STATISTICA ECONOMICA

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