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|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
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.|
|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|
Files in This Item:
|Tabasso_Myriam_Phd_thesis.pdf||2.01 MB||Adobe PDF|
File del Curriculum Vitae:
|CurriculumVitae.pdf|| ||100.47 kB||Adobe PDF|
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