Dr. Eng. H. Omrani and Dr. P. Gerber

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Presentation transcript:

Small Area Estimation with application to Urban Audit project estimates Dr. Eng. H. Omrani and Dr. P. Gerber GEODE department, CEPS/INSTEAD, LU EUROSTAT, 27-28 sept., 2010

Outline 1. Introduction 2. Proposition 3. Conclusions and perspectives Objectives and problems Overview: Small Area Estimation 2. Proposition Theoritical aspects Application, results and assessment 3. Conclusions and perspectives

1. Introduction

Small Area Estimation Methods: Horvitz Thomson estimator, GREG, EBLUP, S- EBLUP, LOGIT and NN

How do we get the data?

The variables to be estimated: Total Number of Households (excluding institutional households) One person households Households with children aged 0 to under 18 Total Number of Households with less than half of the national average disposable annual household income

Outline 1. Introduction 2. Proposition 3. Conclusions and perspectives Objectives and problems Overview: Small Area Estimation 2. Proposition Theoritical aspects Application and results Assessment 3. Conclusions and perspectives

2. Proposition LOGIT NEURAL NETWORK Sensitivity analysis: ROC curve analysis Monte Carlo Simulation

Application: Number of households with level estimation (city level) The data used is from: the census file (2001) administrative data sets (2003-2009) The variables used: Fig: Multi-layer perceptron network for prediction of number of households

Results from Logit and Neural Network models

Results…

3. Conclusions Neural Network (NN) is particularly useful and efficient when complex nonlinear phenomena. NN technique seems particularly adapted to estimate sublevel indicators better than Logit model. The results of the proposed methods are assessed by ROC curve analysis They have been represented by confidence interval with Monte Carlo simulation. In the future, we plan to apply the developed technique to estimate the set of indicators at sublevel scale of the UA project. In a next step of our research, we may try to : include the spatial units (e.g. municipality level) into the NN model predict the revenues and the structure of households (number of children) compare the results with Poisson regression model which is widely used to predict such variables.

Thanks for your attention QUESTIONS !