Discussion on the Paper From Start to Finish: a Framework for the Production of small area official statistics” by Tzavidis et al.,

Research output: Contribution to journalComment/debateResearch

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Abstract

I strongly welcome this paper which sets out a framework to produce small area official statistics. Tzavidis and his colleagues have proposed an iterative process on three broadly defined stages which are specification, analysis and adaptation, and evaluation. Keeping in view the fast developments in the area of machine learning I strongly suggest using these tools at the specification stage. Whereas machine learning tools have been successfully used in many areas I am a little surprised that the application of these tools in small area estimation is still at its infancy. Machine learning methods like classification and regression trees, random forests and stochastic gradient boosting (‘TreeNet’) have several advantages over ordinary least squares such as that they can handle outliers, missing values and model non‐linear relationships and local effects which are common problems in small area estimation. These methods are quite efficient in selecting variables and modelling variable interactions. They are also particularly useful for unbalanced data sets. They can be used to select optimal predictors of the target population under the model and can yield parsimonious models.
Original languageEnglish
Pages (from-to)966
Number of pages1
JournalJournal of the Royal Statistical Society. Series A: Statistics in Society
Volume181
Issue number4
DOIs
Publication statusPublished - Sep 2018

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Official Statistics
official statistics
Small Area Estimation
Machine Learning
Specification
Classification and Regression Trees
Unbalanced Data
Stochastic Gradient
Ordinary Least Squares
Random Forest
Missing Values
Boosting
Iterative Process
Outlier
Predictors
Model
learning method
Target
learning
Evaluation

Cite this

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title = "Discussion on the Paper From Start to Finish: a Framework for the Production of small area official statistics” by Tzavidis et al.,",
abstract = "I strongly welcome this paper which sets out a framework to produce small area official statistics. Tzavidis and his colleagues have proposed an iterative process on three broadly defined stages which are specification, analysis and adaptation, and evaluation. Keeping in view the fast developments in the area of machine learning I strongly suggest using these tools at the specification stage. Whereas machine learning tools have been successfully used in many areas I am a little surprised that the application of these tools in small area estimation is still at its infancy. Machine learning methods like classification and regression trees, random forests and stochastic gradient boosting (‘TreeNet’) have several advantages over ordinary least squares such as that they can handle outliers, missing values and model non‐linear relationships and local effects which are common problems in small area estimation. These methods are quite efficient in selecting variables and modelling variable interactions. They are also particularly useful for unbalanced data sets. They can be used to select optimal predictors of the target population under the model and can yield parsimonious models.",
author = "Kuldeep Kumar",
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AB - I strongly welcome this paper which sets out a framework to produce small area official statistics. Tzavidis and his colleagues have proposed an iterative process on three broadly defined stages which are specification, analysis and adaptation, and evaluation. Keeping in view the fast developments in the area of machine learning I strongly suggest using these tools at the specification stage. Whereas machine learning tools have been successfully used in many areas I am a little surprised that the application of these tools in small area estimation is still at its infancy. Machine learning methods like classification and regression trees, random forests and stochastic gradient boosting (‘TreeNet’) have several advantages over ordinary least squares such as that they can handle outliers, missing values and model non‐linear relationships and local effects which are common problems in small area estimation. These methods are quite efficient in selecting variables and modelling variable interactions. They are also particularly useful for unbalanced data sets. They can be used to select optimal predictors of the target population under the model and can yield parsimonious models.

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JO - Journal of the Royal Statistical Society. Series A: Statistics in Society

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