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Improved multilevel regression with post-stratification through machine Learning (autoMrP)


Broniecki, Philipp; Leemann, Lucas; Wüest, Reto (2022). Improved multilevel regression with post-stratification through machine Learning (autoMrP). The Journal of Politics, 84(1):597-601.

Abstract

Multilevel regression with post-stratification (MrP) has quickly become the gold standard for small area estimation. While the first MrP models did not include contextlevel information, current applications almost always make use of such data. When using MrP, researchers are faced with three problems: how to select features, how to specify the functional form, and how to regularize the model parameters. These problems are especially important with regard to features included at the context level. We propose a systematic approach to estimating MrP models that addresses these issues by employing a number of machine learning techniques. We illustrate our approach based on 89 items from public opinion surveys in the US and demonstrate that our approach outperforms a standard MrP model, in which the choice of contextlevel variables has been informed by a rich tradition of public opinion research.

Abstract

Multilevel regression with post-stratification (MrP) has quickly become the gold standard for small area estimation. While the first MrP models did not include contextlevel information, current applications almost always make use of such data. When using MrP, researchers are faced with three problems: how to select features, how to specify the functional form, and how to regularize the model parameters. These problems are especially important with regard to features included at the context level. We propose a systematic approach to estimating MrP models that addresses these issues by employing a number of machine learning techniques. We illustrate our approach based on 89 items from public opinion surveys in the US and demonstrate that our approach outperforms a standard MrP model, in which the choice of contextlevel variables has been informed by a rich tradition of public opinion research.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Political Science
Dewey Decimal Classification:320 Political science
Language:English
Date:1 January 2022
Deposited On:03 May 2021 08:46
Last Modified:26 Mar 2024 02:35
Publisher:University of Chicago Press
ISSN:0022-3816
OA Status:Green
Publisher DOI:https://doi.org/10.1086/714777
  • Content: Accepted Version
  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial 1.0 Generic (CC BY-NC 1.0)