Statistical Science

Models as Approximations—Rejoinder

Andreas Buja, Arun Kumar Kuchibhotla, Richard Berk, Edward George, Eric Tchetgen Tchetgen, and Linda Zhao

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Abstract

We respond to the discussants of our articles emphasizing the importance of inference under misspecification in the context of the reproducibility/replicability crisis. Along the way, we discuss the roles of diagnostics and model building in regression as well as connections between our well-specification framework and semiparametric theory.

Article information

Source
Statist. Sci., Volume 34, Number 4 (2019), 606-620.

Dates
First available in Project Euclid: 8 January 2020

Permanent link to this document
https://projecteuclid.org/euclid.ss/1578474027

Digital Object Identifier
doi:10.1214/19-STS762

Mathematical Reviews number (MathSciNet)
MR4048593

Keywords
Well-specification reproducibility/replicability proper scoring rules causal inference semiparametrics diagnostics

Citation

Buja, Andreas; Kuchibhotla, Arun Kumar; Berk, Richard; George, Edward; Tchetgen Tchetgen, Eric; Zhao, Linda. Models as Approximations—Rejoinder. Statist. Sci. 34 (2019), no. 4, 606--620. doi:10.1214/19-STS762. https://projecteuclid.org/euclid.ss/1578474027


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See also

  • Main article: Models as Approximations I: Consequences Illustrated with Linear Regression.
  • Main article: Models as Approximations II: A Model-Free Theory of Parametric Regression.