Statistical Science

Models as Approximations II: A Model-Free Theory of Parametric Regression

Andreas Buja, Lawrence Brown, Arun Kumar Kuchibhotla, Richard Berk, Edward George, and Linda Zhao

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Abstract

We develop a model-free theory of general types of parametric regression for i.i.d. observations. The theory replaces the parameters of parametric models with statistical functionals, to be called “regression functionals,” defined on large nonparametric classes of joint ${x\textrm{-}y}$ distributions, without assuming a correct model. Parametric models are reduced to heuristics to suggest plausible objective functions. An example of a regression functional is the vector of slopes of linear equations fitted by OLS to largely arbitrary ${x\textrm{-}y}$ distributions, without assuming a linear model (see Part I). More generally, regression functionals can be defined by minimizing objective functions, solving estimating equations, or with ad hoc constructions. In this framework, it is possible to achieve the following: (1) define a notion of “well-specification” for regression functionals that replaces the notion of correct specification of models, (2) propose a well-specification diagnostic for regression functionals based on reweighting distributions and data, (3) decompose sampling variability of regression functionals into two sources, one due to the conditional response distribution and another due to the regressor distribution interacting with misspecification, both of order $N^{-1/2}$, (4) exhibit plug-in/sandwich estimators of standard error as limit cases of ${x\textrm{-}y}$ bootstrap estimators, and (5) provide theoretical heuristics to indicate that ${x\textrm{-}y}$ bootstrap standard errors may generally be preferred over sandwich estimators.

Article information

Source
Statist. Sci., Volume 34, Number 4 (2019), 545-565.

Dates
First available in Project Euclid: 8 January 2020

Permanent link to this document
https://projecteuclid.org/euclid.ss/1578474017

Digital Object Identifier
doi:10.1214/18-STS694

Mathematical Reviews number (MathSciNet)
MR4048583

Keywords
Ancillarity of regressors misspecification econometrics sandwich estimator bootstrap bagging

Citation

Buja, Andreas; Brown, Lawrence; Kuchibhotla, Arun Kumar; Berk, Richard; George, Edward; Zhao, Linda. Models as Approximations II: A Model-Free Theory of Parametric Regression. Statist. Sci. 34 (2019), no. 4, 545--565. doi:10.1214/18-STS694. https://projecteuclid.org/euclid.ss/1578474017


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See also

Supplemental materials

  • Supplement to “Models as Approximations II: A Model-Free Theory of Parametric Regression”. This supplement contains Appendices A-G.