Goodness-of-fit testing of error distribution in linear measurement error models

Koul, Hira L.; Song, Weixing; Zhu, Xiaoqing

doi:10.1214/17-aos1627

Cited by 4 publications

(2 citation statements)

References 26 publications

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“…Thus care should be taken to ensure distributional assumptions are reasonable. See also [ 42 ], who discuss goodness-of-fit testing of the error distribution in linear measurement error models.…”

Section: Resultsmentioning

confidence: 99%

A general algorithm for error-in-variables regression modelling using Monte Carlo expectation maximization

2023

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In regression modelling, measurement error models are often needed to correct for uncertainty arising from measurements of covariates/predictor variables. The literature on measurement error (or errors-in-variables) modelling is plentiful, however, general algorithms and software for maximum likelihood estimation of models with measurement error are not as readily available, in a form that they can be used by applied researchers without relatively advanced statistical expertise. In this study, we develop a novel algorithm for measurement error modelling, which could in principle take any regression model fitted by maximum likelihood, or penalised likelihood, and extend it to account for uncertainty in covariates. This is achieved by exploiting an interesting property of the Monte Carlo Expectation-Maximization (MCEM) algorithm, namely that it can be expressed as an iteratively reweighted maximisation of complete data likelihoods (formed by imputing the missing values). Thus we can take any regression model for which we have an algorithm for (penalised) likelihood estimation when covariates are error-free, nest it within our proposed iteratively reweighted MCEM algorithm, and thus account for uncertainty in covariates. The approach is demonstrated on examples involving generalized linear models, point process models, generalized additive models and capture–recapture models. Because the proposed method uses maximum (penalised) likelihood, it inherits advantageous optimality and inferential properties, as illustrated by simulation. We also study the model robustness of some violations in predictor distributional assumptions. Software is provided as the refitME package on R, whose key function behaves like a refit() function, taking a fitted regression model object and re-fitting with a pre-specified amount of measurement error.

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Section: Resultsmentioning

confidence: 99%

A general algorithm for error-in-variables regression modelling using Monte Carlo expectation maximization

2023

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“…Exemplarily, we mention the work of Marzec and Marzec (1997), Stute et al (1998), Koul (2004, 2009), Haywood and Khmaladze (2008), Dette and Hetzler (2009), Koul and Song (2010), Müller et al (2012), and Can et al (2015), who use the Khmaladze transform to construct goodness-fit-tests for various problems. The work which is most similar in spirit to our work is the paper of Koul et al (2018), who consider a similar problem in linear measurement error models.…”

Section: Introductionmentioning

confidence: 90%