Measurement Error: Models, Methods, and Applications

By John P. Buonaccorsi. 2010

521 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/443eba03ef7b1a1857ab70bcccc97c7303967e17

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

Citations in the corpus

Bayesian analysis of measurement error models using integrated nested Laplace approximations (2015, methodology): “This has been discussed in linear regression (Fuller, 1987; Buonaccorsi, 2010), and Bayesian inference has been proposed (Kelly, 2007; Andreson and Hurn, 2013; deCastro et al., 2013).”
Meta‐analysis of variation: ecological and evolutionary applications and beyond (2015, methodology): “Alternatively, we could accommodate these sources of variation in the model by including measurement error, often called measurement error models (i.e. explicitly modelling sample variance in predictors; see Buonaccorsi 2010).”
Foundations of Linear and Generalized Linear Models (2015, background): “Random x is also relevant in the study of errors of measurement of explanatory variables (Buonaccorsi 2010).”
A researcher's guide to regression, discretization, and median splits of continuous variables (2015, background): “Thus, we suggest that researchers consider errors-in-variables regression models (Buonaccorsi, 2010) that account for this.”
Differential analysis of RNA-seq incorporating quantification uncertainty (2016, background): “Response error modeling 26 allows for the separate modeling of biological variance and inferential variance.”
Information Retrieval for E-Discovery (2013, background): “An extensive overview of the statistical treatment of measurement error is contained in Buonaccorsi [30].”
Experimenting with Measurement Error: Techniques with Applications to the Caltech Cohort Study (2019, background): “It is well known that measurement error attenuates estimated coefﬁcients (see, e.g., Buonaccorsi 2010).”
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates (2020, background): “Ideas from the large literature on measurement error models may also be helpful (Fuller, 1987; Carroll et al., 2006; Buonaccorsi, 2010).”
A Bayesian Approach for Estimating Multilevel Latent Contextual Models (2016, background): “…measured without error because unreliability in the dependent variable does not result in biased estimates of unstandardized regression coefficients (e.g., Buonaccorsi, 2010). and at L2 ðλBk ¼ λBk0 Þ, and the measurement error variances are also equal at L1, VarðεWkijÞ ¼ VarðεWk0ijÞ, and at L2,…”