Measurement error in nonlinear models: a modern perspective

By Raymond J. Carroll. 2006

2,445 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Computer Networks and Communications, Control and Systems Engineering

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Citations in the corpus

Causal Models for Dynamical Systems (2020, background): “surement noise, for example. While this might be a realistic assumption in many applications, the approach comes with various statistical diculties, including the famous errors-in-variables problem [Carroll et al., 2006] and an increased diculty when identifying parameters or causal structure from d”
The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning (2016, background): “Also, the classic errors-in-variable model Hall (2008) could also be relevant if quantization is treated as a source of “error.””
An alternative view of the deconvolution problem (2008, background): “See also Carroll et al. (2006) and Delaigle, Hall and Qiu (2006).”
Inflation of Type I error rate in multiple regression when independent variables are measured with error (2009, background): “This accounts for passing references — for example by Fuller (1978, p. 55), Cochran (1968, p. 653) Carroll et al. (2006, pp. 52-53) — to incorrect Type I error rates and incorrect conclusions when measurement error is ignored.”
On Measurement Bias in Causal Inference (2010, methodology): “It is motivated by a result known to epidemiologists (Greenland and Kleinbaum, 1983; Greenland and Lash, 2008) and regression analysts (Carroll et al., 2006; Selén, 1986) that has not been fully utilized in causal analysis or graphical models.”
MULTILEVEL FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS (2009, result): “The comparison is analogous to that between the regression calibration estimates and the naive estimates in measurement error models [Carroll et al. (2006)].”
Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score (2018, methodology): “To proceed further we introduce a well-known probit approximation of logistic function [16]: H(t) ≈ Φ(t/1.”
Measurement bias and effect restoration in causal inference (2014, methodology): “This is indeed the approach taken in the vast literature on measurement error (e.g., Sel en, 1986; Carroll et al., 2006).”
Density estimation with heteroscedastic error (2008, background): “The fields of application are various and include astronomy, biology, chemistry, economy and public health; see, for example, Merritt (1997) or the numerous examples described in Carroll et al. (2006).”
Locally Efficient Estimators for Semiparametric Models With Measurement Error (2006, methodology): “Although earlier analysis (Carroll et al. 2006) has assumed a linear model for the age effect, our analysis will show that the linearity assumption is somewhat violated, and hence the inclusion of an arbitrary function θ(Z) is needed.”