Measurement Error in Nonlinear Models: A Modern Perspective, 2nd edition by R. J. Carroll, D. Ruppert, L. A. Stefansky, and C. M. Crainiceanu

By S. Murphy, R. L. Smith, H. Tong. 2007

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Citations in the corpus

Causal Models for Dynamical Systems (2020, methodology): “While this might be a realistic assumption in many applications, the approach comes with various statistical difficulties, includ ing the famous errors-in-variables problem [Carroll et al. 2006] and an increased difficulty when identifying parameters or causal structure from data [Zhang et al.”
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)].”
Marginal and Nested Structural Models Using Instrumental Variables (2010, methodology): “Equivalently, the expansion of ϑ̂ can be derived by stacking the estimating equations for ψ and ϑ (see Carroll et al. 2006, section A.6.6).”
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 [17] could also be relevant if quantization is”
On Measurement Bias in Causal Inference (2010, methodology): “It is motivated by a result known to epidemi-ologists (Greenland and Kleinbaum, 1983; Greenland and Lash, 2008) and regression analysts (Carroll et al., 2006; Sel´en, 1986) that has not been fully utilized in causal analysis or graphical models.”
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.”
DOUBLY ROBUST INSTRUMENTAL VARIABLE REGRESSION (2012, methodology): “Special cases of this model have been extensively used in the conventional IV analysis and in related measurement-error problems (e.g., Carroll et al. (2006)).”
Inflation of Type I error rate in multiple regression when independent variables are measured with error (2009, methodology): “For multiple regression proper, earlier discussions are reviewed and clarified by Cochran (1968), who shows that when the independent variables are measured with error, ordinary least squares estimates of the regression coefficients can be inconsistent and biased, even asymptotically; also see McCal”
Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score (2018, background): “1) as an errors-in-variables regression problem [15], where we are regressing ̂j , whose expectation is j , on γ̂j , which can be regarded as a noisy observation of the actual regressor γj .”