estim_eq_corrected

Constructs the moment-corrected estimating equations for a linear regression
model with averaged error-prone covariates and error-free covariates.

internal

Estimation and inference for coefficients of linear EIV models with symmetric measurement errors.
The measurement errors can be homoscedastic or heteroscedastic, for the latter, replication for at least some observations needs to be available. The estimation method and asymptotic inference are
based on a generalised method of moments framework, where the estimating equations are formed from (1) minimising the distance between the empirical phase function (normalised characteristic function) of the response and that of the linear combination of all the covariates at the estimates,
and (2) minimising a corrected least-square discrepancy function. Specifically, for a linear EIV model with p error-prone and q error-free covariates, if replicates are available, the GMM approach is based on a 2(p+q) estimating equations if some replicates are available and based on p+2q estimating equations if no replicate is available.
The details of the method are described in Nghiem and Potgieter (2020) <doi:10.1093/biomet/asaa025> and Nghiem and Potgieter (2025) <doi:10.5705/ss.202022.0331>.

Chang Liu

PhaseGMM

Phase-Function Based Estimation and Inference for Linear
Errors-in-Variables (EIV) Models

Linh Nghiem

estim_eq_corrected function

<dl><dt>y</dt>
<dd>Numeric vector of length <code>n</code> containing the response.</dd>
<dt>Wbar</dt>
<dd>Numeric matrix of dimension <code>n x p</code> containing averaged
error-prone covariates.</dd>
<dt>Zmat</dt>
<dd>Numeric matrix of dimension <code>n x q</code> containing error-free
covariates (including an intercept if required).</dd>
<dt>b_in</dt>
<dd>Numeric vector of length <code>p + q</code> containing the regression
coefficients at which the estimating equations are evaluated.</dd>
<dt>estSigmaU_bar</dt>
<dd>Numeric array of dimension <code>n x p x p</code> (or numeric
vector in the univariate case) containing measurement error covariance
estimates.</dd></dl>

Arguments

Moment-corrected estimating equations for measurement error regression — estim_eq_corrected

<dl>

<dt>y</dt>
<dd>Numeric vector of length <code>n</code> containing the response.</dd>


<dt>Wbar</dt>
<dd>Numeric matrix of dimension <code>n x p</code> containing averaged
error-prone covariates.</dd>


<dt>Zmat</dt>
<dd>Numeric matrix of dimension <code>n x q</code> containing error-free
covariates (including an intercept if required).</dd>


<dt>b_in</dt>
<dd>Numeric vector of length <code>p + q</code> containing the regression
coefficients at which the estimating equations are evaluated.</dd>


<dt>estSigmaU_bar</dt>
<dd>Numeric array of dimension <code>n x p x p</code> (or numeric
vector in the univariate case) containing measurement error covariance
estimates.</dd>

</dl>

estim_eq_corrected: Moment-corrected estimating equations for measurement error regression

Description

Usage

Value

Arguments