Abstract
Summary We propose an estimation method that incorporates the correlation/covariance structure between repeated measurements in covariate-adjusted regression models for distorted longitudinal data. In this distorted data setting, neither the longitudinal response nor (possibly time-varying) predictors are directly observable. The unobserved response and predictors are assumed to be distorted/contaminated by unknown functions of a common observable confounder. The proposed estimation methodology adjusts for the distortion effects both in estimation of the covariance structure and in the regression parameters using generalized least squares. The finite-sample performance of the proposed estimators is studied numerically by means of simulations. The consistency and convergence rates of the proposed estimators are also established. The proposed method is illustrated with an application to data from a longitudinal study of cognitive and social development in children.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 319-333 |
| Number of pages | 15 |
| Journal | Australian and New Zealand Journal of Statistics |
| Volume | 51 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 2009 |
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Keywords
- Binning
- Clustered data
- Covariance structure
- General linear model
- Generalized least squares
- Multiplicative effect
- Varying-coefficient models
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
Cite this
Covariate-adjusted regression for longitudinal data incorporating correlation between repeated measurements. / Nguyen, Danh V.; Şentürk, Damla.
In: Australian and New Zealand Journal of Statistics, Vol. 51, No. 3, 09.2009, p. 319-333.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Covariate-adjusted regression for longitudinal data incorporating correlation between repeated measurements
AU - Nguyen, Danh V.
AU - Şentürk, Damla
PY - 2009/9
Y1 - 2009/9
N2 - Summary We propose an estimation method that incorporates the correlation/covariance structure between repeated measurements in covariate-adjusted regression models for distorted longitudinal data. In this distorted data setting, neither the longitudinal response nor (possibly time-varying) predictors are directly observable. The unobserved response and predictors are assumed to be distorted/contaminated by unknown functions of a common observable confounder. The proposed estimation methodology adjusts for the distortion effects both in estimation of the covariance structure and in the regression parameters using generalized least squares. The finite-sample performance of the proposed estimators is studied numerically by means of simulations. The consistency and convergence rates of the proposed estimators are also established. The proposed method is illustrated with an application to data from a longitudinal study of cognitive and social development in children.
AB - Summary We propose an estimation method that incorporates the correlation/covariance structure between repeated measurements in covariate-adjusted regression models for distorted longitudinal data. In this distorted data setting, neither the longitudinal response nor (possibly time-varying) predictors are directly observable. The unobserved response and predictors are assumed to be distorted/contaminated by unknown functions of a common observable confounder. The proposed estimation methodology adjusts for the distortion effects both in estimation of the covariance structure and in the regression parameters using generalized least squares. The finite-sample performance of the proposed estimators is studied numerically by means of simulations. The consistency and convergence rates of the proposed estimators are also established. The proposed method is illustrated with an application to data from a longitudinal study of cognitive and social development in children.
KW - Binning
KW - Clustered data
KW - Covariance structure
KW - General linear model
KW - Generalized least squares
KW - Multiplicative effect
KW - Varying-coefficient models
UR - http://www.scopus.com/inward/record.url?scp=63549129382&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=63549129382&partnerID=8YFLogxK
U2 - 10.1111/j.1467-842X.2009.00547.x
DO - 10.1111/j.1467-842X.2009.00547.x
M3 - Article
AN - SCOPUS:63549129382
VL - 51
SP - 319
EP - 333
JO - Australian and New Zealand Journal of Statistics
JF - Australian and New Zealand Journal of Statistics
SN - 1369-1473
IS - 3
ER -