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Resampling approach for estimating p...

Li, Wei.

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Resampling approach for estimating prediction error and for adjusting logistic regression models for covariate measurement error.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Resampling approach for estimating prediction error and for adjusting logistic regression models for covariate measurement error./
Author:	Li, Wei.
Description:	78 p.
Notes:	Advisers: Sati Mazumdar; Vincent C. Arena.
Contained By:	Dissertation Abstracts International63-05B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3054302
ISBN:	0493695850

Resampling approach for estimating prediction error and for adjusting logistic regression models for covariate measurement error.
Li, Wei.

Resampling approach for estimating prediction error and for adjusting logistic regression models for covariate measurement error. - 78 p.

Advisers: Sati Mazumdar; Vincent C. Arena.

Thesis (Ph.D.)--University of Pittsburgh, 2002.

Methods based on the resampling approach are proposed to address two issues related to prediction modeling: estimation of prediction error and adjustment for covariance measurement error.

ISBN: 0493695850Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Resampling approach for estimating prediction error and for adjusting logistic regression models for covariate measurement error.
LDR:02778nam 2200313 a 45 001 929156
005 20110427
008 110427s2002 eng d
020 $a 0493695850
035 $a (UnM)AAI3054302
035 $a AAI3054302
040 $a UnM $c UnM
100 1 $a Li, Wei. $3 884990
245 1 0 $a Resampling approach for estimating prediction error and for adjusting logistic regression models for covariate measurement error.
300 $a 78 p.
500 $a Advisers: Sati Mazumdar; Vincent C. Arena.
500 $a Source: Dissertation Abstracts International, Volume: 63-05, Section: B, page: 2146.
502 $a Thesis (Ph.D.)--University of Pittsburgh, 2002.
520 $a Methods based on the resampling approach are proposed to address two issues related to prediction modeling: estimation of prediction error and adjustment for covariance measurement error.
520 $a <italic>Repartitioning k-fold cross-validation</italic> (CV<italic>K </italic>R) is proposed to enhance the estimation of the prediction error of classification models. Compared to cross-validation, the traditional method of choice, CV<italic>K</italic>R reduces the variability of estimated prediction error. In addition, it provides an empirical distribution of prediction error rather than a single estimate unaccompanied by an estimate of standard error for the point estimate. SAS macros are developed for the implementation of CV<italic>K</italic>R.
520 $a <italic>Bootstrap regression calibration</italic> (<italic>BRC</italic>) is proposed to adjust the coefficient estimates of logistic regression models when measurement error is present in model covariates. This method can be thought of as a bootstrap-smoothed version of the popular regression calibration method (Rosner et al. <italic>AM. J. Epidemiol.</italic> 1990, 1992). These two methods are evaluated and compared with respect to <italic>prediction accuracy</italic>, something not found in previous works. Receiver Operating Characteristic (ROC) methodology was employed to measure models' prediction accuracy and the area under ROC curve (AUC) was used as the index for prediction accuracy. The methods were also evaluated with respect to the attenuation (or bias) in the estimated coefficients. Results from simulation studies showed that BRC offers consistent enhancement over the regression calibration method in terms of improving the prediction accuracy and reducing bias in estimated coefficients.
590 $a School code: 0178.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Statistics. $3 517247
690 $a 0308
690 $a 0463
710 2 0 $a University of Pittsburgh. $3 958527
773 0 $t Dissertation Abstracts International $g 63-05B.
790 $a 0178
790 1 0 $a Arena, Vincent C., $e advisor
790 1 0 $a Mazumdar, Sati, $e advisor
791 $a Ph.D.
792 $a 2002
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3054302