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Accounting for Measurement Error in Covariates in the Context of ANCOVA Using Maximum Likelihood Estimation

Citation

Wilson, Ana. (2022-05). Accounting for Measurement Error in Covariates in the Context of ANCOVA Using Maximum Likelihood Estimation. Theses and Dissertations Collection, University of Idaho Library Digital Collections. https://www.lib.uidaho.edu/digital/etd/items/wilson_idaho_0089n_12367.html

Title:
Accounting for Measurement Error in Covariates in the Context of ANCOVA Using Maximum Likelihood Estimation
Author:
Wilson, Ana
Date:
2022-05
Program:
Mathematics & Statistical Sci
Subject Category:
Statistics
Abstract:

Analysis of covariance (ANCOVA) is a common statistical model. An implicit assumption of ANCOVA is that the covariate is measured without error. However, in many applications, there is covariate measurement error. In this case, the estimates produced by classic ANCOVA methods can include bias, causing predictions and inferences to be inaccurate. This thesis uses monte carlo simulation to examine the effectiveness of an alternative model in estimating the parameters associated with ANCOVA. This model is shown to be effective in accounting for covariate measurement error in the case where there aretwo treatment groups.

Description:
masters, M.S., Mathematics & Statistical Sci -- University of Idaho - College of Graduate Studies, 2022-05
Major Professor:
Johnson, Timothy
Committee:
Fu, Audrey; Williams, Chris; Abo, Hirotachi
Defense Date:
2022-05
Identifier:
Wilson_idaho_0089N_12367
Type:
Text
Format Original:
PDF
Format:
application/pdf

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