Abstract
In Chap. 3, the induced bias matrix is proposed to identify the inconsistent elements in a complete pairwise comparison matrix (PCM). Besides inconsistency, a PCM may be incomplete due to limited expertise or unwillingness to judge. For an incomplete pairwise comparison matrix (IPCM), the missing values must first be estimated in order for the IPCM to be useful. The revised PCM needs to pass the consistency test. For this purpose, we have extended the IBMM to estimate the missing values in an IPCM (Ergu et al. 2011c). The revised PCM with the estimated values by IBMM is shown to satisfy the consistency requirement. In this Chapter, the details of IBMM for missing data estimation in AHP/ANP are comprehensively addressed.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Ergu D, Kou G, Peng Y, Shi Y, Shi Yu (2011c) BIMM: a bias induced matrix model for incomplete reciprocal pairwise comparison matrix. J Multi-Crit Decis Anal. doi:10.1002/mcda.472
Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15(3):234–281
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Kou, G., Ergu, D., Peng, Y., Shi, Y. (2013). IBMM for Missing Data Estimation. In: Data Processing for the AHP/ANP. Quantitative Management, vol 1. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29213-2_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-29213-2_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29212-5
Online ISBN: 978-3-642-29213-2
eBook Packages: Business and EconomicsBusiness and Management (R0)