Center strategies for universal transformations: modified iteration policy and two alternative models

Hu, Yu; Fang, Xing

doi:10.1007/s10291-023-01419-3

Cited by 8 publications

(1 citation statement)

References 42 publications

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“…The terminology of TLS was first coined by Golub and van Loan [49], who also proposed the widely used algorithm based on Singular Value Decomposition (SVD). Nowadays, it finds application in the fields of signal processing [50,51], image processing [52,53], and applied geodesy [54][55][56][57][58][59], to name a few. Its aim is to minimize the sum of the squares of all random errors in the model.…”

Section: Total Least Squares Estimationmentioning

confidence: 99%

Total Least Squares Estimation in Hedonic House Price Models

Zhan,

Hu,

Zeng

et al. 2024

IJGI

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In real estate valuation using the Hedonic Price Model (HPM) estimated via Ordinary Least Squares (OLS) regression, subjectivity and measurement errors in the independent variables violate the Gauss–Markov theorem assumption of a non-random coefficient matrix, leading to biased parameter estimates and incorrect precision assessments. In this contribution, the Errors-in-Variables model equipped with Total Least Squares (TLS) estimation is proposed to address these issues. It fully considers random errors in both dependent and independent variables. An iterative algorithm is provided, and posterior accuracy estimates are provided to validate its effectiveness. Monte Carlo simulations demonstrate that TLS provides more accurate solutions than OLS, significantly improving the root mean square error by over 70%. Empirical experiments on datasets from Boston and Wuhan further confirm the superior performance of TLS, which consistently yields a higher coefficient of determination and a lower posterior variance factor, which shows its more substantial explanatory power for the data. Moreover, TLS shows comparable or slightly superior performance in terms of prediction accuracy. These results make it a compelling and practical method to enhance the HPM.

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