Application of the Huber and Hampel M-estimation in real estate value modeling

Adamczyk, Tomasz

doi:10.7494/geom.2017.11.1.15

Cited by 10 publications

(9 citation statements)

References 6 publications

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“…There are a fairly large number of functionals that provide robust M-estimates. The most common are combined functionals proposed by Huber [14,15] and Hempel [16,17]. They consist of a quadratic functional that ensures the optimality of estimates for the Gaussian distribution and modular one that allows obtaining a more robust estimate for distributions with heavy "tails" (outliers).…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Robust identification of non-stationary objects with nongaussian interference

Rudenko¹,

Bezsonov²,

Lebediev³

et al. 2019

EEJET

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Robust identification of non-stationary objects with nongaussian interference

Rudenko¹,

Bezsonov²,

Lebediev³

et al. 2019

EEJET

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“…If its value is more than parameter k, then the modification of the observation weights in the next iterations is realized by the expression (Adamczyk, 2017):…”

Section: Huber's Methodsmentioning

confidence: 99%

“…In the case when v value was higher than parameter k, the modification of weight was done for every observation which did not realize this criterion. All the calculations were made for k = 1.5 and k = 2, according to Adamczyk (2017). The results of Huber's method in search for outliers are given in Table 3 and 4.…”

Section: Huber's Methodsmentioning

confidence: 99%

Application of Chosen Methods of Robust Estimation: Baarda’s and Huber’s in Search for Outliers in the Real Estate Market Modeling

Śpiewak

2018

Folia Oeconomica Stetinensia

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In the article, an attempt was made to compile a dataset which was devoid of outliers, on the example of Cracovian apartment market. Robust estimation was the tool which was used, but only its two methods were considered: Baarda’s and Huber’s. Huber’s method belongs to the so-called active methods which means that it allows to eliminate gross errors during the estimation of the parameters of multiple linear regression where a unit price is called a dependent variable or forecasted one. Baarda’s method is a passive method which is based on statistical tests and allows, after determining the parameters of a multiple linear regression model, to indicate the observations which may be burdened with gross errors. Thus both mentioned algorithms differ from each other substantially. In this publication, Baarda’s and Huber’s methods were compared in the context of their effectiveness for the analyzed dataset, and as tools of preparing the data for further analysis. The results showed that Baarda’s method is more appropriate for the analyzed dataset than Huber’s algorithm, but it does not mean that the active method is worse.

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“…One of them is robust estimation, which is used mainly in economic analyses (ORWAT 2006;DEHNEL, GOŁATA 2010;MAJEWSKA 2011;JANSSEN, SODERBERG, ZHOU 1999) and when dealing with geodetic network adjustment (KAMIŃSKI, NOWEL 1992;WIŚNIEWSKI 2009;HUBER 1981). The application of chosen algorithms of robust estimation in modeling market value is presented in the papers (ADAMCZYK 2017;LIGAS 2010).…”

Section: Literature Review On the Application Of Robust Estimationmentioning

confidence: 99%

Application of Passive Methods of Robust Estimation: Baarda's and Pope's in Real Estate Market Analysis

Śpiewak

2018

Real Estate Management and Valuation

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The article presents the results of research into the appraisal of the usefulness and effectiveness of passive methods of robust estimation in real estate market analysis. Two methods are considered, i.e.: Baarda's and Pope's. The research is based on a database of flats sold in Kraków in 2015. It has been proved that algorithms of robust estimation allow for eliminating outlying transactions from the collected database, causing the set of observations to become coherent and free of observations which might be fraught with gross error caused by the human factor.

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Application of the Huber and Hampel M-estimation in real estate value modeling

Cited by 10 publications

References 6 publications

Robust identification of non-stationary objects with nongaussian interference

Robust identification of non-stationary objects with nongaussian interference

Application of Chosen Methods of Robust Estimation: Baarda’s and Huber’s in Search for Outliers in the Real Estate Market Modeling

Application of Passive Methods of Robust Estimation: Baarda's and Pope's in Real Estate Market Analysis

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