Abstract

The presence of even a single outlier in a sample estimate can have strong repercussions on the regression models obtained with the method of least squares, nullifying its reliability. This is a condition to avoid in real estate appraisal where regression is used with predictive and explanatory purposes, and therefore it is essential that the regression model best represents the phenomenon investigated. In this study the outliers detection was carried out with a robust regression that uses the method of least median of squared residuals (LMS). With the aid of a special software, the calculations were performed on a sample of houses recently sold in a district of the city of Bari (Italy). The experiment revealed that the regression model, which was initially to be rejected, showed instead excellent performance once all the outliers identified with the LMS were removed from the sample

Autore Pugliese

• Morano Pierluigi

Tutti gli autori

• Morano P , De Mare G , Tajani F

Titolo volume/Rivista

LECTURE NOTES IN COMPUTER SCIENCE

Anno di pubblicazione

2013

ISSN

0302-9743

ISBN

Non Disponibile

Numero di citazioni Wos

Nessuna citazione

Ultimo Aggiornamento Citazioni

Non Disponibile

Numero di citazioni Scopus

28

Ultimo Aggiornamento Citazioni

2017-04-22 03:20:59

Settori ERC

Non Disponibile

Codici ASJC

Non Disponibile