✦ LIBER ✦

A comparison of forecasts from least absolute value and least squares regression

✍ Scribed by Terry E. Dielman

Publisher: John Wiley and Sons
Year: 1986
Tongue: English
Weight: 462 KB
Volume: 5
Category: Article
ISSN: 0277-6693
DOI: 10.1002/for.3980050305

No coin nor oath required. For personal study only.

✦ Synopsis

A Monte Carlo simulation is used to compare forecasts from least absolute value and least squares estimated regression equations. When outliers are present, the least absolute value forecasts are shown to be superior to least squares forecasts. The results emphasize the importance of exercising caution when using forecasts from least squares estimated regressions. Use of least absolute value regression (or some other robust regression method) instead of, or as an adjunct to, least squares is recommended. The comparison of forecasts from the two methods provides one way of assessing whether the least squares forecasts have been adversely affected by outliers. If outliers are present, the least absolute value regression forecasts can be used.

📜 SIMILAR VOLUMES

Corrections to a comparison of forecasts

Corrections to a comparison of forecasts from least absolute value and least squares regression

✍ Terry E. Dielman 📂 Article 📅 1989 🏛 John Wiley and Sons 🌐 English ⚖ 75 KB 👁 1 views

The minimum sum of absolute errors regre

The minimum sum of absolute errors regression: a robust alternative to the least squares regression

✍ Subhash C. Narula; Paulo H. N. Saldiva; Carmen D. S. Andre; Silvia N. Elian; Aur 📂 Article 📅 1999 🏛 John Wiley and Sons 🌐 English ⚖ 134 KB 👁 1 views

This paper concerns the minimum sum of absolute errors regression. It is a more robust alternative to the popular least squares regression whenever there are outliers in the values of the response variable, or the errors follow a long tailed distribution, or the loss function is proportional to the

Prediction of mixture composition by chr

Prediction of mixture composition by chromatographic characterization, multivariate classification and partial least-squares regression, a comparison of methods

✍ Peter Kaufmann 📂 Article 📅 1993 🏛 Elsevier Science 🌐 English ⚖ 421 KB

Regional flood frequency analysis using

Regional flood frequency analysis using Bayesian generalized least squares: a comparison between quantile and parameter regression techniques

✍ Khaled Haddad; Ataur Rahman; Jery R. Stedinger 📂 Article 📅 2011 🏛 John Wiley and Sons 🌐 English ⚖ 331 KB

## Abstract Regression‐based regional flood frequency analysis (RFFA) methods are widely adopted in hydrology. This paper compares two regression‐based RFFA methods using a Bayesian generalized least squares (GLS) modelling framework; the two are quantile regression technique (QRT) and parameter re

Estimation of Hammett sigma constants fr

Estimation of Hammett sigma constants from calculated atomic charges using partial least squares regression

✍ Pierre Genix; Hervé Jullien; Rémi Le Goas 📂 Article 📅 1996 🏛 John Wiley and Sons 🌐 English ⚖ 377 KB 👁 1 views

Hammett constants describe the electronic donating or withdrawing capabilities of a given substituent. Since Hammett constants represent information of an electronic nature, their values are to be linked with atomic charges and orbital-derived parameters of the atoms of the substrate bearing the sub

Comparison of Artificial Neural Networks

Comparison of Artificial Neural Networks with Partial Least Squares Regression for Simultaneous Determinations by ICP-AES

✍ Mohamad KHAYATZADEH MAHANI; Marzieh CHALOOSI; Mohamad GHANADI MARAGHEH; Ali Reza 📂 Article 📅 2007 🏛 John Wiley and Sons 🌐 English ⚖ 68 KB 👁 1 views

## Abstract Simultaneous determination of several elements (U, Ta, Mn, Zr and W) with inductively coupled plasma atomic emission spectrometry (ICP‐AES) in the presence of spectral interference was performed using chemometrics methods. True comparison between artificial neural network (ANN) and part