Identifying and removing sources of imprecision in polynomial regression

Neima Brauner and Mordechai Shacham

Mathematics and Computers in Simulation (MATCOM), 1998, vol. 48, issue 1, 75-91

Abstract: Identification and removal of imprecision in polynomial regression, originating from random errors (noise) in the independent variable data is discussed. The truncation error-to-noise ratio (TNR) is used to discriminate between imprecision dominated by collinearity, or numerical error propagation, or inflated variance due to noise in the independent variable. It is shown that after the source of the imprecision has been identified, it can often be removed by simple data transformations or using numerical algorithms which are less sensitive to error propagation (such as QR decomposition). In other cases, more precise independent variable data may be required to improve the accuracy and the statistical validity of the correlation.

Keywords: Regression; Polynomial; Precision; Noise; Collinearity (search for similar items in EconPapers)
Date: 1998
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378475498001463
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:48:y:1998:i:1:p:75-91

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().