Identifying and removing sources of imprecision in polynomial regression

Neima Brauner*, Mordechai Shacham

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)75-91
Number of pages17
JournalMathematics and Computers in Simulation
Issue number1
StatePublished - 1 Nov 1998


  • Collinearity
  • Noise
  • Polynomial
  • Precision
  • Regression


Dive into the research topics of 'Identifying and removing sources of imprecision in polynomial regression'. Together they form a unique fingerprint.

Cite this