Transparent Data Mining for Big and Small Data

  • Cerquitelli T
  • Quercia D
  • Pasquale F
N/ACitations
Citations of this article
122Readers
Mendeley users who have this article in their library.

Abstract

The performances of three different estimators for the roughness parameterof an important distribution for synthetic aperture radar data analysisare compared: those of a moment estimator, of the maximum likelihoodestimator and of the second-order bias-corrected maximum likelihoodestimator. A Monte Carlo study is designed to perform this comparison,due to the untractability of the estimators distributions from ananalytical point of view. From this study, the use of the second-orderbias-corrected maximum likelihood estimator is suggested.

Cite

CITATION STYLE

APA

Cerquitelli, T., Quercia, D., & Pasquale, F. (2017). Transparent Data Mining for Big and Small Data. Springer International Publishing (p. 223). Retrieved from http://link.springer.com/10.1007/978-3-319-54024-5

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free