Fitting power-law distributions to data with measurement errors

Details Fitting power-law distributions to data with measurement errors Fitting power-law distributions to data with measurement errors

Jul 2009

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009mnras.397..495k&link_type=abstract

Monthly Notices of the Royal Astronomical Society, Volume 397, Issue 1, pp. 495-505.

Astronomy and Astrophysics

Astronomy

2

Methods: Statistical, Ism: Clouds, Galaxies: Ism

Scientific paper

If X, which follows a power-law distribution, is observed subject to Gaussian measurement error e, then X + e is distributed as the convolution of the power-law and Gaussian distributions. Maximum-likelihood estimation of the parameters of the two distributions is considered. Large-sample formulae are given for the covariance matrix of the estimated parameters, and implementation of a small-sample method (the jackknife) is also described. Other topics dealt with are tests for goodness of fit of the posited distribution, and tests whether special cases (no measurement errors or an infinite upper limit to the power-law distribution) may be preferred. The application of the methodology is illustrated by fitting convolved distributions to masses of giant molecular clouds in M33 and the Large Magellanic Cloud (LMC), and to HI cloud masses in the LMC.

Affiliated with

Also associated with

No associations

Rating

Fitting power-law distributions to data with measurement errors does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Fitting power-law distributions to data with measurement errors, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Fitting power-law distributions to data with measurement errors will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-1863955