Kernel density estimations applied to gamma ray light curves

Details Kernel density estimations applied to gamma ray light curves Kernel density estimations applied to gamma ray light curves

Dec 1986

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1986a%26a...170..187d&link_type=abstract

Astronomy and Astrophysics (ISSN 0004-6361), vol. 170, no. 1, Dec. 1986, p. 187-196. Research supported by the Council for Scien

Statistics

Computation

37

Computational Astrophysics, Gamma Ray Astronomy, Histograms, Kernel Functions, Light Curve, Probability Density Functions, Data Smoothing, Estimators, Random Variables

Scientific paper

Two kernel estimators, the naive estimator (NE) and the Fourier series estimator (FSE), are compared with the histogram estimator (HE) for a wide variety of possible unimodal light curves. Kernel estimators are shown to be objective and asymptotically unbiased, allowing errors to be approximated using the variance component only, and they are found to be smoother and more consistent than the HE. The NE is found to be better than the FSE for very narrow and weak peaks, though the FSE is optimal for broader smoother and stronger peaks.

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