Back to Search
Start Over
Distribution of the C statistic with applications to the sample mean of Poisson data.
- Source :
-
Journal of Applied Statistics . Aug2020, Vol. 47 Issue 11, p2044-2065. 22p. 5 Charts, 8 Graphs. - Publication Year :
- 2020
-
Abstract
- The C statistic, also known as the Cash statistic, is often used in astronomy for the analysis of low-count Poisson data. The main advantage of this statistic, compared to the more commonly used χ 2 statistic, is its applicability without the need to combine data points. This feature has made the C statistic a very useful method to analyze Poisson data that have small (or even null) counts in each resolution element. One of the challenges of the C statistic is that its probability distribution, under the null hypothesis that the data follow a parent model, is not known exactly. This paper presents an effort towards improving our understanding of the C statistic by studying (a) the distribution of C statistic for a fully specified model, (b) the distribution of Cmin resulting from a maximum-likelihood fit to a simple one-parameter constant model, i.e. a model that represents the sample mean of N Poisson measurements, and (c) the distribution of the associated Δ C statistic that is used for parameter estimation. The results confirm the expectation that, in the high-count limit, both C statistic and Cmin have the same mean and variance as a χ 2 statistic with same number of degrees of freedom. It is also found that, in the low-count regime, the expectation of the C statistic and Cmin can be substantially lower than for a χ 2 distribution. The paper makes use of recent X-ray observations of the astronomical source PG 1116+215 to illustrate the application of the C statistic to Poisson data. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02664763
- Volume :
- 47
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- Journal of Applied Statistics
- Publication Type :
- Academic Journal
- Accession number :
- 144989521
- Full Text :
- https://doi.org/10.1080/02664763.2019.1704703