Estimating probabilities under the three-parameter gamma distribution using composite sampling

  • Authors:

  • Jeffrey N. Jonkman;Patrick D. Gerard;William H. Swallow

  • Affiliations:

  • Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;Department of Applied Economics and Statistics, Clemson University, Clemson, SC 20634, USA;Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA

  • Venue:
  • Computational Statistics & Data Analysis
  • Year:
  • 2009

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Abstract

Composite sampling may be used in industrial or environmental settings for the purpose of quality monitoring and regulation, particularly if the cost of testing samples is high relative to the cost of collecting samples. In such settings, it is often of interest to estimate the proportion of individual sampling units in the population that are above or below a given threshold value, C. We consider estimation of a proportion of the form p=P(XC) from composite sample data, assuming that X follows a three-parameter gamma distribution. The gamma distribution is useful for modeling skewed data, which arise in many applications, and adding a shift parameter to the usual two-parameter gamma distribution also allows the analyst to model a minimum or baseline level of the response. We propose an estimator of p that is based on maximum likelihood estimates of the parameters @a, @b, and @c, and an associated variance estimator based on the observed information matrix. Theoretical properties of the estimator are briefly discussed, and simulation results are given to assess the performance of the estimator. We illustrate the proposed estimator using an example of composite sample data from the meat products industry.