1980•Communication in Statistics- Theory and MethodsRequires access

Estimation with truncated inverse binomial sampling

Saul B umenthal, Laiitha Sanathanan

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Abstract

Consider truncated samples taken from an infinite population with a fixed uumber n of observations recorded. A randon number X of items must be sampled in order to observe after trunca-tion. adified maximun liicel.ihooa estimators of X (assumed un-'mown) and of the population parameters are develo~cd, and a computing scheme is given for the exponential distribution. On the basis of asymptotu ,operties, some estimators are singled out and compared with the usual maximum likelihood estimators.

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What this paper is about

Consider truncated samples taken from an infinite population with a fixed uumber n of observations recorded. A randon number X of items must be sampled in order to observe after trunca-tion. adified maximun liicel.ihooa estimators of X (assumed un-'mown) and of the population parameters are develo~cd, and a computing scheme is given for the exponential distribution. On the basis of asymptotu ,operties, some estimators are singled out and compared with the usual maximum likelihood estimators.

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Available abstract

Consider truncated samples taken from an infinite population with a fixed uumber n of observations recorded. A randon number X of items must be sampled in order to observe after trunca-tion. adified maximun liicel.ihooa estimators of X (assumed un-'mown) and of the population parameters are develo~cd, and a computing scheme is given for the exponential distribution. On the basis of asymptotu ,operties, some estimators are singled out and compared with the usual maximum likelihood estimators.

Key concepts: Statistics, Negative binomial distribution, Binomial (polynomial), Mathematics, Estimation, Sampling (signal processing), Econometrics, Inverse

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