Predicting the value of an integer-valued random variable

Daniel R. Jeske

Statistics & Probability Letters, 1993, vol. 16, issue 4, 297-300

Abstract: Predicting the value of a random variable Y, based on the observed value of another random variable X is a common objective of data analysis. It is well-known that the minimum mean-squared error predictor of Y is the mean of the conditional distribution of Y, given X. In cases where Y is necessarily integer-valued, the conditional mean is not always a feasible value for Y and, therefore, is an unsatisfactory predicted value. In this paper, it is shown how minimum mean-squared error integer-valued predictors can be obtained.

Keywords: Mean-squared; error; integer-valued; distributions; unbiased; prediction (search for similar items in EconPapers)
Date: 1993
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