This paper extends previous work on the distribution-free newsvendor problem, where only partial information about the demand distribution is available. More specifically, the analysis assumes that the demand distribution f belongs to a class of probability distribution functions (pdf) ℱ with mean μ and standard deviation σ. While previous work has examined the expected value of distribution information (EVDI) for a particular order quantity and a particular pdf f, this paper aims at computing the maximum EVDI over all f ∈ ℱ for any order quantity. In addition, an optimization procedure is provided to calculate the order quantity that minimizes the maximum EVDI.
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Biometrika Trust is collaborating with JSTOR to digitize, preserve and extend access to Biometrika. SUMMARY This paper presents a class of smooth weight function estimators for discrete distributions. Any estimator in the class depends on choosing a parameterized set of weights. The resulting estimators are strongly consistent and asymptotically normal under mild regularity conditions. A general procedure for choosing the weight function smoothing parameter is given along with specific solutions in some cases. Mean squared error comparisons with the maximum likelihood estimator based on large-sample theory and small-sample simulations are obtained. Typically, the weight function estimates yield significantly smaller mean squared error in these comparisons.
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