Displaying similar documents to “Accelerated Monte Carlo estimation of exceedance probabilities under monotonicity constraints”

How many bins should be put in a regular histogram

Lucien Birgé, Yves Rozenholc (2006)

ESAIM: Probability and Statistics

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Given an -sample from some unknown density on , it is easy to construct an histogram of the data based on some given partition of , but not so much is known about an optimal choice of the partition, especially when the data set is not large, even if one restricts to partitions into intervals of equal length. Existing methods are either rules of thumbs or based on asymptotic considerations and often involve some smoothness properties of . Our purpose in this paper is to give an automatic,...

Using auxiliary information in statistical function estimation

Sergey Tarima, Dmitri Pavlov (2006)

ESAIM: Probability and Statistics

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In many practical situations sample sizes are not sufficiently large and estimators based on such samples may not be satisfactory in terms of their variances. At the same time it is not unusual that some auxiliary information about the parameters of interest is available. This paper considers a method of using auxiliary information for improving properties of the estimators based on a current sample only. In particular, it is assumed that the information is available as a number of estimates...

Analysis on the individual efficiency prediction in the composed error frontier model. A Monte Carlo study.

Rafaela Dios Palomares, Antonio Ramos Millán, José Angel Roldán-Casas (2002)

Qüestiió

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This study seeks to analyse some important questions related to the Stochastic Frontier Model, such as the method proposed by Jondrow et al (1982) to separate the error term into its two components, and the measure of efficiency given by Timmer (1971). To this purpose, a Monte Carlo experiment has been carried out using the Half-Normal and Normal-Exponential specifications throughout the rank of the γ parameter. The estimation errors have been eliminated, so that the intrinsic variability...