Maximum Approximate Bernstein Likelihood Estimation

Fit raw or grouped continuous data from a population with a smooth density on unit interval by an approximate Bernstein polynomial model which is a mixture of certain beta distributions and find maximum approximate Bernstein likelihood estimator of the unknown coefficients. Consequently, maximum likelihood estimates of the unknown density, distribution functions, and more can be obtained. If the support of the density is not the unit interval then transformation can be applied. This is an implementation of the methods proposed by the author this package published in the Journal of Nonparametric Statistics: Guan (2016) and Guan (2017) .


Reference manual

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1.0 by Zhong Guan, a year ago

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Authors: Zhong Guan [aut, cre]

Documentation:   PDF Manual  

GPL (>= 3) license

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