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Semiparametric maximum likelihood probability density estimation
2021
PLoS ONE
A comprehensive methodology for semiparametric probability density estimation is introduced and explored. The probability density is modelled by sequences of mostly regular or steep exponential families generated by flexible sets of basis functions, possibly including boundary terms. Parameters are estimated by global maximum likelihood without any roughness penalty. A statistically orthogonal formulation of the inference problem and a numerically stable and fast convex optimization algorithm
doi:10.1371/journal.pone.0259111
pmid:34752460
pmcid:PMC8577774
fatcat:5d3za43znvgfjl2yznkjmys26i