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Inferences for Two-Component Mixture Models with Stochastic Dominance
In this thesis, we studied a two-component nonparametric mixture model with a stochastic dominance constraint, which is a model that arises naturally from genetic studies. For this model, we proposed and studied nonparametric estimation based on cumulative distribution functions (c.d.f.s) and maximum likelihood estimation (MLE) through multinomial approximation. In order to incorporate the stochastic dominance constraint, we introduced a semiparametric model structure for which we proposed anddoi:10.11575/prism/5396 fatcat:slpfscbbmfftjina2zt3bamzra