Nonparametric and Semiparametric Mixture Estimation
Maximum Likelihood Estimation of a Nonparametric Mixture Model
Maximum Likelihood Estimation of a Semiparametric Mixture Model
Class cvps
Class disc
Density function of a mixture distribution
Grid points
Hierarchical Constrained Newton method
Initialization for a nonparametric/semiparametric mixture
Initialisation
Log-likleihood Extra Term.
Derivative of the log-likleihood Extra Term
Log-density and its derivative values
Log-likelihood value of a mixture
Class mlogit
Class npgeom
Class npnbinom
Class npnorm
Class nppois
Plot a discrete distribution function
Plotting a nonparametric geometric mixture
Plotting a nonparametric negative binomial mixture
Plotting a Nonparametric or Semiparametric Normal Mixture
Plotting a nonparametric Poisson mixture
Plots a function for an object of class nspmix
Plot the Gradient Function
Prints a discrete distribution function
Sorting of an Object of Class npnorm
Sorting of an Object of Class nppois
Support space
Valid parameter values
Weights
Weighted HistogramsPlots or computes the histogram with observations w...
Mainly for maximum likelihood estimation of nonparametric and semiparametric mixture models, but can also be used for fitting finite mixtures. The algorithms are developed in Wang (2007) <doi:10.1111/j.1467-9868.2007.00583.x> and Wang (2010) <doi:10.1007/s11222-009-9117-z>.