Maximum likelihood decay curve fits by the simplex method

Estimation of Gillnet Selectivity Curve by Maximum Likelihood Method

Maximum likelihood estimation of survival curve parameters.

A maximum likelihood procedure is presented for the estimation of the parameters in a survival curve which is used in the quantitative investigation of cytological damage resulting from ionizing radiation. This estimation procedure is developed under the assumption that the observations are distributed as independent Poisson random variables. In addition, a weighted least squares procedure, whi...

GooFit: A library for massively parallelising maximum-likelihood fits

Fitting complicated models to large datasets is a bottleneck of many analyses. We present GooFit, a library and tool for constructing arbitrarily-complex probability density functions (PDFs) to be evaluated on nVidia GPUs or on multicore CPUs using OpenMP. The massive parallelisation of dividing up event calculations between hundreds of processors can achieve speedups of factors 200-300 in real...

The Dual of the Maximum Likelihood Method

Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. Summary The Maximum Likelihood method estimates the parameter values of a statistical model that maximize the corresponding likelihood function, given the sample information. This is the primal approach that, in this paper, is presented as a...

Nonparametric Maximum Likelihood Estimation by the Method of Sieves

Maximum likelihood estimation often fails when the parameter takes values in an infinite dimensional space. For example, the maximum likelihood method cannot be applied to the completely nonparametric estimation of a density function from an iid sample; the maximum of the likelihood is not attained by any density. In this example, as in many other examples, the parameter space (positive functio...