On nonparametric maximum likelihood for a class of stochastic inverse problems

Meyer K (1989) Restricted maximum likelihood to estimate variance components for animal models with several random effects using a derivative-free algorithm. User

The Thurstonian view described in the finite model can be easily extended to the nonparametric one, where a partial ranking (X ρ `1. X ρ `m ) then constitutes our top-m list,

We consider the problem of fitting and feature selection in MoE models, and propose a regularized maximum likelihood estimation approach that encourages sparse solutions

R EMARK 1.6.– It should be emphasized that to estimate the function S in [1.1.1] we use the approach developed in Galtchouk and Pergamenshchikov (2011) for the sequential

Abstract: We construct a robust truncated sequential estimators for the pointwise estimation problem in nonpara- metric autoregression models with smooth coefficients.. For

We also propose a Markov Chain Monte Carlo algorithm to approx- imately sample from this posterior distribution inspired from a recent Bayesian nonparametric method for hidden

Note that generating random samples from p i (ψ i |y i ) is useful for several tasks to avoid approximation of the model, such as linearisation or Laplace method. Such tasks include

However, on the example of the foundation embedded in a layer of random soil on a rigid bedrock, this model error seems to have little effect on the shaking element of the