Numerical algorithms for constrained maximum likelihood estimation
نویسندگان
چکیده
منابع مشابه
A comparison of algorithms for maximum likelihood estimation of Spatial GLM models
In spatial generalized linear mixed models, spatial correlation is assumed by adding normal latent variables to the model. In these models because of the non-Gaussian spatial response and the presence of latent variables the likelihood function cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. The main purpose of this paper is to introduce two n...
متن کاملKullback Proximal Algorithms for Maximum Likelihood Estimation
Accelerated algorithms for maximum likelihood image reconstruction are essential for emerging applications such as 3D tomography, dynamic tomographic imaging, and other high dimensional inverse problems. In this paper, we introduce and analyze a class of fast and stable sequential optimization methods for computing maximum likelihood estimates and study its convergence properties. These methods...
متن کاملOn Optimization Algorithms for Maximum Likelihood Estimation
Maximum likelihood estimation (MLE) is one of the most popular technique in econometric and other statistical applications due to its strong theoretical appeal, but can lead to numerical issues when the underlying optimization problem is solved. We examine in this paper a range of trust region and line search algorithms and focus on the impact that the approximation of the Hessian matrix has on...
متن کاملConstrained Maximum-likelihood Covariance Estimation for Time-varying Sensor Arrays
We examine the problem of maximum likelihood covariance estimation using a sensor array in which the relative positions of individual sensors change over the observation interval The problem is cast as one of estimating a structured covariance matrix sequence. A vector space structure is imposed on such sequences, and within that vector space we define a constraint space given by the intersecti...
متن کاملConstrained Nonparametric Maximum Likelihood Estimation for Mixture Models
A nonparametric mixture model speciies that observations arise from a mixture distribution , R f(x;) dG(); where the mixing distribution, G; is completely unspeciied. A number of algorithms have been developed to obtain unconstrained maximum likelihood estimates of G (see, for instance, Laird 1978, Bohning 1985, and Lesperance and Kalbbeisch 1992), but none of these algorithms lead to estimates...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The ANZIAM Journal
سال: 2003
ISSN: 1446-1811,1446-8735
DOI: 10.1017/s1446181100013171