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Maximum kernel likelihood estimation [PDF]
, 2008 We introduce an estimator for the population mean based on maximizing likelihoods formed by parameterizing a kernel density estimate. Due to these origins, we have dubbed the estimator the maximum kernel likelihood estimate (mkle). A speedy computational
Jaki, Thomas, West, R. Webster
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Particle methods for maximum likelihood estimation in latent variable models [PDF]
, 2008 Standard methods for maximum likelihood parameter estimation in latent variable models rely on the Expectation-Maximization algorithm and its Monte Carlo variants.
Davy, Manuel +8 more
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A simple approach to maximum intractable likelihood estimation [PDF]
, 2013 Approximate Bayesian Computation (ABC) can be viewed as an analytic approximation of an intractable likelihood coupled with an elementary simulation step.
Rubio, Francisco J., Johansen, Adam M.
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Maximum likelihood estimation of the parameters of a system of simultaneous regression equations [PDF]
, 1988 Procedures for computing the full information maximum likelihood (FIML) estimates of the parameters of a system of simultaneous regression equations have been described by Koopmans, Rubin, and Leipnik, Chernoff and Divinsky, Brown, and Eisenpress ...
James Durbin, Durbin, J.
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Warranty Claim Forecasting Based On Weighted Maximum Likelihood Estimation [PDF]
, 2012 Warranty claims reported in recent months might carry more up-to-date information than those reported in earlier months. Using weighted maximum likelihood estimation for estimating model parameters might therefore lead to better performance of warranty ...
Akbarov, Artur +3 more
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Journal of Applied Mathematics, 2022 Numerical computation of maximum likelihood estimates (MLE) is one of the most common problems encountered in applied statistics. Even if there exist many algorithms considered as performing, they can suffer in some cases for one or many of the following
Issa Cherif Geraldo
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Maximum likelihood estimation of Wiener models [PDF]
Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187), 2002 A Wiener model consists of a linear dynamic system followed by a static nonlinearity. The input and output are measured, but not the intermediate signal. We discuss the maximum likelihood estimate for Gaussian measurement and process noise, and the special cases when one of the noise sources is zero.
Hagenblad, Anna, Ljung, Lennart
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Statistical Inference for Geometric Process with the Power Lindley Distribution
Entropy, 2018 The geometric process (GP) is a simple and direct approach to modeling of the successive inter-arrival time data set with a monotonic trend. In addition, it is a quite important alternative to the non-homogeneous Poisson process.
Cenker Bicer
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Semi-Nonparametric Maximum Likelihood Estimation [PDF]
Econometrica, 1987 The density of Hermite forms: \[ h(u)=P^ 2_ k(u-\tau)\Phi^ 2(u| \tau,diag(\gamma)) \] where \(P_ k\) is a polynomial of degree K and \(\Phi\) is the density function of the multivariate normal distribution is shown to be capable of approximating any density arbitrarily closely subject to minimal qualifications relating to compactness, denseness ...
Gallant, A Ronald, Nychka, Douglas W
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Bayes and Non-Bayes Estimation of Change Point in Nonstandard Mixture Inverse Weibull Distribution
Journal of Statistical Theory and Applications (JSTA), 2019 We consider a sequence of independent random variables X1,X2,…,Xm,…,Xnn≥3 exhibiting a change in the probability distribution of the data generating mechanism.
Masoud Ganji, Roghayeh Mostafayi
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