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Implied Probabilities in GMM Estimators
Econometrica, 1993The conventional way to estimate a distribution function is to assume it belongs to a class parameterized by a finite-dimensional vector and then estimate the unknown parameter vector. In many cases, e.g., regression models, part of the assumption is of the form: a given function of the data and of the parameter vector has a zero mean.
Back, Kerry, Brown, David P
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A Computational Implementation of GMM [PDF]
SSRN Electronic Journal, 2014 In this paper we study a statistical method of implementing quasi-Bayes estimators for nonlinear and nonseparable GMM models, that is motivated by the ideas proposed in Chernozhukov and Hong (2003) and Creel and Kristensen (2011) and that combines simulation with nonparametric regression in the computation of GMM models.
Jiti Gao, Han Hong
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GMM-based significance decoding
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013The accuracy of automatic speech recognition systems in noisy and reverberant environments can be improved notably by exploiting the uncertainty of the estimated speech features using so-called uncertainty-of-observation techniques. In this paper, we introduce a new Bayesian decision rule that can serve as a mathematical framework from which both known
Ahmed Hussen Abdelaziz +4 more
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Econometric Theory, 2009
This paper proposes the least absolute shrinkage and selection operator–type (Lasso-type) generalized method of moments (GMM) estimator. This Lasso-type estimator is formed by the GMM objective function with the addition of a penalty term. The exponent of the penalty term in the regular Lasso estimator is equal to one.
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This paper proposes the least absolute shrinkage and selection operator–type (Lasso-type) generalized method of moments (GMM) estimator. This Lasso-type estimator is formed by the GMM objective function with the addition of a penalty term. The exponent of the penalty term in the regular Lasso estimator is equal to one.
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An Improved SalBayes Model with GMM
2011SalBayes is an efficient visual attention model. We describe an improved SalBayes model with Gaussian Mixture Model (GMM) which can fit the object with various transformations better. The improved model learns the probability of an object's visual appearance within a particular feature map, and the Probability Distribution Function (PDF) is modeled ...
Hairu Guo +3 more
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Performances Evaluation of GMM-UBM and GMM-SVM for Speaker Recognition in Realistic World
2011In this paper, an automatic speaker recognition system for realistic environments is presented. In fact, most of the existing speaker recognition methods, which have shown to be highly efficient under noise free conditions, fail drastically in noisy environments.
Nassim Asbai +2 more
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GMM-based SVM for face recognition
18th International Conference on Pattern Recognition (ICPR'06), 2006A new face recognition algorithm is presented. It supposes that a video sequence of a person is available both at enrollment and test time. During enrollment, a client Gaussian mixture model (GMM) is adapted from a world GMM using eigenface features extracted from each frame of the video.
Bredin, Hervé +2 more
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Gaussian Component Based Index for GMMs
2016 IEEE 16th International Conference on Data Mining (ICDM), 2016Efficient similarity search for uncertain data is a challenging task in many modern data mining applications like image retrieval, speaker recognition and stock market analysis. A common way to model the uncertainty of data objects is using probability density functions in the form of Gaussian Mixture Models (GMMs), which have an ability to approximate
Linfei Zhou +4 more
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Tricks with Hicks: Stata GMM code for nonlinear gmm [PDF]
, 2011 In a June, 2009 American Economic Review article entitled "Tricks with Hicks: The EASI Demand System", Arthur Lewbel and Krishna Pendakur proposed the exact affine Stone index demand system. This system allows Engel curve behavior higher than rank 3, demographics, and unobserved heterogeneity in tastes.
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Sequential and efficient GMM estimation of dynamic short panel data models
Econometric Reviews, 2021Fei Jin, Lung-Fei Lee
exaly