maximum likelihood estimation technique
A New Class of Generalized Burr III Distribution for Lifetime Data
... presented. Maximum likelihood estimation technique is used to estimate the model parameters and applications to real datasets in order to illustrate the usefulness of the model are ...
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Savings, entrepreneurial trait and self-employment: evidence from selected Ghanaian Universities
... the Maximum Likelihood Estimation technique (MLE) specifically a probit model, they observed that personality factors such as extraversion, neuroticism, and agreeableness, support from family ...
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Modelling asymmetric conditional heteroskedasticity in financial asset returns: an extension of Nelson’s EGARCH model
... Quasi-maximum likelihood estimation technique coupled with martingale techniques, while relaxing the independence assumption of the innovations; the paper has shown that the proposed ...
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Maximum-Likelihood Estimation of Coalescence Times in Genealogical Trees
... profile likelihood while PAML use asymptotic normality of the MLEs and estimates the variance of the MLEs from the information ...The likelihood-based ...
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Maximum likelihood estimation for directional conditionally autoregressive models
... From the results based on the ML estimates of CAR model, we observe that for all the cases there are no significant biases at 5% level. The ESE of ρ is a good approximation to finite sample variance when the spatial ...
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Maximum likelihood estimation for multiscale Ornstein Uhlenbeck processes
... the estimation problem for discretely observed diffusions (see [21, 22, ...The maximum likelihood estimator for the drift of a homogenized equation converges after proper ...
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Maximum-Likelihood Estimation of Relatedness
... less well than the former ( J. Wang, personal communi- frequent, and one in which allele frequencies at each cation). Note that some of these nonlikelihood estima- locus were independently drawn from the same Dirich- ...
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Maximum Likelihood Estimation of the Multivariate Normal Mixture Model
... The maximum likelihood estimates themselves are usually computed via the EM algorithm, which is a derivative-free method, but they can also be computed directly from the likelihood or by setting the ...
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Asymptotics of Maximum Composite Likelihood Estimation for Geostatistical Data
... Stein et al. (2004) extended the above work by investigating the effect of conditioning on observations that are not necessarily the nearest neighbours. This is done in the context of restricted maximum com- ...
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Maximum likelihood estimation for stochastic processes - a martingale approach
... Another contribution to ML estimation for dependent observations has been made in two papers by Weiss [1, 2], which are discussed in detail in 2.§5. These do not use the martingale property and are restricted to ...
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Computational approaches for maximum likelihood estimation for nonlinearmixed models.
... In order to study the robustness of the two approximate methods to the assump- tions of normal random eects and specication of the model d in Equation (2.2), we carried out simulation studies in which several factors ...
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Estimation of Dynamic Stochastic Frontier Model using Likelihood based Approaches
... The earlier SF panel models (Pitt and Lee, 1981; Schmidt and Sickles, 1984; Kumbhakar, 1987; among others) treated technical inefficiency as time invariant. Although subsequent researchers allowed the inefficiency to ...
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Manifold learning and maximum likelihood estimation for hyperbolic network embedding
... proposed technique is not to find a specific set of coordinates, but the one that corresponds better with the hyperbolic, distance-dependent connection probabilities that produce the network of ...
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A Phrase Based HMM Approach to Document/Abstract Alignment
... simple maximum likelihood estimation is in- adequate for this model: the maximum likelihood solution is simply to make phrases as long as pos- sible; unfortunately, doing so will first ...
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5sociolinguistics pdf
... resulting likelihood values λ . The aim of maximum likelihood estimation is to find the value of π that has the highest ...this likelihood function, the point that is chosen as the best ...
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Approximate maximum likelihood estimation for population genetic inference
... approximate maximum likelihood methods have been proposed that estimate the likelihood surface in an ABC like fashion (Creel and Kristensen, 2013; Rubio and Johansen, 2013) or using MCMC (de Valpine, ...
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Research on Initialization on EM Algorithm Based on Gaussian Mixture Model
... the maximum-likelihood estimates ( MLEs ) for mixture distributions is the EM algorithm ( Dempster et ...the maximum likelihood estimate when the observation data is incomplete data, which has ...
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Model-Free IRL Using Maximum Likelihood Estimation
... The problem of learning an expert’s unknown reward func- tion using a limited number of demonstrations recorded from the expert’s behavior is investigated in the area of inverse re- inforcement learning (IRL). To gain ...
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Maximum Likelihood Estimation of Feature Based Distributions
... and P r(+G | +G) ; it is not an independent pa- rameter. In fact, because P r(a | b) is computed directly as the normalized product of parameters P r(−F | +F ) and P r(+G | +G) , the assump- tion is that the features F ...
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Estimation of the Reliability Measures of a Three component System with Human Errors and Common Cause Failures
... Therefore, the M L estimators are generally hard to beat consistently, even in small samples and our simulation results showed a strong preference for the M L estimation method for situations arising in practical ...
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