Singularly Imputed vs the Exact Likelihood Model
Imputed Empirical Likelihood for Varying Coefficient Models with Missing Covariates
... Empirical Likelihood; Varying Coefficient Model; Missing Covariate ...linear model with missing covariates, which is a useful extension of the parametric regression ...coefficient model is ...
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Exact Maximum Likelihood Estimation for Copula Models
... maximum likelihood estimation), semi- parametric estimation and non-parametric ...maximum likelihood estimations usually include the exact maximum likelihood method (EML) and the inference for ...
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Exact Maximum Likelihood estimation for the BL-GARCH model under elliptical distributed innovations
... Further, as the simulation experiments show, one advantage of the maximum likelihood estimator procedure, proposed in this paper, compared to the method used by Storti and Vitale 2003b, [r] ...
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Exact Maximum Likelihood estimation for the BL-GARCH model under elliptical distributed innovations
... (ARCH) model. The ARCH model, developed by Engle (1982) and later extended to the GARCH model by Bollerslev (1986), formu- lates the conditional variance of a random variable as a linear function of ...
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Choosing the number of clusters in a finite mixture model using an exact integrated completed likelihood criterion
... Here we focus more on the role of the hyperparameters in determining the final GCICL solution, essentially providing a sensitivity analysis. In particular we make use of simulated data t[r] ...
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Estimation of multivariate probit models by exact maximum likelihood
... the exact likelihood function of lower dimen- sion allows for a major reduction of computing time while simultaneously obtaining consistent and efficient estimates for both the slope and the scale ...
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Joint modelling of repeated measurements and time-to-event outcomes: flexible model specification and exact likelihood inference.
... In this paper we propose an approach which admits exact likelihood inference for a wide range of random-effect specifications. The key is to consider events S to occur only at a discrete set of potential ...
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Exact likelihood computation for nonlinear DSGE models with heteroskedastic innovations
... DSGE model with nominal rigidities, where technology and government spending shocks are potentially characterised by high or low variance ...the model on US data over the 1966Q1-2009Q1 sample and …nd ...
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A Convex Approach for Image Restoration with Exact Poisson-Gaussian Likelihood
... Poisson-Gaussian model can accurately describe the noise present in a number of imaging ...the exact, mixed continuous-discrete model corresponding to the data ...the exact data fidelity term ...
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A Convex Approach for Image Restoration with Exact Poisson-Gaussian Likelihood
... the exact data fidelity ...EXP model. The computational time difference between Exact and EXP data fidelity term may result from: i) the relatively high value of Lipschitz constant and ii) the ...
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Likelihood free model choice
... for model choice and this results in a loss of information, when compared with the exact inferential approach, hence a wider discrepancy between the exact Bayes factor and the quantity produced by an ...
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Likelihood-free model choice
... for model choice and this results in a loss of information, when compared with the exact inferential approach, hence a wider discrepancy between the exact Bayes factor and the quantity produced by an ...
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Exact Likelihood Estimation and Probabilistic Forecasting in Higher order INAR(p) Models
... the likelihood function using both our new method and the direct method based on ...INAR(2) model 4 as well as an INAR(5) model 5 ...the likelihood function using the two ...
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Estimation of Technical and Allocative Inefficiencies in a Cost System: An Exact Maximum Likelihood Approach
... It is often argued that since the data is from the 1970s, the empirical results are not relevant to the electric utility industry of today. Comparing our results to those that used the recent data (for example, Kumbhakar ...
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Exact properties of the maximum likelihood estimator in exponential regression models: a differential geometry approach
... maximum likelihood estimator ...the exact density even when the estimator is only implicitly de…ned in terms of the ...regression model is well-known to be of this type, and in this paper we apply ...
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Distributions of Maximum Likelihood Estimators and Model Comparisons
... with exact techniques to be preferred unless they are too difficult to apply in a particular situa- ...estimation model is equivalent to the data generating ...generating model differs from the ...
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Bayesian model comparison with un normalised likelihood
... this model the exact posterior is available at each SMC target, so we may replace the use of an MCMC move to update the parameter with a direct simulation from the poste- ...
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Likelihood Ratio Tests for Multiply Imputed Datasets: Introducing milrtest
... Carlin, J. B., J. C. Calati, & P. Royson (2008) A new framework for managing and analyzing multiply imputed data in Stata. The Stata Journal 8(1): 49-67. Carlin, J.B., N. Li, P. Greenwood, & C. Coffey ...
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Model building with multiply imputed data
... 50 model M110 more ...true model M110 more ...stacked imputed data with weighted linear regression is better than RR approach applied to linear ...models. Model averaging using multiple ...
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Exact slow fast decomposition of the nonlinear singularly perturbed optimal control problem
... Fridman, Exact slow-fast decomposition of a class of nonlinear singularly perturbed optimal control problems via invariant manifolds, Internat.. Gajic, Eigenvector approach for order red[r] ...
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