conditional maximum likelihood estimation
cquad: An R and Stata Package for Conditional Maximum Likelihood Estimation of Dynamic Binary Panel Data Models
... for conditional maximum likelihood estimation of the quadratic exponential (QE) model proposed by Bar- tolucci and Nigro (2010) for the analysis of binary panel ...pseudo conditional ...
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Maximum likelihood estimation of higher-order integer-valued autoregressive processes
... for maximum likelihood estimation of GIN AR(p) processes based on a recursive representation of the transition proba- ...resulting likelihood, we derive the score function and the Fisher ...
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Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors
... The new contribution of this paper is the Residual Iterative Conditional Fitting (RICF) algorithm for maximum likelihood estimation in BAP models. Software for computation of MLEs in ...
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Conditional Likelihood for Risk Estimation in Genome Scans and Coefficient Shrinkage
... Pseudo maximum likelihood estimation was first proposed by Gong and Samaniego (1981) in the parametric set ...the likelihood with its empirical ...pseudo likelihood estimation in ...
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Computational approaches for maximum likelihood estimation for nonlinearmixed models.
... 20 be a nonlinear function of physically meaningful parameters; e.g. in pharmacokinet- ics, a function derived by compartmental modeling, depending in a nonlinear way on individual-specic absorption, elimination, and ...
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Readings in Targeted Maximum Likelihood Estimation
... outcomes, conditional density and hazard estimation, and can be generalized to censored outcomes such as sur- ...the estimation problem can be framed as a prediction problem where one is predicting ...
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Maximum Likelihood Estimation of Latent Affine Processes
... The conditional distribution of daily returns is approximately a mixture of three normals: normal daily volatility that varies stochastically over a range of ...Log likelihood rises from ...log ...
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Asymptotics of Maximum Composite Likelihood Estimation for Geostatistical Data
... composite conditional K-nearest neighbours likelihood and K-sequential neighbours likelihood were also tested for different values of ...the maximum likelihood estimates, we can see ...
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Estimation and tests for power-transformed and threshold GARCH models
... autoregressive conditional heteroscedastic (ARCH) model proposed by Engle (1982) has led to considerable interest in models in which the conditional variance (volatility) of the current observation, σ t 2 , ...
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Maximum Likelihood Estimation of the Multivariate Normal Mixture Model
... The mean squared error of the standard error is the sum of the variance and the square of the bias. The contribution of the bias is small. In the case reported in Table 3, the ratio of the absolute bias to the RMSE is 9% ...
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Generalized Least Squares Estimation for Cointegration Parameters Under Conditional Heteroskedasticity
... (RR) maximum likelihood (ML) approach is the most popular method for estimating the cointegration parameters in a vector error correction model (VECM) setup of a ...
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Estimation of Dynamic Stochastic Frontier Model using Likelihood based Approaches
... estimates are. The numbers in parentheses are the standard errors of the FML, PCL and QML estimators and computed using the inverse of the negative Hessian matrix. The numbers in brackets are the standard errors of the ...
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Analysis of Minute Features in Speckled Imagery with Maximum Likelihood Estimation
... the likelihood in the solutions, the one computed by Broyden was orders of times smaller than the one found by maximization tech- ...duced likelihood at the estimates produced by Broyden was − ...
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Maximum Likelihood Estimation for Mixtures of Spherical Gaussians is NP-hard
... Suppose the optimal k-means solution is realized by centers µ ∗ = (µ ∗ 1 , . . . , µ ∗ k ). Let π 1 ∗ = · · · = π k ∗ = 1/k and σ ∗2 = Φ(µ ∗ )/nd. To bound the log-likelihood of the mixture model (π ∗ , µ ∗ , σ ∗ ...
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Maximum likelihood estimation for multiscale Ornstein Uhlenbeck processes
... the likelihood of the observations is different depending on whether we observe a path of the slow process generated by (2a) or the homogenized process (16) (see also [21, 22, ...
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Maximum likelihood estimation for directional conditionally autoregressive models
... effects models to explain the latent spatial process using suitably formed neighbors (Breslow and Clayton, 1993). Gaussian CAR process has the merit that the finite dimensional joint distributions of the spatial process ...
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Maximum-Likelihood Estimation of Coalescence Times in Genealogical Trees
... Our method has the advantage of the optimal as- ymptotic properties (as the sequence length increases) of maximum-likelihood estimation and in simulation stud- ies was shown to give slight ...
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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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A maximum likelihood approach to correlation dimension and entropy estimation
... To obtain the correlation dimension and entropy from an experimental time series we derive estimators for these quantities together with expressions for their variances [r] ...
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Research on Initialization on EM Algorithm Based on Gaussian Mixture Model
... The average iteration times of the original EM algorithm is approximately 33, and the final iteration result is also highly unstable. Then we compare the para- meter estimation results of the original method and ...
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