maximum-likelihood point estimation
Maximum Likelihood Estimation of Factored Regular Deterministic Stochastic Languages
... It is noteworthy to point out that establishing concavity does not mean the solution is unique. In fact, the solutions can be a set of points. An exam- ple FRPD class illustrating this is one which con- tains two ...
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On the Approximate Maximum Likelihood Estimation for Diffusion Processes
... forward likelihood expressions (Prakasa Rao, 1999) available for continuously observed diffusion ...time point to the next do not have finite analytic expressions except for only a few specific ...efficient ...
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Change Point Estimation of the Stationary State in Auto Regressive Moving Average Models, Using Maximum Likelihood Estimation and Singular Value Decomposition-based Filtering
... change point. Utilizing the maximum likelihood estimator (MLE), artificial neural networks (ANNs) and clustering were introduced to measure the change point alluded to in the ...change ...
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A simple approach to maximum intractable likelihood estimation
... ducting likelihood-based inference, despite the complexity of the corresponding likelihood function, on time series models which allow for simulating realisations at any parameter ...parameter ...
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Change Point Estimation of Location Parameter in Multistage Processes
... extended maximum likeliness ...developed maximum likelihood estimators for estimating out-of-control stage and product (sample) when a signal was received from the proposed DEWMA control ...
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Targeted maximum likelihood estimation for a binary treatment: A tutorial.
... for estimation of the ...time point, the propensity score is defined as the probability of being treated given the observed confounders W, denoted P(A = ...
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A Review of Methods for Point and Interval Estimation of Population Size in Capture-Recapture Studies
... Suppose one of the urns is chosen at random and we are trying to guess which urn. If we are told the value ofY (the number of red balls seen among two balls drawn from this urn) then intuitively this information is a big ...
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Computational approaches for maximum likelihood estimation for nonlinearmixed models.
... true likelihood be performed instead of using approximations and how can the optimization be performed with random in- put? By using numerical integration techniques, the likelihood can be approximated to ...
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Asymptotics of Maximum Composite Likelihood Estimation for Geostatistical Data
... neighbours likelihood and K-sequential neighbours likelihood were also tested for different values of ...the maximum likelihood estimates, we can see from Table ...have point estimates ...
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Model-Free IRL Using Maximum Likelihood Estimation
... Eq. 6. We refer to this version of Q-learning as Q-softmax. For both these Q-learning methods, we will continue to use the Boltzmann policy for the action distribution. This introduces a discrepancy between how the ...
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Maximum Likelihood Estimation of the Multivariate Normal Mixture Model
... 67) point out, delta methods such as the ‘supplemented’ EM method or the conditional bootstrap often underestimate the standard errors, and the same occurs ...
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Maximum likelihood estimation for stochastic processes - a martingale approach
... this point we quickly stress that we are not here concerned with invariance theorems (functional limit theorems) since this type of result is not of immediate relevance to the asymptotic theory of (ML) ...
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Maximum Likelihood Estimation by R
... the maximum likelihood estimates of the parameters, out$gradient is the gradient of the negative log- likelihood function at this point, out$hessian is the value of the second derivative at ...
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Maximum likelihood parametric reconstruction of forest vertical structure from inclined laser quadrat sampling.
... Laser point quadrat sampling has been suggested as one alternative, but previous statistical approaches to model- ing forest structure using such data have required impracti- cal sample ...for maximum ...
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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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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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Manifold learning and maximum likelihood estimation for hyperbolic network embedding
... Current methods for mapping networks to hyperbolic space are based on maximum likelihood estimations or manifold learning. The former approach is very accurate but slow; the latter improves efficiency at ...
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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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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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