maximum likelihood model parameters
Simple Penalties on Maximum-Likelihood Estimates of Genetic Parameters to Reduce Sampling Variation
... A measure of the quality of an estimator is its “loss,” i.e., the deviation of the estimate from the true value. This is an ag- gregate of bias and sampling variation. We speak of improv- ing an estimator if we can ...
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Some Inference Problems in Clustered (Longitudinal) Count Data with Over-dispersion
... sets, maximum likelihood estimation of the parameters using the above procedure poses difficulty ...of parameters and a Bayesian formulation of the model is required which is given ...
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Minimum Risk Training for Neural Machine Translation
... conventional maximum likelihood estimation, minimum risk training is ca- pable of optimizing model parameters di- rectly with respect to arbitrary evaluation metrics, which are not necessarily ...
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A new method of inference of ancestral nucleotide and amino acid sequences.
... A model of nucleotide or amino acid substitution was employed to analyze data of the present-day sequences, and maximum likelihood estimates of parameters such as bra[r] ...
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Asymptotic Comparison of Method of Moments Estimators and Maximum Likelihood Estimators of Parameters in Zero Inflated Poisson Model
... of θ also has no closed form expression and it has to be computed using a numerical procedure. Of course, it is much easier than computing the MLE of θ by maximiz- ing (2.1). He has also observed that finding the values ...
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Stochastic Restricted Maximum Likelihood Estimator in Logistic Regression Model
... regression model becomes unstable when there exists strong dependence among explanatory variables ...regression model is appropriate for this ...the model parameters becomes inaccurate because ...
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Odds Generalized Exponential-Inverse Weibull Distribution: Properties & Estimation
... new model, called the odds generalized exponential-inverse Weibull distribution based on T-X family presented by Alzaatreh et ...the model parameters is approached by maximum ...
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Methods of accounting for maternal effects in the estimation and prediction of genetic parameters : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy at Massey University
... Maximum likelihood techniques for estimating variance components have desirable ...of maximum likelihood methods for estimating variance components from unbalanced data is ...generating ...
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The Dual of the Maximum Likelihood
... model’s parameters, it is necessary to state the definition of the variance as the inner product of the sample information and the residuals (noise), as revealed by Equations (15) and (25), because it is the only ...
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The Burr X Fréchet Model for Extreme Values: Mathematical Properties, Classical Inference and Bayesian Analysis
... unknown parameters of BrXFr distribution. There are many situations where maximum likelihood estimator does not converge, especially with higher dimension ...the parameters. Here we assume the ...
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Modified Moment, Maximum Likelihood and Percentile Estimators for the Parameters of the Power Function Distribution
... The Power function distribution is a flexible life time distribution model that may offer a good fit to some sets of failure data. Theoretically Power function distribution is the inverse of Pareto distribution. ...
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Distributions of Maximum Likelihood Estimators and Model Comparisons
... the parameters are common in the two ...generating model and a negative exponential estimation ...estimation model, because then the term E w [ | j(θ, w) || θ= ˆ θ ] in the expectation term (5) is ...
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A New Class of Generalized Burr III Distribution for Lifetime Data
... The maximum likelihood estimates (MLEs) of the GGBIII parameters J, , , and g are computed by maximizing the objective function via the sub-routine NLMIXED in ...the parameters (standard error ...
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Comparing Parameter Estimates Obtained by Simulation Study and Real Life Data from the Two-Parameter Gamma Model
... the parameters, we can estimate its mode by using either nonparametric estimators of the density or another mode seeking technique such as the mean-shift algorithm (Fukunaga and Hostetler, ...for ...
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On the Maximum Likelihood and Least Squares Estimation for the Inverse Weibull Parameters with Progressively First Failure Censoring
... IW model, we consider the problem of estimating the parameters of the model using the maximum likelihood, the approximate maximum likelihood and the least square ...
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Burr Type XII Software Reliability Growth Model
... Growth model (SRGM) is a mathematical model of how the software reliability improves as faults are detected and ...growth model with time domain data. The unknown parameters of the ...
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Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data
... standard maximum likelihood regularity assumptions applied to the observable probability model representation ...the likelihood of an environment events assigned by the researcher’s complete ...
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Maximum likelihood estimation for stochastic Lotka–Volterra model with jumps
... We notice that there are huge works in economics and finance considering MLE of jump- diffusion models, where the data is usually observed discretely. In this case, transition densities play an important role, but their ...
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Maximum likelihood estimation of a stochastic frontier model with residual covariance
... frontier model are assumed to be independent random ...parametric model to describe it. The approach we adopt, through maximum likelihood, is similar to the earlier studies by Cliff and Ord ...
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Maximum likelihood estimation of population parameters.
... Under the assumptions that sequences are infinitely long and that the scaled coalescent times can be estimated without error, FELSENSTEIN (1992) showed that the improvement [r] ...
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