Statistical Simulation
Zahra Zandi; Hossein Bevrani; Reza Arabi Belaghi
Abstract
In this paper, we consider the problem of parameter estimation in {color{blue} negative binomial mixed model} when it is suspected that some of the fixed parameters may be restricted to a subspace via linear shrinkage, {color{blue} preliminary test}, shrinkage {color{blue} ...
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In this paper, we consider the problem of parameter estimation in {color{blue} negative binomial mixed model} when it is suspected that some of the fixed parameters may be restricted to a subspace via linear shrinkage, {color{blue} preliminary test}, shrinkage {color{blue} preliminary test}, shrinkage, and positive shrinkage estimators along with the unrestricted maximum likelihood and restricted estimators. The random effects are considered as nuisance parameters. We conduct a Monte Carlo simulation study to evaluate the performance of each estimator in the sense of simulated relative efficiency. The results of simulation study reveal that the proposed estimation strategies perform more better than {color{blue} the} maximum likelihood method. The proposed estimators are applied to a real dataset to appraise their performance.
Statistical Simulation
farzad eskandari
Abstract
Imprecise measurement tools produce imprecise data. Interval-valued data is usually used to deal with such imprecision. So interval-valued variables are used in estimation methods. They have recently been modeled by linear regression models. If response variable has any statistical distributions, interval-valued ...
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Imprecise measurement tools produce imprecise data. Interval-valued data is usually used to deal with such imprecision. So interval-valued variables are used in estimation methods. They have recently been modeled by linear regression models. If response variable has any statistical distributions, interval-valued variables are modeled in generalized linear models framework. In this article, we propose a new consistent estimator of a parameter in generalized linear models with regard to distributions of response variable in the exponential family. A simulation study shows that the new estimator is better than others on the basis of particular distributions of response variable. We present optimal properties of the estimators in this research
