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.
mozhgan taavoni
Abstract
This paper considers an extension of the linear mixed model, called semiparametric mixed effects model, for longitudinal data, when multicollinearity is present. To overcome this problem, a new mixed ridge estimator is proposed while the nonparametric function in the semiparametric model is approximated ...
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This paper considers an extension of the linear mixed model, called semiparametric mixed effects model, for longitudinal data, when multicollinearity is present. To overcome this problem, a new mixed ridge estimator is proposed while the nonparametric function in the semiparametric model is approximated by the kernel method. The proposed approache integrates ridge method into the semiparametric mixed effects modeling framework in order to account for both the correlation induced by repeatedly measuring an outcome on each individual over time, as well as the potentially high degree of correlation among possible predictor variables. The asymptotic normality of the exhibited estimator is established. To improve efficiency, the estimation of the covariance function is accomplished using an iterative algorithm. Performance of the proposed estimator is compared through a simulation study and analysis of CD4 data.
