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Showing 8 results for Arashi
Mohammad Arashi, Mahammad Mahdi Tabatabaei,
Volume 1, Issue 2 (2-2008)
Abstract
In this paper, we obtain the generalized least square, restricted generalized least square and shrinkage estimators for the regression vector parameter assuming that the errors have multivariate t distribution. Also we calculate their quadratic risks and propose the dominance order of the underlying estimators.
Abdollah Safari, Ali Sharifi, Hamid Pezeshk, Peyman Nickchi, Sayed-Amir Marashi, Changiz Eslahchi,
Volume 6, Issue 2 (2-2013)
Abstract
There are several methods for inference about gene networks, but there are few cases in which the historical information have been considered. In this research we deal with Bayesian inference on gene network. We apply a Bayesian framework to use the available information. Assuming a proper prior distribution and taking the dependency of parameters into account, we seek a model to obtain promising results. We also deal with the hyper parameter estimation. Two methods are considered. The results will be compared by the use of a simulation based on Gibbs sampler. The strengths and weaknesses of each method are briefly mentioned.
Hamid Karamikabir, Mohammad Arashi,
Volume 8, Issue 1 (9-2014)
Abstract
In this paper we consider of location parameter estimation in the multivariate normal distribution with unknown covariance. Two restrictions on the mean vector parameter are imposed. First we assume that all elements of mean vector are nonnegative, at the second hand assumed only a subset of elements are nonnegative. We propose a class of shrinkage estimators which dominate the minimax estimator of mean vector under the quadratic loss function.
Mina Norouzirad, Mohammad Arashi,
Volume 11, Issue 1 (9-2017)
Abstract
Penalized estimators for estimating regression parameters have been considered by many authors for many decades. Penalized regression with rectangular norm is one of the mainly used since it does variable selection and estimating parameters, simultaneously. In this paper, we propose some new estimators by employing uncertain prior information on parameters. Superiority of the proposed shrinkage estimators over the least absoluate and shrinkage operator (LASSO) estimator is demonstrated via a Monte Carlo study. The prediction rate of the proposed estimators compared to the LASSO estimator is also studied in the US State Facts and Figures dataset.
Maryam Borzoei Bidgoli, Mohammad Arashi,
Volume 12, Issue 2 (3-2019)
Abstract
One way of dealing with the problem of collinearity in linear models, is to make use of the Liu estimator. In this paper, a new estimator by generalizing the modified Liu estimator of Li and Yang (2012) has been proposed. This estimator is constructed based on a prior information of vector parameters in linear regression and the generalized estimator of Akdeniz and Kachiranlar (1995). Using the mean square error matrix criterion, we have obtained the superiority conditions Of this newly defined estimator over the generalized Liu estimator. For comparison sake, a numerical example as well as a Monte Carlo simulation study are considered.
Mohammad Kazemi, Davood Shahsavani, Mohammad Arashi,
Volume 12, Issue 2 (3-2019)
Abstract
In this paper, we introduce a two-step procedure, in the context of high dimensional additive models, to identify nonzero linear and nonlinear components. We first develop a sure independence screening procedure based on the distance correlation between predictors and marginal distribution function of the response variable to reduce the dimensionality of the feature space to a moderate scale. Then a double penalization based procedure is applied to identify nonzero and linear components, simultaneously. We conduct extensive simulation experiments and a real data analysis to evaluate the numerical performance of the proposed method.
Mohammmad Arast, Mohammmad Arashi, Mohammmad Reza Rabie,
Volume 13, Issue 1 (9-2019)
Abstract
Often, in high dimensional problems, where the number of variables is large the number of observations, penalized estimators based on shrinkage methods have better efficiency than the OLS estimator from the prediction error viewpoint. In these estimators, the tuning or shrinkage parameter plays a deterministic role in variable selection. The bridge estimator is an estimator which simplifies to ridge or LASSO estimators varying the tuning parameter. In these paper, the shrinkage bridge estimator is derived under a linear constraint on regression coefficients and its consistency is proved. Furthermore, its efficiency is evaluated in a simulation study and a real example.
Mozhgan Taavoni, Mohammad Arashi,
Volume 14, Issue 2 (2-2021)
Abstract
This paper considers the problem of simultaneous variable selection and estimation in a semiparametric mixed-effects model for longitudinal data with normal errors. We approximate the nonparametric function by regression spline and simultaneously estimate and select the variables under the optimization of the penalized objective function. Under some regularity conditions, the asymptotic behaviour of the resulting estimators is established in a high-dimensional framework where the number of parametric covariates increases as the sample size increases. For practical implementation, we use an EM algorithm to selects the significant variables and estimates the nonzero coefficient functions. Simulation studies are carried out to assess the performance of our proposed method, and a real data set is analyzed to illustrate the proposed procedure.
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