Yazar "Roozbeh, Mahdi" seçeneğine göre listele

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    A new difference-based weighted mixed Liu estimator in partially linear models
    (TAYLOR & FRANCIS LTD, 2018) Akdeniz, Esra; Akdeniz, Fikri; Roozbeh, Mahdi
    In this paper, a generalized difference-based estimator is introduced for the vector parameter beta in the partially linear model when the errors are correlated. A generalized difference-based Liu estimator is defined for the vector parameter beta. Under the linear stochastic constraint r = R beta + e, a new generalized difference-based weighted mixed Liu estimator is introduced. The performance of this estimator over the generalized difference-based weighted mixed estimator and the generalized difference-based Liu estimator in terms of the mean squared error matrix criterion is investigated. Then, a method to select the biasing parameter d and non-stochastic weight. is considered. The efficiency properties of the newestimator are illustrated by a simulation study. Finally, the performance of the new estimator is evaluated for a real data set.
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    Efficiency of the generalized difference-based liu estimators in semiparametric regression models with correlated errors
    (TAYLOR & FRANCIS LTD, 2015) Akdeniz, Fikri; Duran, Esra Akdeniz; Roozbeh, Mahdi; Arashi, Mohammad
    In this paper, a generalized difference-based estimator is introduced for the vector parameter beta in the semiparametric regression model when the errors are correlated. A generalized difference-based Liu estimator is defined for the vector parameter beta in the semiparametric regression model. Under the linear nonstochastic constraint R beta=r, the generalized restricted difference-based Liu estimator is given. The risk function for the beta(GRD)(eta) associated with weighted balanced loss function is presented. The performance of the proposed estimators is evaluated by a simulated data set.
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    Efficiency of the generalized-difference-based weighted mixed almost unbiased two-parameter estimator in partially linear model
    (TAYLOR & FRANCIS INC, 2017) Akdeniz, Fikri; Roozbeh, Mahdi
    In this paper, a generalized difference-based estimator is introduced for the vector parameter in partially linear model when the errors are correlated. A generalized-difference-based almost unbiased two-parameter estimator is defined for the vector parameter . Under the linear stochastic constraint r = R + e, we introduce a new generalized-difference-based weighted mixed almost unbiased two-parameter estimator. The performance of this new estimator over the generalized-difference-based estimator and generalized- difference-based almost unbiased two-parameter estimator in terms of the MSEM criterion is investigated. The efficiency properties of the new estimator is illustrated by a simulation study. Finally, the performance of the new estimator is evaluated for a real dataset.
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    Generalized difference-based weighted mixed almost unbiased liu estimator in semiparametric regression models
    (2020) Akdeniz, Fikri; Roozbeh, Mahdi; Akdeniz, Esra; Khan, Naushad Mamode
    In classical linear regression analysis problems, the ordinary leastsquares (OLS) estimation is the popular method to obtain the regression weights, given the essential assumptions are satisfied. However, often, in real-life studies, the response data and its associated explanatory variables do not meet the required conditions, in particular under multicollinearity, and hence results can be misleading. To overcome such problem, this paper introduces a novel generalized differencebased weighted mixed almost unbiased Liu estimator. The performance of this new estimator is evaluated against the classical estimators using the mean squared error. This is followed by an approach to select the Liu parameter and in this context, a non-stochastic weight is also considered. Monte Carlo simulation experiments are executed to assess the performance of the new estimator and subsequently,we illustrate its application to a real-life data example
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    Generalized difference-based weighted mixed almost unbiased ridge estimator in partially linear models
    (2019) Akdeniz, Fikri; Roozbeh, Mahdi
    In this paper, a generalized difference-based estimator is introduced for the vector parameter β in partially linear model when the errors are correlated. A generalized difference-based almost unbiased ridge estimator is defined for the vector parameter β. Under the linear stochastic constraint r = Rβ + e, a new generalized difference-based weighted mixed almost unbiased ridge estimator is proposed. The performance of this estimator over the generalized difference-based weighted mixed estimator, the generalized difference-based estimator, and the generalized differencebased almost unbiased ridge estimator in terms of the mean square error matrix criterion is investigated. Then, a method to select the biasing parameter k and nonstochastic weight ω is considered. The efficiency properties of the new estimator is illustrated by a simulation study. Finally, the performance of the new estimator is evaluated for a real dataset