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  • Küçük Resim
    Öğe
    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.
  • Küçük Resim
    Öğe
    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