Yazar "Wu, Jibo" seçeneğine göre listele

Listeleniyor 1 - 11 / 11
  • Küçük Resim Yok
    Öğe
    EFFICIENCY OF THE PRINCIPAL COMPONENT LIU-TYPE ESTIMATOR IN LOGISTIC REGRESSION
    (Inst Nacional Estatistica-Ine, 2020) Wu, Jibo; Asar, Yasin
    In this paper we propose a principal component Liu-type logistic estimator by combining the principal component logistic regression estimator and Liu-type logistic estimator to overcome the multicollinearity problem. The superiority of the new estimator over some related estimators are studied under the asymptotic mean squared error matrix. A Monte Carlo simulation experiment is designed to compare the performances of the estimators using mean squared error criterion. Finally, a conclusion section is presented.
  • Küçük Resim Yok
    Öğe
    An improved and efficient biased estimation technique in logistic regression model
    (Taylor & Francis Inc, 2020) Asar, Yasin; Wu, Jibo
    In this article, we propose a new improved and efficient biased estimation method which is a modified restricted Liu-type estimator satisfying some sub-space linear restrictions in the binary logistic regression model. We study the properties of the new estimator under the mean squared error matrix criterion and our results show that under certain conditions the new estimator is superior to some other estimators. Moreover, a Monte Carlo simulation study is conducted to show the performance of the new estimator in the simulated mean squared error and predictive median squared errors sense. Finally, a real application is considered.
  • Küçük Resim Yok
    Öğe
    More on the restricted Liu estimator in the logistic regression model
    (Taylor & Francis Inc, 2017) Wu, Jibo; Asar, Yasin
    Siray etal. proposed a restricted Liu estimator to overcome multicollinearity in the logistic regression model. They also used a Monte Carlo simulation to study the properties of the restricted Liu estimator. However, they did not present the theoretical result about the mean squared error properties of the restricted estimator compared to MLE, restricted maximum likelihood estimator (RMLE) and Liu estimator. In this article, we compare the restricted Liu estimator with MLE, RMLE and Liu estimator in the mean squared error sense and we also present a method to choose a biasing parameter. Finally, a real data example and a Monte Carlo simulation are conducted to illustrate the benefits of the restricted Liu estimator.
  • Küçük Resim Yok
    Öğe
    On almost unbiased ridge logistic estimator for the logistic regression model
    (Hacettepe Univ, Fac Sci, 2016) Wu, Jibo; Asar, Yasin
    Schaefer et al. (15) proposed a ridge logistic estimator in logistic regression model. In this paper a new estimator based on the ridge logistic estimator is introduced in logistic regression model and we call it as almost unbiased ridge logistic estimator. The performance of the new estimator over the ridge logistic estimator and the maximum likelihood estimator in scalar mean squared error criterion is investigated. We also present a numerical example and a simulation study to illustrate the theoretical results
  • Küçük Resim Yok
    Öğe
    On the restricted almost unbiased Liu estimator in the logistic regression model
    (Taylor & Francis Inc, 2018) Wu, Jibo; Asar, Yasin; Arashi, Mohammad
    It is known that when the multicollinearity exists in the logistic regression model, variance of maximum likelihood estimator is unstable. As a remedy, in the context of biased shrinkage Liu estimation, Chang introduced an almost unbiased Liu estimator in the logistic regression model. Making use of his approach, when some prior knowledge in the form of linear restrictions are also available, we introduce a restricted almost unbiased Liu estimator in the logistic regression model. Statistical properties of this newly defined estimator are derived and some comparison results are also provided in the form of theorems. A Monte Carlo simulation study along with a real data example are given to investigate the performance of this estimator.
  • Küçük Resim Yok
    Öğe
    On the stochastic restricted Liu estimator in logistic regression model
    (Taylor & Francis Ltd, 2020) Li, Yong; Asar, Yasin; Wu, Jibo
    In this paper, we study the effects of near-singularity which is known as multicollinearity in the binary logistic regression. Furthermore, we also assume the presence of stochastic non-sample linear restrictions. The well-known logistic Liu estimator is combined with the stochastic linear restrictions in order to propose a new method, namely, the stochastic restricted Liu estimation. Theoretical comparisons between the usual maximum likelihood estimator, Liu estimator, stochastic restricted maximum-likelihood estimator and the new stochastic restricted Liu estimator are derived using matrix mean-squared errors of the estimators. A Monte Carlo simulation experiment is designed to evaluate the performances of the listed estimators in terms of mean-squared error and mean absolute error criteria. Artificial data are used to show how to interpret the theorems. According to the results of the simulation, the new method beats the other estimators when the data matrix has the problem of collinearity along with the stochastic restrictions.
  • Küçük Resim Yok
    Öğe
    ON THE STOCHASTIC RESTRICTED LIU-TYPE MAXIMUM LIKELIHOOD ESTIMATOR IN LOGISTIC REGRESSION MODEL
    (Ankara Univ, Fac Sci, 2019) Wu, Jibo; Asar, Yasin
    In order to overcome multicollinearity, we propose a stochastic restricted Liu-type maximum likelihood estimator by incorporating Liu-type maximum likelihood estimator to the logistic regression model when the linear restrictions are stochastic. We also discuss the properties of the new estimator. Moreover, we give a method to choose the biasing parameter in the new estimator. Finally, a simulation study is given to show the performance of the new estimator.
  • Küçük Resim Yok
    Öğe
    Performance of the almost unbiased ridge-type principal component estimator in logistic regression model
    (Taylor & Francis Inc, 2018) Wu, Jibo; Asar, Yasin
    This article considers some different parameter estimation methods in logistic regression model. In order to overcome multicollinearity, the almost unbiased ridge-type principal component estimator is proposed. The scalar mean squared error of the proposed estimator is derived and its properties are investigated. Finally, a numerical example and a simulation study are presented to show the performance of the proposed estimator.
  • Küçük Resim Yok
    Öğe
    Restricted ridge estimator in the logistic regression model
    (Taylor & Francis Inc, 2017) Asar, Yasin; Arashi, Mohammad; Wu, Jibo
    It is known that when the multicollinearity exists in the logistic regression model, variance of maximum likelihood estimator is unstable. As a remedy, Schaefer et al. presented a ridge estimator in the logistic regression model. Making use of the ridge estimator, when some linear restrictions are also present, we introduce a restricted ridge estimator in the logistic regression model. Statistical properties of this newly defined estimator will be studied and comparisons are done in the simulation study in the sense of mean squared error criterion. A real-data example and a simulation study are introduced to discuss the performance of this estimator.
  • Küçük Resim Yok
    Öğe
    A two-parameter estimator in linear measurement error model
    (Taylor & Francis Ltd, 2022) Wu, Jibo; Asar, Yasin
    This article is concerned with the parameter estimation in linear measurement error model when there is ill-conditioned data. To deal with the multicollinearity problem, a new two-parameter estimator is proposed. The asymptotic properties of the new estimator are considered using the mean squared error matrix. Finally, a Monte Carlo simulation is presented to show the performances of the estimators in terms of simulated mean squared error criteria. According to the results, the new estimator can be suggested as an alternative to the other existing estimators in the presence of ill-conditioned data.
  • Küçük Resim Yok
    Öğe
    A weighted stochastic restricted ridge estimator in partially linear model
    (Taylor & Francis Inc, 2017) Wu, Jibo; Asar, Yasin
    In this article, we consider the estimation of a partially linear model when stochastic linear restrictions on the parameter components are assumed to hold. Based on the weighted mixed estimator, profile least-squares method, and ridge method, a weighted stochastic restricted ridge estimator of the parametric component is introduced. The properties of the new estimator are also discussed. Finally, a simulation study is given to show the performance of the new estimator.