Yazar "Asar, Yasin" seçeneğine göre listele

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    IMPROVING ESTIMATIONS IN QUANTILE REGRESSION MODEL WITH AUTOREGRESSIVE ERRORS
    (Vinca Inst Nuclear Sci, 2018) Yuzbasi, Bahadir; Asar, Yasin; Sik, M. Samil; Demiralp, Ahmet
    An important issue is that the respiratory mortality may be a result of air pollution which can be measured by the following variables: temperature, relative humidity, carbon monoxide, sulfur dioxide, nitrogen dioxide, hydrocarbons, ozone, and particulates. The usual way is to fit a model using the ordinary least squares regression, which has some assumptions, also known as Gauss-Markov assumptions, on the error term showing white noise process of the regression model. However, in many applications, especially for this example, these assumptions are not satisfied. Therefore, in this study, a quantile regression approach is used to model the respiratory mortality using the mentioned explanatory variables. Moreover, improved estimation techniques such as preliminary testing and shrinkage strategies are also obtained when the errors are autoregressive. A Monte Carlo simulation experiment, including the quantile penalty estimators such as lasso, ridge, and elastic net, is designed to evaluate the performances of the proposed techniques. Finally, the theoretical risks of the listed estimators are given.
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    L1 Correlation-Based Penalty in High-Dimensional Quantile Regression
    (Ieee, 2018) Yuzbasi, Bahadir; Ahmed, S. Ejaz; Asar, Yasin
    In this study, we propose a new method called L1 norm correlation based estimation in quantile regression in high-dimensional sparse models where the number of explanatory variables is large, may be larger than the number of observations, however, only some small subset of the predictive variables are important in explaining the dependent variable. Therefore, the importance of new method is that it addresses both grouping affect and variable selection. Monte Carlo simulations confirm that the new method compares well to the other existing regularization methods.
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    Liu-type shrinkage estimations in linear models
    (Taylor & Francis Ltd, 2022) Yuzbasi, Bahadir; Asar, Yasin; Ahmed, S. Ejaz
    In this study, we present the preliminary test, Stein-type and positive part Stein-type Liu estimators in the linear models when the parameter vector beta is partitioned into two parts, namely, the main effects beta(1) and the nuisance effects beta(2) such that beta = (beta(1), beta(2)). We consider the case that a priori known or suspected set of the explanatory variables do not contribute to predict the response so that a sub-model maybe enough for this purpose. Thus, the main interest is to estimate beta(1) when beta(2) is close to zero. Therefore, we investigate the performance of the suggested estimators asymptotically and via a Monte Carlo simulation study. Moreover, we present a real data example to evaluate the relative efficiency of the suggested estimators, where we demonstrate the superiority of the proposed estimators.
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    SLASSO: a scaled LASSO for multicollinear situations
    (Taylor & Francis Ltd, 2021) Arashi, Mohammad; Asar, Yasin; Yuzbasi, Bahadir
    We propose a re-scaled LASSO by pre-multiplying the LASSO with a matrix term, namely, scaled LASSO (SLASSO), for multicollinear situations. Our numerical study has shown that the SLASSO is comparable with other sparse modeling techniques and often outperforms the LASSO and elastic net. Our findings open new visions about using the LASSO still for sparse modeling and variable selection. We conclude our study by pointing that the same efficient algorithm can solve the SLASSO for solving the LASSO and suggest following the same construction technique for other penalized estimators