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    Double shrunken selection operator
    (Taylor & Francis Inc, 2019) Yuzbasi, B.; Arashi, M.
    The least absolute shrinkage and selection operator (LASSO) is a prominent estimator which selects significant (under some sense) features and kills insignificant ones. Indeed the LASSO shrinks features larger than a noise level to zero. In this article, we force LASSO to be shrunken more by proposing a Stein-type shrinkage estimator emanating from the LASSO, namely the Stein-type LASSO. The newly proposed estimator proposes good performance in risk sense numerically. Variants of this estimator have smaller relative MSE and prediction error, compared to the LASSO, in the analysis of prostate cancer dataset.
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    On Distributions of Discrete Order Statistics
    (Eudoxus Press, Llc, 2016) Bulut, Y.; Gungor, M.; Yuzbasi, B.; Ozbey, F.; Canpolat, E.
    In this study, the joint distributions of order statistics of innid discrete random variables are expressed in the form of an integral. Then, the results related to pf and df are given.
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    On joint distributions of order statistics for a discrete case
    (Springer International Publishing Ag, 2012) Gungor, M.; Bulut, Y.; Yuzbasi, B.
    In this study, the joint distributions of order statistics of innid discrete random variables are expressed. Also, the joint distributions are obtained in the form of an integral. Then, the results related to pf and df are given.
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    On Joint Distributions of Order Statistics from Nonidentically Distributed Discrete Vectors
    (Eudoxus Press, Llc, 2013) Yuzbasi, B.; Gungor, M.
    In this study, the joint distributions of order statistics arising from innid discrete random vectors are expressed in the form of an integral. Then, the results related to pf and df are given.