This paper investigates the impacts of Central Bank Indicators on Bitcoin/TL prices as a Commodity by using ARDL Bounds Test. In article, monthly data between 2017:09 – 2019:12 is used. The are explained M2 money supply, one-month interest rates bank deposits, one-week repo rate, 10-year government bond. paper, Bitcoin's considered in TL. stationary behaviour variables investigated ADF test and...

Bu çalışmada, Türkiye için cari denge ile büyüme, para arzı ve reel döviz kuru değişkenleri arasındaki ilişki, 2003Q1-2020Q4 dönemi Gecikmesi Dağıtılmış Otoregressif Sınır Testi (ARDL) Granger Nedensellik kullanılarak analiz edilmiştir. Çalışmada bağımlı değişken olarak enerji hariç (EHCD) değişkeni kullanılmıştır. Analiz sonuçlarına göre, büyüme (BUY) (RDK) EHCD arasında %5 anlamlılık seviyesi...

Information divergence functions play a critical role in statistics and information theory. In this paper we show that a non-parametric f -divergence measure can be used to provide improved bounds on the minimum binary classification probability of error for the case when the training and test data are drawn from the same distribution and for the case where there exists some mismatch between tr...

We present an Extended Quadratic Frobenius Primality Test (EQFT), which is related to the Miller-Rabin test and the Quadratic Frobenius test (QFT) by Grantham. EQFT is well-suited for generating large, random prime numbers since on a random input number, it takes time about equivalent to 2 Miller-Rabin tests, but has much smaller error probability. EQFT extends QFT by verifying additional algeb...

we propose to use a mathematical method based on stochastic comparisons of markov chains in order to derive performance indice bounds‎. ‎the main goal of this paper is to investigate various monotonicity properties of a single server retrial queue with first-come-first-served (fcfs) orbit and general retrial times using the stochastic ordering techniques‎.

We address the problem of determination of the size of the test set which can can guarantee statistically significant results in classifier error estimation and in selection of the best classifier from a given set. We focus on the case of the 0-1 valued loss function and we provide one and two sides optimal bounds for Validation (known also as Hold-Out Estimate and Train-and-Test Method). We al...