• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.217-234
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• 2018

Copulas are a tool for constructing multivariate distributions and formalizing the dependence structure between random variables. From copula literature review, there are a few asymmetric copulas available so far while data collected from the real world often exhibit asymmetric nature. This necessitates developing asymmetric copulas. In this study, we discuss a method to construct a new class of bivariate asymmetric copulas based on products of symmetric (sometimes asymmetric) copulas with powered arguments in order to determine if the proposed construction can offer an added value for modeling asymmetric bivariate data. With these newly constructed copulas, we investigate dependence properties and measure of association between random variables. In addition, the test of symmetry of data and the estimation of hyper-parameters by the maximum likelihood method are discussed. With two real example such as car rental data and economic indicators data, we perform the goodness-of-fit test of our proposed asymmetric copulas. For these data, some of the proposed models turned out to be successful whereas the existing copulas were mostly unsuccessful. The method of presented here can be useful in fields such as finance, climate and social science.

• Journal of the Korean Data and Information Science Society
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• v.23 no.4
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• pp.797-805
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• 2012

Many circular distributions are known to be not only asymmetric but also bimodal. Hidden truncation method of generating asymmetric distribution is applied to a bivariate circular distribution to generate an asymmetric circular distribution. While many other existing asymmetric circular distributions can only model an asymmetric data, this new circular model has great flexibility in terms of asymmetry and bi-modality. Some properties of the new model, such as the trigonometric moment generating function, and asymptotic inference about the truncation parameter are presented. Simulation and real data examples are provided at the end to demonstrate the utility of the novel distribution.

• International journal of advanced smart convergence
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• v.9 no.4
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• pp.34-41
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• 2020

Nowadays, the advanced smart convergences of the artificial intelligence (AI) and the internet of things (IoT) have been more and more important, in the fifth generation (5G) and beyond 5G (B5G) mobile communication. In 5G and B5G mobile networks, non-orthogonal multiple access (NOMA) has been extensively investigated as one of the most promising multiple access (MA) technologies. In this paper, we investigate the achievable data rate for the asymmetric binary pulse amplitude modulation (2PAM), in non-orthogonal multiple access (NOMA). First, we derive the closed-form expression for the achievable data rate of the asymmetric 2PAM NOMA. Then it is shown that the achievable data rate of the asymmetric 2PAM NOMA reduces for the stronger channel user over the entire range of power allocation, whereas the achievable data rate of the asymmetric 2PAM NOMA increases for the weaker channel user improves over the power allocation range less than 50%. We also show that the sum rate of the asymmetric 2PAM NOMA is larger than that of the conventional standard 2PAM NOMA, over the power allocation range larger than 25%. In result, the asymmetric 2PAM could be a promising modulation scheme for NOMA of 5G systems, with the proper power allocation.

• The Journal of Information Systems
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• v.15 no.4
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• pp.211-224
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• 2006

This study investigates the application of data mining techniques such as artificial neural networks, rough sets, and induction teaming to the intrusion detection systems. To maximize the effectiveness of data mining for intrusion detection systems, we introduced the asymmetric costs with false positive errors and false negative errors. And we present a method for intrusion detection systems to utilize the asymmetric costs of errors in data mining. The results of our empirical experiment show our intrusion detection model provides high accuracy in intrusion detection. In addition the approach using the asymmetric costs of errors in rough sets and neural networks is effective according to the change of threshold value. We found the threshold has most important role of intrusion detection model for decreasing the costs, which result from false negative errors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3231-3244
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• 2016

As processor design has been transiting from homogeneous multicore processor to heterogeneous multicore processor, traditional Amdahl's law cannot meet the new challenges for asymmetric multicore system. In order to further investigate the impact factors related to the Overhead of Data Preparation (ODP) for Asymmetric multicore systems, we evaluate an asymmetric multicore system built with CPU-GPU by measuring the overheads of memory transfer, computing kernel, cache missing and synchronization. This paper demonstrates that decreasing the overhead of data preparation is a promising approach to improve the whole performance of heterogeneous system.

• Communications for Statistical Applications and Methods
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• v.25 no.1
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• pp.43-60
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• 2018

This paper considers the use of classical and Bayesian inference methods to analyze data generated by variables whose natural behavior can be modeled using asymmetric distributions in the presence of left censoring. Our approach used a $L{\grave{e}}vy$ distribution in the presence of left censored data and covariates. This distribution could be a good alternative to model data with asymmetric behavior in many applications as lifetime data for instance, especially in engineering applications and health research, when some observations are large in comparison to other ones and standard distributions commonly used to model asymmetry data like the exponential, Weibull or log-logistic are not appropriate to be fitted by the data. Inferences for the parameters of the proposed model under a classical inference approach are obtained using a maximum likelihood estimators (MLEs) approach and usual asymptotical normality for MLEs based on the Fisher information measure. Under a Bayesian approach, the posterior summaries of interest are obtained using standard Markov chain Monte Carlo simulation methods and available software like SAS. A numerical illustration is presented considering data of thyroglobulin levels present in a group of individuals with differentiated cancer of thyroid.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.47 no.2
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• pp.161-168
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• 2019

This paper proposes a variable data transfer asymmetric TDD(Time Division Duplex) system for small UAV(Unmanned Aerial Vehicle) data link system. In the proposed method, a turbo decoder with a double buffer controller is proposed to apply turbo decoder with long decoding time to asymmetric TDD system. The proposed method achieves variable data transfer rate and maximum data transfer rate. The advantage of the proposed method is demonstrated by its data transfer rate. The measured data transfer rate is more than 1.8 times than that of symmetric TDD system. In addition, PER(Packet Error Rate) performance is the same and data transfer rate is variable.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1033-1043
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• 2011

In this paper, we use a nested family of models of Generalized Autoregressive Conditional Heteroscedasticity(GARCH) to verify asymmetric conditional heteroscedasticity in the KOSPI and Won-Dollar exchange rate. This study starts from an investigation of whether time series data have asymmetric features not explained by standard GARCH models. First, we use kernel density plot to show the non-normality and asymmetry in data as well as to capture asymmetric conditional heteroscedasticity. Later, we use three representative asymmetric heteroscedastic models, EGARCH(Exponential Garch), GJR-GARCH(Glosten, Jagannathan and Runkle), APARCH(Asymmetric Power Arch) that are improved from standard GARCH models to give a better explanation of asymmetry. Thereby we highlight the fact that volatility tends to respond asymmetrically according to positive and/or negative values of past changes referred to as the leverage effect. Furthermore, it is verified that how the direction of asymmetry is different depending on characteristics of time series data. For the KOSPI and Korean won-US dollar exchange rate, asymmetric heteroscedastic model analysis successfully reveal the leverage effect. We obtained predictive values of conditional volatility and its prediction standard errors by using moving block bootstrap.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.999-1005
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• 2011

This paper proposes a weighted least squares support vector machine for asymmetric least squares regression. This method achieves nonlinear prediction power, while making no assumption on the underlying probability distributions. The cross validation function is introduced to choose optimal hyperparameters in the procedure. Experimental results are then presented which indicate the performance of the proposed model.

• Journal of the Korean Statistical Society
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• v.34 no.1
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• pp.61-71
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• 2005

A class of asymmetric ARCH processes is proposed via binary random power transformations. This class accommodates traditional nonlinear models such as threshold ARCH (Rabemanjara and Zacoian (1993)) and Box-Cox type ARCH models(Higgins and Bera (1992)). Stationarity condition of the model is addressed. Iterative least squares(ILS) and pseudo maximum like-lihood(PML) methods are discussed for estimating parameters and related algorithms are presented. Illustrative analysis for Korea Stock Prices Index (KOSPI) data is conducted.