• Communications for Statistical Applications and Methods
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• 제13권2호
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• pp.297-307
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• 2006

Most methods for describing the relationship among random variables require specific probability distributions and some assumptions of random variables. The mutual information based on the entropy to measure the dependency among random variables does not need any specific assumptions. And the redundancy which is a analogous version of the mutual information was also proposed. In this paper, the redundancy and mutual information are explored to multi-dimensional categorical data. It is found that the redundancy for categorical data could be expressed as the function of the generalized likelihood ratio statistic under several kinds of independent log-linear models, so that the redundancy could also be used to analyze contingency tables. Whereas the generalized likelihood ratio statistic to test the goodness-of-fit of the log-linear models is sensitive to the sample size, the redundancy for categorical data does not depend on sample size but its cell probabilities itself.

• International Journal of Reliability and Applications
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• 제9권1호
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• pp.31-52
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• 2008

In this paper generalized version of the geometric distribution is introduced. This distribution can be considered as a two-parameter generalization of the discrete geometric distribution. The main statistical and reliability properties of this distribution are discussed. Two methods of estimation, namely maximum likelihood method and the method of moments are used to estimate the parameters of this distribution. Simulation is utilized to calculate these estimates and to study some of their properties. Also, asymptotic confidence limits are established for the maximum likelihood estimates. Finally, the appropriateness of this new distribution for a set of real data, compared with the geometric distribution, is shown by using the likelihood ratio test and the Kolmogorove-Smirnove test.

• International Journal of Reliability and Applications
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• 제2권4호
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• pp.263-268
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• 2001

Shanmugam(1991) generalized the Poisson distribution to capture a restriction on the incidence rate $\theta$ (i.e. $\theta$ ＜ $\beta$, an unknown upper limit), and named it incidence rate restricted Poisson (IRRP) distribution. Using Neyman's C($\alpha$) concept, Shanmugam then devised a hypothesis testing procedure for $\beta$ when $\theta$ remains unknown nuisance parameter. Shanmugam's C ($\alpha$) based .results involve inverse moments which are not easy tools, This article presents an alternate testing procedure based on likelihood ratio concept. It turns out that likelihood ratio test statistic offers more power than the C($\alpha$) test statistic. Numerical examples are included.

• 한국통신학회논문지
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• 제37A권11호
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• pp.943-951
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• 2012

본 논문에서는 비정규 충격성 잡음 환경에서 협력 무선인지 네트워크를 위한 순서 기반 스펙트럼 센싱 기법을 제안한다. 구체적으로는 잡음을 이변수 등방형 대칭 알파 안정 (bivariate isotropic symmetric ${\alpha}$-stable) 분포를 따르는 것으로 모형화하고, 그에 알맞은 관측 샘플의 순서와 일반화된 우도비 검정 기반 협력 스펙트럼 센싱 기법을 제안한다. 모의실험을 통해 비정규 잡음 환경에서 제안한 기법이 기존의 기법에 비해 더 좋은 스펙트럼 센싱성능을 가짐을 보인다.

• Communications for Statistical Applications and Methods
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• 제2권1호
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• pp.112-121
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• 1995

Let $p_1$ and $p_2$ be the proportions of two populations. To test the hypothesis $H_0 : p_1 = p_2$, we usually use the $x^2$ statistic, the large sample binomial statistic Z, and the Generalized Likelihood Ratio statistic-2log $\lambda$developed based on different mathematical rationale, respectively. Since testing the above hypothesis is equivalent to testing whether two populations follow the common Bernoulli distribution, one may also test the hypothesis by comparing 1 with the ratio of each density estimate and the hypothesized common density estimate, called comparison density, which was devised by Parzen(1988). We show that the above traditional test statistics ate actually estimating the measure of distance between the true densities and the common density under $H_0$ by representing them with the comparison density.

• Journal of the Korean Data and Information Science Society
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• 제17권2호
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• pp.543-557
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• 2006

This paper is concerned with the applicability of the chi-square approximation to the six disparity statistics: the Pearson chi-square, the generalized likelihood ratio, the power divergence, the blended weight chi-square, the blended weight Hellinger distance, and the negative exponential disparity statistic. Three dimensional contingency tables of small and moderate sample sizes are generated to be fitted to all possible hierarchical log-linear models: the completely independent model, the conditionally independent model, the partial association models, and the model with one variable independent of the other two. For models with direct solutions of expected cell counts, point estimates and confidence intervals of the 90 and 95 percentage points of six statistics are explored. For model without direct solutions, the empirical significant levels and the empirical powers of six statistics to test the significance of the three factor interaction are computed and compared.

• 응용통계연구
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• 제16권2호
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• pp.455-467
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• 2003

소표본 분할표 자료에서 적합도 검정통계량들의 카이제곱 근사 적용 가능에 대하여 많은 연구가 진행되었다. 소표본에서 세 가지 검정 통계량(피어슨 카이제곱 Χ$^2$, 일반화 가능도비 G$^2$, 그리고 역발산 Ι(2/3) 검정통계량)에 관하여 비교한 Rudas(1986)의 연구를 확장하여, 최근에 제안된 차이측도(BWHD(1/9), BWCS(1/3), NED(4/3) 검정통계량)를 포함시켜 비교 분석하였다. 독립모형의 이차원 분할표, 조건부 독립모형과 한 변수 독립 모형을 따르는 삼차원 분할표에 대한 모의실험을 통하여 생성된 90과 95 백분위수와 이에 대응하는 95% 신뢰구간을 살펴보고 실제 백분위수와 비교하였다. 그 결과 Χ$^2$, Ι(2/3), 그리고 BWHD(1/9) 검정통계량이 유사한 결과를 나타내었고 이 통계량들이 기존에 제안된 검정통계량들보다 적은 표본크기에서도 카이제곱 근사방법에 적용 가능함을 발견하였다.

• 응용통계연구
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• 제10권1호
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• pp.105-119
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• 1997

일원배열형태의 가산 자료집합에서 각 군의 평균을 이용하여 처리효과를 비교할 수 있다. Barnwal과 Paul(1988)은 각 군의 산포모수가 같다는 가정 아래에서 처리에 따른 차이를 검정하는 우도검정통계량과 $C(\alpha)$ 통계량을 유도하였는데 본 연구에서는 이러한 가정이 만족되지 않아도 검정할 수 있도록 통계량을 일반화하였다. 또한 음이항분포 대신 Efron(1986)의 이중지수계 포아송 모형을 도입하여 새로운 통계량을 제시하였다. 모의실험을 통해 이중지수계 포아송 모형으로부터 유도된 $C(\alpha)$ 통계량이 어느 경우에나 적합함을 밝혔다.

• 전기학회논문지
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• 제63권11호
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• pp.1545-1550
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• 2014

Fault detection and diagnosis (FDD) of photovoltaic (PV) power systems is one of significant techniques for reducing economic loss due to abnormality occurred in PV modules. This paper presents a new FDD method against PV power systems by using statistical comparison. This comparative approach includes deviation signals between the outputs of two neighboring PV modules. We first define a binary hypothesis testing under such deviation and make use of a generalized likelihood ratio testing (GLRT) theory to derive its FDD algorithm. Additionally, a recursive computational mechanism for our proposed FDD algorithm is presented for improving a computational effectiveness in practice. We carry out a real-time experiment to test reliability of the proposed FDD algorithm by utilizing a lab based PV test-bed system.

• Journal of the Korean Data and Information Science Society
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• 제27권6호
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• pp.1661-1671
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• 2016

Risk analysis is a systematic study of uncertainties and risks we encounter in business, engineering, public policy, and many other areas. Value at Risk (VaR) is one of the most widely used risk measurements in risk management. In this paper, the Korean Composite Stock Price Index data has been utilized to model the VaR employing the classical ARMA (1,1)-GARCH (1,1) models with normal, t, generalized hyperbolic, and generalized pareto distributed errors. The aim of this paper is to compare the performance of each model in estimating the VaR. The performance of models were compared in terms of the number of VaR violations and Kupiec exceedance test. The GARCH-GPD likelihood ratio unconditional test statistic has been found to have the smallest value among the models.