• Communications for Statistical Applications and Methods
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• 제18권2호
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• pp.245-255
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• 2011

자료의 분포변화를 검정하는 비모수적 방법으로 경험분포함수를 이용하거나 확률밀도함수 추정량을 이용하는 두 가지 방법을 고려할 수 있다. 이 논문에서는 분포변화 검정을 위한 두가지 방법을 자세히 살펴보고 기존 연구의 결과를 정리한다. 여러 확률모형을 가정하고 분포변화 검정에 대한 모의 실험을 실시하여 두 방법에 대한 이론적 극한 성질이잘 성립하는가를 살펴본다. 검정력 비교를 통하여 모형에 따른 적절한 변화점 분석 방법을 알아본다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권1호
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• pp.1-3
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• 2009

Recently, Carlsson, Full\acute{e}$r and Majlender [1] presented the concept of possibilitic correlation representing an average degree of interaction between marginal distribution of a joint possibility distribution as compared to their respective dispersions. They also formulated the weak and strong forms of the possibilistic Cauchy-Schwarz inequality. In this paper, we define a new probability measure. Then the weak and strong forms of the Cauchy-Schwarz inequality are immediate consequence of probabilistic Cauchy-Schwarz inequality with respect to the new probability measure.

• Genomics & Informatics
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• 제20권2호
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• pp.17.1-17.11
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• 2022

Genetic associations have been quantified using a number of statistical measures. Entropy-based mutual information may be one of the more direct ways of estimating the association, in the sense that it does not depend on the parametrization. For this purpose, both the entropy and conditional entropy of the phenotype distribution should be obtained. Quantitative traits, however, do not usually allow an exact evaluation of entropy. The estimation of entropy needs a probability density function, which can be approximated by kernel density estimation. We have investigated the proper sequence of procedures for combining the kernel density estimation and entropy estimation with a probability density function in order to calculate mutual information. Genotypes and their interactions were constructed to set the conditions for conditional entropy. Extensive simulation data created using three types of generating functions were analyzed using two different kernels as well as two types of multifactor dimensionality reduction and another probability density approximation method called m-spacing. The statistical power in terms of correct detection rates was compared. Using kernels was found to be most useful when the trait distributions were more complex than simple normal or gamma distributions. A full-scale genomic dataset was explored to identify associations using the 2-h oral glucose tolerance test results and γ-glutamyl transpeptidase levels as phenotypes. Clearly distinguishable single-nucleotide polymorphisms (SNPs) and interacting SNP pairs associated with these phenotypes were found and listed with empirical p-values.

• International Journal of Reliability and Applications
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• 제4권3호
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• pp.97-111
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• 2003

By applying Theorem 2.6.4 (Fang and Zhang, 1990, p.66) the dispersion matrix of a multivariate power exponential (MPE) distribution is derived. It is shown that the MPE and the gamma distributions are related and thus the MPE and chi-square distributions are related. By extending Fang and Xu's Theorem (1987) from the normal distribution to the Univariate Power Exponential (UPE) distribution an explicit expression is derived for calculating the probability of an UPE random variable over an interval. A representation of the characteristic function (c.f.) for an UPE distribution is given. Based on the MPE distribution the probability density functions of the generalized non-central chi-square, the generalized non-central t, and the generalized non-central F distributions are derived.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 추계학술대회논문집
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• pp.47-55
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• 2003

This paper Presents active control algorithm using probability density function of structural energy. It is assumed that the structural energy under excitation has Rayleigh probability distribution. This assumption is based on the fact that Rayleigh distribution satisfies the condition that the structural energy is always positive and the occurrence probability of minimum energy is zero. The magnitude of control force is determined by the probability that the structural energy exceeds the specified target critical energy, and the sign of control force is determined by Lyapunov controller design method. Proposed control algorithm shows much reduction of peak responses under seismic excitation compared to LQR controller, and it can consider control force limit in the controller design. Also, chattering problem which sometimes occurs in Lyapunov controller can be avoided.

• 한국태양에너지학회 논문집
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• 제33권2호
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• pp.18-27
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• 2013

This paper presents a method for assessing the wind energy potential at complex terrain using probability distribution. And the proper probability models of the parameters estimating the wind energy are presented. Finally a mixture-Weibull determined by numerical methods procedure are proposed to assess the probability distribution of the energy potential at a site. The developed method is applied to the Kwanjungchun Bridge and compared with wind records which the neighboring weather station.

• Journal of Information Processing Systems
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• 제15권2호
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• pp.331-343
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• 2019

Dynamic thermal rating of the overhead transmission lines is affected by many uncertain factors. The ambient temperature, wind speed and wind direction are the main sources of uncertainty. Measurement uncertainty is an important parameter to evaluate the reliability of measurement results. This paper presents the uncertainty analysis based on Monte Carlo. On the basis of establishing the mathematical model and setting the probability density function of the input parameter value, the probability density function of the output value is determined by probability distribution random sampling. Through the calculation and analysis of the transient thermal balance equation and the steady- state thermal balance equation, the steady-state current carrying capacity, the transient current carrying capacity, the standard uncertainty and the probability distribution of the minimum and maximum values of the conductor under 95% confidence interval are obtained. The simulation results indicate that Monte Carlo method can decrease the computational complexity, speed up the calculation, and increase the validity and reliability of the uncertainty evaluation.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 춘계학술대회논문집
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• pp.1493-1510
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• 2000

Theoretical models that can be used to predict the range of main lobe widths and the probability distribution of the peak sidelobe levels of two-dimensionally sparse arrays are presented here. The arrays are considered to comprise microphones that are randomly positioned on a segmented grid of a given size. First, approximate expressions for the expected squared magnitude of the aperture smoothing function and the variance of the squared magnitude of the aperture smoothing function about this mean are formulated for the random arrays considered in the present study. By using the variance function, the mean value and the lower end of the range i.e., the first I percent of the mainlobe distribution can be predicted with reasonable accuracy. To predict the probability distribution of the peak sidelobe levels, distributions of levels are modeled by a Weibull distribution at each peak in the sidelobe region of the expected squared magnitude of the aperture smoothing function. The two parameters of the Weibull distribution are estimated from the means and variances of the levels at the corresponding locations. Next, the probability distribution of the peak sidelobe levels are assumed to be determined by a procedure in which the peak sidelobe level is determined as the maximum among a finite number of independent random sidelobe levels. It is found that the model obtained from the above approach predicts the probability density function of the peak sidelobe level distribution reasonably well for the various combinations of two different numbers of microphones and grid sizes tested in the present study. The application of these models to the design of random, sparse arrays having specified performance levels is also discussed.

• 한국측량학회지
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• 제30권3호
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• pp.295-303
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• 2012

풍력발전 단지의 수익성 평가를 위해 연간 에너지 생산량(AEP ; Annual Energy Production)의 계산이 중요하다. AEP를 계산하기 위해서는 바람의 확률밀도함수(PDF ; Probability Density Function)와 풍력발전기의 발전곡선(PC; Power Curve)이 필요하며, AEP 예측의 정확성을 향상시키기 위해서는 허브 높이에서의 PDF예측과 그 높이의 공기밀도에 따른 풍력발전기 PC의 결정이 중요하다. 본 연구에서는 제주도 한동, 평대의 실관측 풍황탑(met mast) 자료를 이용하였으며 풍속의 PDF를 Weibull 분포 함수로 가정 하였고 Weibull 함수의 파라미터의 값이 높이에 따라 변화하는 양상을 확인하였다. Weibul 함수의 계산은 모멘트법과 LN-least법을 사용하였으며, 모멘트법과 LN-least법에 의한 형상계수의 경우 높이의 증가에 따라 변화를 보이지 않았고 평균값에서 ${\pm}0.1$의 변화 패턴을 보였다. 척도계수의 경우 높이가 증가함에 따라 선형적으로 증가하였으며 지형별 분류에 따른 높이별 척도계수의 기울기는 확연한 차이를 보이고 있었다. 60m 높이에서 관측된 바람의 상대도수와 관측 값의 높이 보정에 의한 공기밀도와 일반식에 의한 공기밀도를 각각 계산하여 그 결과에 대응하는PC를 선택하여 AEP차이를 계산하였다.

• 충청수학회지
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• 제22권1호
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• pp.1-5
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• 2009

Let {$X_n,\;n{\geq}1}$ be a sequence of independent identically distributed(i.i.d.) sequence of positive random variables with common absolutely continuous distribution function(cdf) F(x) and probability density function(pdf) f(x) and $E(X^2)<{\infty}$. The random variables $\frac{X_i{\cdot}X_j}{(\Sigma^n_{k=1}X_k)^{2}}$ and $\Sigma^n_{k=1}X_k$ are independent for $1{\leq}i if and only if {$X_n,\;n{\geq}1}$ have gamma distribution.