• 한국환경보건학회지
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• 제45권2호
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• pp.192-202
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• 2019

Objectives: According to the central limit theorem, the samples in population might be considered to follow normal distribution if a large number of samples are available. Once we assume that toxicity dataset follow normal distribution, we can treat and process data statistically to calculate genus or species mean value with standard deviation. However, little is known and only limited studies are conducted to investigate whether toxicity dataset follows normal distribution or not. Therefore, the purpose of study is to evaluate the generally accepted normality hypothesis of aquatic toxicity dataset Methods: We selected the 8 chemicals, which consist of 4 organic and 4 inorganic chemical compounds considering data availability for the development of species sensitivity distribution. Toxicity data were collected at the US EPA ECOTOX Knowledgebase by simple search with target chemicals. Toxicity data were re-arranged to a proper format based on the endpoint and test duration, where we conducted normality test according to the Shapiro-Wilk test. Also we investigated the degree of normality by simple log transformation of toxicity data Results: Despite of the central limit theorem, only one large dataset (n>25) follow normal distribution out of 25 large dataset. By log transforming, more 7 large dataset show normality. As a result of normality test on small dataset (n<25), log transformation of toxicity value generally increases normality. Both organic and inorganic chemicals show normality growth for 26 species and 30 species, respectively. Those 56 species shows normality growth by log transformation in the taxonomic groups such as amphibian (1), crustacean (21), fish (22), insect (5), rotifer (2), and worm (5). In contrast, mollusca shows normality decrease at 1 species out of 23 that originally show normality. Conclusions: The normality of large toxicity dataset was not always satisfactory to the central limit theorem. Normality of those data could be improved through log transformation. Therefore, care should be taken when using toxicity data to induce, for example, mean value for risk assessment.

• 지능정보연구
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• 제28권2호
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• pp.19-31
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• 2022

기계학습 모델 구축 간 트레이닝 데이터를 활용하며, 훈련 간 사용되지 않은 테스트 데이터를 활용하여 모델의 정확도와 일반화 성능을 판단한다. 일반화 성능이 낮은 모델의 경우 새롭게 받아들이게 되는 데이터에 대한 예측 정확도가 현저히 감소하게 되며 이러한 현상을 두고 모델이 과적합 되었다고 한다. 본 연구는 중심극한정리를 기반으로 데이터를 생성 및 기존의 훈련용 데이터와 결합하여 새로운 훈련용 데이터를 구성하고 데이터의 정규성을 증가시킴과 동시에 이를 활용하여 모델의 일반화 성능을 증가시키는 방법에 대한 것이다. 이를 위해 중심극한정리의 성질을 활용해 데이터의 각 특성별로 표본평균 및 표준편차를 활용하여 데이터를 생성하였고, 새로운 훈련용 데이터의 정규성 증가 정도를 파악하기 위하여 Kolmogorov-Smirnov 정규성 검정을 진행한 결과, 새로운 훈련용 데이터가 기존의 데이터에 비해 정규성이 증가하였음을 확인할 수 있었다. 일반화 성능은 훈련용 데이터와 테스트용 데이터에 대한 예측 정확도의 차이를 통해 측정하였다. 새롭게 생성된 데이터를 K-Nearest Neighbors(KNN), Logistic Regression, Linear Discriminant Analysis(LDA)에 적용하여 훈련시키고 일반화 성능 증가정도를 파악한 결과, 비모수(non-parametric) 기법인 KNN과 모델 구성 간 정규성을 가정으로 갖는 LDA의 경우에 대하여 일반화 성능이 향상되었음을 확인할 수 있었다.

• 한국수학교육학회지시리즈B:순수및응용수학
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• 제11권2호
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• pp.139-147
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• 2004

Let {<$\mathds{X}_t$} be an m-dimensional linear process of the form $\mathbb{X}_t\;=\sumA,\mathbb{Z}_{t-j}$ where {$\mathbb{Z}_t$} is a sequence of stationary m-dimensional negatively associated random vectors with $\mathbb{EZ}_t$ = $\mathbb{O}$ and $\mathbb{E}\parallel\mathbb{Z}_t\parallel^2$ < $\infty$. In this paper we prove the central limit theorems for multivariate linear processes generated by negatively associated random vectors.

• 대한수학회지
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• 제61권4호
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• pp.713-742
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• 2024

From the ordinary notion of upper-tail quantitle function, a new concept called conditionally upper-tail quantitle function given a σ-algebra is proposed. Some basic properties of this terminology and further properties of conditionally strictly stationary sequences are derived. By means of these properties, several conditional central limit theorems for a sequence of conditionally strong mixing and conditionally strictly stationary random variables are established, some of which are the conditional versions corresponding to earlier results under non-conditional case.

• Geomechanics and Engineering
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• 제6권5호
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• pp.503-515
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• 2014

On the basis of Hoek-Brown failure criterion, a numerical solution for the shape of collapsing block in the rectangular cavity subjected to seepage forces is obtained by upper bound theorem of limit analysis. The seepage forces obtained from the gradient of excess pore pressure distribution are taken as external loadings in the limit analysis, and the pore pressure is easily calculated with pore pressure coefficient. Thus the seepage force is incorporated into the upper bound analysis as a work rate of external force. The upper solution of the shape of collapsing block is derived by virtue of variational calculation. In order to verify the validity of the method proposed in the paper, the result when the pore pressure coefficient equals zero, and only hydrostatic pressure is taken into consideration, is compared with that of previous work. The results show good effectiveness in calculating the collapsing block shape subjected to seepage forces. The influence of parameters on the failure mechanisms is investigated.

• Communications for Statistical Applications and Methods
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• 제6권1호
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• pp.267-274
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• 1999

We consider a doubly stochastically perturbed dynamical system {$X_n$} generated by $X_n\Gamma_n(X_{n-1})+W_n where \Gamma_n$ is a Markov chain of random functions and $W_n$ is i.i.d. random elements. Sufficient conditions for stationarity and geometric ergodicity of $X_n$ are obtained by considering asymptotic behaviours of the associated Markov chain. Ergodic theorem and functional central limit theorem are proved.

• Journal of the Korean Data and Information Science Society
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• 제9권2호
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• pp.113-118
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• 1998

In this paper we study central limit theorem(C.L.T.) and law of iterated logarithm (L.I.L.) for fuzzy random variables with respect to Hausdorff distance.

• Communications for Statistical Applications and Methods
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• 제3권3호
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• pp.59-74
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• 1996

The properties of the residuals are investigated when K-consecutive observations are interpolated. The central limit theorem is also proved for the LSE for autoregressive parameters when $\kappa4--consecutive observations are contaminated. The performance of the interpolated LSE in small samples is investigated by simulation. And the interpolated with the Yule-Walker type estimator.

• 대한수학회논문집
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• 제33권2호
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• pp.605-618
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• 2018

The main goal of this paper is to study an extension of random summations of independent and identically distributed random variables when the number of summands in random summation is a partial sum of n independent, identically distributed, non-negative integer-valued random variables. Some characterizations of random summations are considered. The central limit theorems and weak law of large numbers for extended random summations are established. Some weak limit theorems related to geometric random sums, binomial random sums and negative-binomial random sums are also investigated as asymptotic behaviors of extended random summations.

• 대한수학회지
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• 제33권4호
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• pp.983-992
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• 1996

Let p(x, dy) be a transition probability function on $(S, \rho)$, where S is a complete separable metric space. Then a Markov process $X_n$ which has p(x, dy) as its transition probability may be generated by random iterations of the form $X_{n+1} = f(X_n, \varepsilon_{n+1})$, where $\varepsilon_n$ is a sequence of independent and identically distributed random variables (See, e.g., Kifer(1986), Bhattacharya and Waymire(1990)).