• 대한수학회보
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• 제48권5호
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• pp.1041-1046
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• 2011

Let {X, $X_{i};\;i{\geq}1$} be a sequence of independent and identically distributed positive random variables. Denote $S_n= \sum\array\\_{i=1}^nX_i$ and $S\array\\_n^{(k)}=S_n-X_k$ for n ${\geq}$1, $1{\leq}k{\leq}n$. Under the assumption of the finiteness of the second moments, we derive a type of the law of the iterated logarithm for $S\array\\_n^{(k)}$ and the limit point set for its certain normalization.

• 한국경영과학회지
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• 제27권3호
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• pp.59-74
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• 2002

This study investigates two basic operations of mobility management of PCNs (Personal Communication Networks), i.e., the location update and the paging of the mobile terminal. From the realistic consideration that a user either moves through several cells consecutively or stays in a cell with long time, we model the mobility pattern by introducing two types of CRT (Cell Residence Time). Mobility patterns of the mobile terminal are classified Into various ways by using the ratios of two types of CRT. Cost analysis is performed for distance-based and movement-based location update schemes combined with blanket polling paging and selective paging scheme. It is demonstrated that in a certain condition of mobility pattern and call arrival pattern, 2-state CRT model produces different optimal threshold and so, is more effective than IID ( Independently-Identically-Distributed) CRT model. An analytical model for the new CRT model is compact and easily extendable to the other location update schemes.

• 대한수학회보
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• 제43권3호
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• pp.479-486
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• 2006

Let $Z_n(s,\;f)=n^{-1}\;{\sum}^{ns}_{i=1}(f(X_i)-Pf)$ be the sequential empirical process based on the independent and identically distributed random variables. We prove that convergence problems of $sup_{(s,\;f)}|Z_n(s,\;f)|$ to zero boil down to those of $sup_f|Z_n(1,\;f)|$. We employ Ottaviani's inequality and the complete convergence to establish, under bracketing entropy with the second moment, the almost sure convergence of $sup_{(s,\;f)}|Z_n(s,\;f)|$ to zero.

• Communications for Statistical Applications and Methods
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• 제12권1호
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• pp.19-27
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• 2005

The present paper obtains Bayes estimators for the probability p(Y

• Communications for Statistical Applications and Methods
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• 제6권2호
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• pp.585-594
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• 1999

Let {$X_{nj}$, 1$\leq$j$\leq$n,j$\geq$1} be a triangular array of random variables which are neither independent nor identically distributed. The almost sure convergences of randomly weighted partial sums of the form $$\sum_n^{j=1}$$ $W_{nj}$$X_{nj} are studied where {Wnj 1$\leq$j$\leq$n, j$\geq$1} is a triangular array of random weights. Application regarding the Efron bootstrap is also introduced.

• Communications for Statistical Applications and Methods
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• 제16권3호
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• pp.529-540
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• 2009

In this paper, we present some results on weak laws of large numbers for weighted sums of fuzzy random variables taking values in the space of fuzzy numbers of the real line R. We first give improvements of WLLN for weighted sums of convex-compactly uniformly integrable fuzzy random variables obtained by Joo and Hyun (2005). And then, we consider the case that the averages of expectations of fuzzy random variables converges. As results, WLLN for weighted sums of convexly tight or identically distributed case is obtained.

• Communications for Statistical Applications and Methods
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• 제16권3호
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• pp.549-556
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• 2009

Let $\{(X_{ni}|1{\leq}i{\leq}n,\;n{\geq}1)\}$ be an array of rowwise negatively associated random variables. In this paper we discuss $n^{{\alpha}p-2}h(n)max_{1{\leq}k{\leq}n}|{\sum}_{i=1}^kX_{ni}|/n^{\alpha}{\rightarrow}0$ completely as $n{\rightarrow}{\infty}$ under not necessarily identically distributed with suitable conditions for ${\alpha}$>1/2, 0${\alpha}p{\geq}1$ and a slowly varying function h(x)>0 as $x{\rightarrow}{\infty}$. In addition, we obtain the complete convergence of moving average process based on negative association random variables which extends the result of Zhang (1996).

• 한국시뮬레이션학회논문지
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• 제5권2호
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• pp.59-72
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• 1996

The purpose of this study is to make an improvement to the batch-means method, which is a procedure to construct a confidence interval(c.i.) for the steady-state process mean of a stationary simulation output process. In the batch-means method, the data in the output process are grouped into batches. The sequence of means of the data included in individual batches is called a batch-menas process and can be treated as an independently and identically distributed set of variables if each batch includes sufficiently large number of observations. The traditional batch-means method, therefore, uses a batch size as large as possible in order to. destroy the autocovariance remaining in the batch-means process. The c.i. prodedure developed and empirically tested in this study uses a small batch size which can be well fitted by a simple ARMA model, and then utilizes the dependence structure in the fitted model to correct for bias in the variance estimator of the sample mean.

• 산업경영시스템학회지
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• 제23권57호
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• pp.123-129
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• 2000

The Bootstrap method proposed by Efron is non-parametric method which doesn't depend on the estimation of prior distribution refer to population. A typical statistical process control chart which is generally used is developed under the assumption that observations follow mutually independent and identically distributed within a sample and between samples. However, autocorrelation greatly affect the developed control chart under the assumption that observations are mutually independent. Many researchers showed that the result which was analyzed by using a typical control chart for the observations which has the correlation violated to the independence assumption can not be true. Therefore, we compared the standard method with bootstrap method and then evaluated them for x control chart and EWMA control chart by using bootstrap method which was proposed by Efron in the AR(1) model when the observations have correlation.

• East Asian mathematical journal
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• 제36권1호
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• pp.25-34
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• 2020

The classical limit theorems like strong law of large numbers, central limit theorems and law of iterated logarithms are fundamental theories in probability and statistics. These limit theorems are proved under additivity of probabilities and expectations. In this paper, we investigate strong law of large numbers under sub-linear expectation which generalize the classical ones. We give strong law of large numbers under sub-linear expectation with respect to the partial sums and some conditions similar to Petrov's. It is an extension of the classical Chung type strong law of large numbers of Jardas et al.'s result. As an application, we obtain Chung's strong law of large number and Marcinkiewicz's strong law of large number for independent and identically distributed random variables under the sub-linear expectation. Here the sub-linear expectation and its related capacity are not additive.