• 대한수학회논문집
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• 제14권3호
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• pp.597-609
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• 1999

In this article we prove a central limit theorem for strictly stationary weakly dependent random fields with some interlaced mix-ing conditions. Mixing coefficients are not assumed. The result it basically the same to Peligrad([4]), which is CLT weakly depen-dent arrays of random variables. The proof is quite similar to the of Peligrad.

• Communications for Statistical Applications and Methods
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• 제9권1호
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• pp.241-248
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• 2002

The problem of wavelet density estimation based on Shannon's wavelets is studied when the sample observations are contaminated with random noise. In this paper we will discuss the asymptotic normality for deconvolving wavelet density estimator of the unknown density f(x) when courier transform of random noise has polynomial descent.

• Korean Journal of Mathematics
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• 제6권2호
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• pp.231-242
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• 1998

On the basis of Heyer and Zeuner's results we will treat the central limit theorem for probability measures on hypergroup.

• 호남수학학술지
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• 제27권3호
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• pp.505-513
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• 2005

The aim of this paper is to establish a functional central limit theorem for multivariate moving average process generated by negatively associated random vectors under the finite second moments.

• 대한수학회논문집
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• 제14권3호
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• pp.629-629
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• 1999

• 대한수학회논문집
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• 제16권1호
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• pp.125-135
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• 2001

In this article we investigate the dependence between components of the random vector which is given as an asymptotic limit of an array of random vectors with interlaced mixing conditions. We discuss the cross covariance of the limiting vector process and give a stronger condition to have a central limit theorem for an array of random vectors with mixing conditions.

• Communications for Statistical Applications and Methods
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• 제21권4호
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• pp.327-334
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• 2014

Various modified GARCH(1, 1) models have been found adequate in many applications. We are interested in their continuous time versions and limiting properties. We first define a stochastic integral that includes useful continuous time versions of modified GARCH(1, 1) processes and give sufficient conditions under which the process is exponentially ergodic and ${\beta}$-mixing. The central limit theorem for the process is also obtained.

• Journal of applied mathematics & informatics
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• 제18권1_2호
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• pp.441-453
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• 2005

In this article we will introduce a real valued random field on a restricted indexed set and construct a classical asymptotic limit theorems on them. We will survey the basic properties of weakly dependent random processes and investigate two major mixing conditions for sequences of random variables. The concepts of weakly dependent sequence of random variables will be generalized to the case of random fields. Finally we will construct a central limit theorem and prove it.

• Journal of applied mathematics & informatics
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• 제26권1_2호
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• pp.417-425
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• 2008

For stationary m-dimensional martingale difference sequences we prove the random functional central limit theorems and propose an almost sure consistent estimator for the limiting covariance matrix.

• Communications for Statistical Applications and Methods
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• 제21권6호
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• pp.539-559
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• 2014

We derive the exact distribution of summation for random samples from uniform distribution and then compare the exact distribution with the approximated normal distribution obtained by the central limit theorem. To check the similarity between two distributions, we consider five existing normality tests based on the difference between the target normal distribution and empirical distribution: Anderson-Darling test, Kolmogorov-Smirnov test, Cramer-von Mises test, Shapiro-Wilk test and Shaprio-Francia test. For the purpose of comparison, those normality tests are applied to the simulated data. It can sometimes be difficult to derive an exact distribution. Thus, we try two different transformations to find out which transform is easier to get the exact distribution in terms of calculation complexity. We compare two transformations and comment on the advantages and disadvantages for each transformation.