• Bulletin of the Korean Mathematical Society
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• v.47 no.4
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• pp.825-837
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• 2010

In this paper, we obtain a Korovkin type approximation theorem for double sequences of positive linear operators of two variables from $H_w$ (K) to C (K) via A-statistical convergence. Also, we construct an example such that our new approximation result works but its classical case does not work. Furthermore, we study the rates of A-statistical convergence by means of the modulus of continuity.

• Journal of the Korean Statistical Society
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• v.23 no.2
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• pp.251-269
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• 1994

In this paper we extend Kim and Pollard's cube root asymptotics to other rates of convergence, to establish an asymptotic theory for a multidimensional mode estimator based on uniform kernel with shrinking bandwidths. We obtain rates of convergence depending on shrinking rates of bandwidth and non-normal limit distributions. Optimal decreasing rates of bandwidth are discussed.

• Proceedings of the Korean Statistical Society Conference
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• 2003.10a
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• pp.183-188
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• 2003

In this paper, we establish some results on strong convergence for weighted sums of uniformly integrable fuzzy random variables taking values in the space of upper-semicontinuous fuzzy sets in R$^{p}$.

• Journal of the Korean Statistical Society
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• v.28 no.2
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• pp.253-264
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• 1999

Conditinal weakly convergence of the blockwise bootstrapped empirical process for stationary sequences to the appropriate Gaussian process is reestablished particularly for severely dependent $\alpha$-mixing sequences. Issue of block size is discussed from the point of validity of bootstrap method.

• Journal of the Korean Statistical Society
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• v.27 no.2
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• pp.153-163
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• 1998

We investigate density estimation problem in the L$^{\infty}$ norm and show that the iii optimal minimax rates are achieved for smooth classes of weakly dependent stationary sequences. Our results are then applied to give uniform convergence rates for various problems including the Gibbs sampler.

• Journal of the Korean Statistical Society
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• v.31 no.3
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• pp.359-368
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• 2002

Nonparametric kernel regression problem is considered for a stationary dependent sequence ｛(Xi, Yj) 1 j $\geq$ 1 ｝. In particular consistency and rates of convergence are discussed, which gives some useful insight for the effect of dependence for stationary $\alpha$-mixing sequences.

• Journal of the Korean Statistical Society
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• v.36 no.3
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• pp.367-372
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• 2007

Classification as a tool to extract information from data plays an important role in science and engineering. Among various classification methodologies, support vector machine has recently seen significant developments. The central problem this paper addresses is the accuracy of support vector machine. In particular, we are interested in the situations where fast rates of convergence to the Bayes risk can be achieved by support vector machine. Through learning examples, we illustrate that support vector machine may yield fast rates if the space spanned by an adopted kernel is sufficiently large.

• Journal of the Korean Statistical Society
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• v.32 no.2
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• pp.203-211
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• 2003

A new result of weak convergence of the empirical process is established for a stationary ${\phi}-mixing$ sequence of random variables, which relaxes the existing conditions on mixing coefficients. The result is basically obtained from bounds for even moments of sums of ${\phi}-mixing$ r.v.'s useful for handling triangular arrays with entries decreasing in size.

• Journal of the Korean Statistical Society
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• v.28 no.4
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• pp.435-441
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• 1999

A 'convergence in probability' rate of the empirical distributions and quantiles of linear processes is obtained. As an application of the limit theorems, a trimmed mean for the location of the linear process is considered. It is shown that the trimmed mean is asymptotically normal. A consistent estimator for the asymptotic variance of the trimmed mean is provided.

• Communications for Statistical Applications and Methods
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• v.15 no.6
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• pp.959-967
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• 2008

In this paper we show the weak convergence of the linear random(multistochastic process) field generated by identically distributed 2-parameter array of associated random variables. Our result extends the result in Newman and Wright (1982) to the linear 2-parameter processes as well as the result in Kim and Ko (2003) to the 2-parameter case.