• 호남수학학술지
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• 제42권1호
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• pp.161-173
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• 2020

We introduce statistically localized sequences in 2-normed spaces and give some main properties of statistically localized sequences. Also, we prove that a sequence is statistically Cauchy sequence if and only if its statistical barrier is equal to zero. Moreover, we define the uniformly statistically localized sequences on 2-normed spaces and investigate its relationship with statistically Cauchy sequences.

• ETRI Journal
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• 제34권5호
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• pp.753-758
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• 2012

As the next-generation video coding standard, High Efficiency Video Coding (HEVC) has adopted advanced coding tools despite the increase in computational complexity. In this paper, we propose a selective bi-prediction method to reduce the encoding complexity of HEVC. The proposed method evaluates the statistical property of the sum of absolute differences in the motion estimation process and determines whether bi-prediction is performed. A performance comparison of the complexity reduction is provided to show the effectiveness of the proposed method compared to the HEVC test model version 4.0. On average, 50% of the bi-prediction time can be reduced by the proposed method, while maintaining a negligible bit increment and a minimal loss of image quality.

• ETRI Journal
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• 제32권4호
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• pp.626-629
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• 2010

In this letter, we present an enhanced block-based adaptive loop filter (E-BALF) with multiple filter symmetric structures. The E-BALF adapts various filter symmetric structures in a rate-distortion optimization sense, reflecting the statistical properties of each image in a video sequence. Experimental results show that the proposed method achieves a reduction in the Bj${\phi}$ntegaard delta (BD)-bitrate by an average of 9.60% compared with Joint Model 11.0 of H.264/AVC. Compared to the state-of-the-art BALF, a reduction of up to 1.13% in BD-bitrate is achieved.

• 수산해양교육연구
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• 제27권4호
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• pp.963-972
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• 2015

This study examined the impact of variables(collaboration, convergence motive, convergence thinking) on the creativity problem solving of engineering college students. 522 students among engineering colleges in Pusan and Ulsan were sampled. For the statistical analysis, analysis of covariance structure by AMOS 18.0 was applied. Results from structural equation modeling analyses indicated that a hypothesized model produced a better fit to the data than a comparative structural model. The hypothesized model shows the following results. On the basis of the hypothesized model, collaboration effected to directly convergence motive and creative problem solving, and convergence motive effected to directly convergence thinking, convergence motive effected to directly creative problem solving, convergence thinking effected to directly creative problem solving, and collaboration effected to indirectly convergence thinking by convergence motive. Therefore this study suggested the collaboration, convergence motive and convergence thinking are significantly variables to facilitate the creative problem solving for knowledge fusion in engineering.

• 대한수학회지
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• 제58권1호
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• pp.237-264
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• 2021

In this work the stationary bootstrap of Politis and Romano [27] is applied to the empirical distribution function of stationary and associated random variables. A weak convergence theorem for the stationary bootstrap empirical processes of associated sequences is established with its limiting to a Gaussian process almost surely, conditionally on the stationary observations. The weak convergence result is proved by means of a random central limit theorem on geometrically distributed random block size of the stationary bootstrap procedure. As its statistical applications, stationary bootstrap quantiles and stationary bootstrap mean residual life process are discussed. Our results extend the existing ones of Peligrad [25] who dealt with the weak convergence of non-random blockwise empirical processes of associated sequences as well as of Shao and Yu [35] who obtained the weak convergence of the mean residual life process in reliability theory as an application of the association.

• 응용통계연구
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• 제6권2호
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• pp.319-328
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• 1993

뉴톤-랩슨 반복법이 최우추정량에 접근하는 비율이 초기값에 따라 가속화함을 보았다. 그러 므로 최우추정량을 구하기 어려운 경우에 통계적 목적 - Bahadur 효율, 콰지(Quasi) 우도비 검정 통계량의 점근분포, Bartlett 정정계수(correction factor)등 - 에 따라 뉴톤-랩슨 반복 의 횟수를 정하여 쓸 수 있다.

• Communications for Statistical Applications and Methods
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• 제24권5호
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• pp.481-492
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• 2017

This paper investigates a convergence rate of a test statistics given by two scale sampling method based on $A\ddot{i}t$-Sahalia and Jacod (Annals of Statistics, 37, 184-222, 2009). This statistics tests for longitudinal data having the existence of long memory dependence driven by fractional Brownian motion with Hurst parameter $H{\in}(1/2,\;1)$. We obtain an upper bound in the Kolmogorov distance for normal approximation of this test statistic. As a main tool for our works, the recent results in Nourdin and Peccati (Probability Theory and Related Fields, 145, 75-118, 2009; Annals of Probability, 37, 2231-2261, 2009) will be used. These results are obtained by employing techniques based on the combination between Malliavin calculus and Stein's method for normal approximation.

• Journal of the Korean Statistical Society
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• 제35권1호
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• pp.105-114
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• 2006

In this paper we propose a simple and computationally attractive difference-based variance estimator in nonparametric regression models with multivariate predictors. We show that the estimator achieves $n^{-1/2}$ rate of convergence for regression functions with only a first derivative when d, the dimension of the predictor, is less than or equal to 4. When d > 4, the rate turns out to be $n^{-4/(d+4)}$ under the first derivative condition for the regression functions. A numerical study suggests that the proposed estimator has a good finite sample performance.

• Communications for Statistical Applications and Methods
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• 제6권3호
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• pp.849-860
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• 1999

This paper addresses the issue of estimating regression function with errors in variables using wavelets. We adopt a nonparametric approach in assuming that the regression function has no specific parametric form, To account for errors in covariates deconvolution is involved in the construction of a new class of linear wavelet estimators. using the wavelet characterization of Besov spaces the question of regression estimation with Besov constraint can be reduced to a problem in a space of sequences. Rates of convergence are studied over Besov function classes $B_{spq}$ using $L_2$ error measure. It is shown that the rates of convergence depend on the smoothness s of the regression function and the decay rate of characteristic function of the contaminating error.

• 대한수학회논문집
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• 제21권2호
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• pp.261-272
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• 2006

In this paper, we define the sequence spaces: $[V,{\lambda},f,p]_0({\Delta}^r,E,u),\;[V,{\lambda},f,p]_1({\Delta}^r,E,u),\;[V,{\lambda},f,p]_{\infty}({\Delta}^r,E,u),\;S_{\lambda}({\Delta}^r,E,u),\;and\;S_{{\lambda}0}({\Delta}^r,E,u)$, where E is any Banach space, and u = ($u_k$) be any sequence such that $u_k\;{\neq}\;0$ for any k , examine them and give various properties and inclusion relations on these spaces. We also show that the space $S_{\lambda}({\Delta}^r, E, u)$ may be represented as a $[V,{\lambda}, f, p]_1({\Delta}^r, E, u)$ space. These are generalizations of those defined and studied by M. Et., Y. Altin and H. Altinok [7].