• Journal of the Korean Statistical Society
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• v.30 no.1
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• pp.115-125
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• 2001

We consider the threshold autoregressive moving average(TARMA) process and find a sufficient condition for strict stationarity of the proces. Given region for stationarity of TARMA(p,q) model is the same as that of TAR(p) model given by Chan and Tong(1985), which shows that the moving average part of TARMA(p,q) process does not affect the stationarity of the process. We find also a sufficient condition for the existence of kth moments(k$\geq$1) of the process with respect to the stationary distribution.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.573-579
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• 2001

Wavelet thresholding is a method for he reduction of noise in image. Wavelet coefficients of image are correlated in local characterization. Thee correlations also appear in he original pixel representation of the image, and they do not follow from the characterizations of the wavelet transform. In this paper, we compare noise-free distributions of Bayes approach to improve the classical threshold algorithm.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.745-754
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• 2006

It has been shown that LAD estimates are more efficient than LS estimates when the error distribution is double exponential in AR(1) model. In order to explore the performance of LAD estimates one can use bootstrap approaches. In this paper we consider the efficiencies of bootstrap methods when we apply LAD estimates with highly variable data. Monte Carlo simulation results are given for comparing generalized bootstrap, stationary bootstrap and threshold bootstrap methods.

• Nuclear Engineering and Technology
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• v.52 no.8
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• pp.1771-1776
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• 2020

Adverse effects in the measured gamma spectrum caused by radioactive statistical fluctuations, gamma ray scattering, and electronic noise can be reduced by energy spectrum denoising. Wavelet threshold denoising can be used to perform multi-scale and multi-resolution analysis on noisy signals with small root mean square errors and high signal-to-noise ratios. However, in traditional wavelet threshold denoising methods, there are signal oscillations in hard threshold denoising and constant deviations in soft threshold denoising. An improved wavelet threshold calculation method and threshold processing function are proposed in this paper. The improved threshold calculation method takes into account the influence of the number of wavelet decomposition layers and reduces the deviation caused by the inaccuracy of the threshold. The improved threshold processing function can be continuously guided, which solves the discontinuity of the traditional hard threshold function, avoids the constant deviation caused by the traditional soft threshold method. The examples show that the proposed method can accurately denoise and preserves the characteristic signals well in the gamma energy spectrum.

• Communications for Statistical Applications and Methods
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• v.27 no.2
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• pp.177-187
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• 2020

This article is concerned with the issue of forecasting and evaluation of threshold-asymmetric volatility models for time series of count data. In particular, threshold integer-valued models with conditional Poisson and conditional negative binomial distributions are highlighted. Based on the parametric bootstrap method, some evaluation measures are discussed in terms of one-step ahead forecasting. A parametric bootstrap procedure is explained from which directional measure, magnitude measure and expected cost of misclassification are discussed to evaluate competing models. The cholera data in Bangladesh from 1988 to 2016 is analyzed as a real application.

• The Korean Journal of Applied Statistics
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• v.23 no.4
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• pp.621-633
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• 2010

This paper considers a certain expense structure where a vendor of equity-linked investment product will collect its expenses continuously from the investor's account whenever the investment performance exceeds a certain threshold level. Under the Black-Scholes framework, we derive compact convolution formulas for evaluating the total expenses to be collected during the investment period by using the joint Laplace transform of the Brownian motion and its excursion time. We provide numerical examples for illustration.

• Journal of Korean Society for Quality Management
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• v.33 no.3
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• pp.126-134
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• 2005

This paper proposes a selectively cumulative sum(S-CUSUM) control chart for detecting shifts in the process mean. The basic idea of the S-CUSUM chart is to accumulate previous samples selectively in order to increase the sensitivity. The S-CUSUM chart employs a threshold limit to determine whether to accumulate previous samples or not. Consecutive samples with control statistics out of the threshold limit are to be accumulated to calculate a standardized control statistic. If the control statistic falls within the threshold limit, only the next sample is to be used. During the whole sampling process, the S-CUSUM chart produces an 'out-of-control' signal either when any control statistic falls outside the control limit or when L -consecutive control statistics fall outside the threshold limit. The number L is a decision variable and is called a 'control length'. A Markov chain approach is employed to describe the S-CUSUM sampling process. Formulae for the steady state probabilities and the Average Run Length(ARL) during an in-control state are derived in closed forms. Some properties useful for designing statistical parameters are also derived and a statistical design procedure for the S-CUSUM chart is proposed. Comparative studies show that the proposed S-CUSUM chart is uniformly superior to the CUSUM chart or the Exponentially Weighted Moving Average(EWMA) chart with respect to the ARL performance.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.443-450
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• 2007

In this paper, we propose a novel threshold selection method based on statistical information on gray-levels of given images and interval-valued fuzzy sets. In the proposed threshold selection method, the interval-valued fuzzy set is used to represent more definitely the relationship between a pixel and its belonging region, that is, the object and the background. Also the statistical information on gray-level is used to determine the rules and partitions of interval-valued fuzzy sets. To show the validity of the proposed method, we compared the performance of the proposed with those of conventional methods such as Otsu's method, Huang and Wang's method applied to 5 test images with various types of histograms.

• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.551-558
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• 2008

Association rule mining searches for interesting relationships among items in a given database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. If there is many item in the association rule, much time is required. Xiong(2004) studies new method which is to compute the support of upper. They used support of upper to the $^{\theta}$. But $^{\theta}$ is subjective. In this paper, we present statistical objective criterion for efficiently identifying correlated pairs.

• 한국데이터정보과학회:학술대회논문집
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• 2006.11a
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• pp.197-203
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• 2006

Association rule mining searches for interesting relationships among Items in a given database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule support and confidence and lift. If there is many item in the association rule, much time is required. Xiong(2004) studies new method which is to compute the support of upper. They used support of upper to the $\theta$. But $\theta$ is subjective. In this paper, we present statistical objective criterion for efficiently identifying correlated pairs.