• International Journal of Reliability and Applications
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• v.10 no.1
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• pp.43-57
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• 2009

In this article, we study the reliability equivalence factor of a series system. The failure rates of the system components are functions of time t. we study two cases of non-constat failure rates (i) weibull distribution (ii) linear increasing failure rate distribution. There are two methods are used to improve the given system. Two types of reliability equivalence factors are discussed. Numerical examples are presented to interpret how one can utilize the obtained results.

• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.149-160
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• 1995

We consider the problem of optimal adaptive estiamtion of the euclidean parameter vector $\theta$ of the univariate non-linerar autogressive time series model ${X_t}$ which is defined by the following system of stochastic difference equations ; $X_t = \sum^p_{i=1} \theta_i \cdot T_i(X_{t-1})+e_t, t=1, \cdots, n$, where $\theta$ is the unknown parameter vector which descrives the deterministic dynamics of the stochastic process ${X_t}$ and ${e_t}$ is the sequence of white noises with unknown density $f(\cdot)$. Under some general growth conditions on $T_i(\cdot)$ which guarantee ergodicity of the process, we construct a sequence of adaptive estimatros which is locally asymptotic minimax (LAM) efficient and also attains the least possible covariance matrix among all regular estimators for arbitrary symmetric density.

• Wind and Structures
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• v.2 no.2
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• pp.85-94
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• 1999

The EARPG(1)/UPS was first developed by Seong (1993) and has been tested for wind pressure time series simulations (Seong and Peterka 1993, 1997, 1998) to prove its excellent performance for generating non-Gaussian time series, in particular, with large amplitude sharp peaks. This paper presents a parametric study focused on simulation of extreme value statistics based on the synthetic realizations of the EARPG(1)/UPS. The method is shown to have a great capability to simulate a wide range of non-Gaussian statistic values and extreme value statistics with exact target sample power spectrum. The variation of skewed long tail in PDF and extreme value distribution are illustrated as function of relevant parameters.

• Korean Management Science Review
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• v.8 no.1
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• pp.3-11
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• 1991

Non-Stationary transition probabilities models which is incorporated into a Markov framework with exogenous variables to account for some of variability are discussed, and extended for alternative procedure. Also as an application of the methodology, the size change of aggregate time-series data on the planted system of agricultural crops is estimated, and evaluated for the precision of time-varying evolution statistically.

• Asia-Pacific Journal of Atmospheric Sciences
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• v.54 no.4
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• pp.611-622
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• 2018

Observed climate data are processed under the assumption that their time series are stationary, as in multi-step temperature and precipitation prediction, which usually leads to low prediction accuracy. If a climate system model is based on a single prediction model, the prediction results contain significant uncertainty. In order to overcome this drawback, this study uses a method that integrates ensemble prediction and a stepwise regression model based on a mean-valued generation function. In addition, it utilizes empirical mode decomposition (EMD), which is a new method of handling time series. First, a non-stationary time series is decomposed into a series of intrinsic mode functions (IMFs), which are stationary and multi-scale. Then, a different prediction model is constructed for each component of the IMF using numerical ensemble prediction combined with stepwise regression analysis. Finally, the results are fit to a linear regression model, and a short-term climate prediction system is established using the Visual Studio development platform. The model is validated using temperature data from February 1957 to 2005 from 88 weather stations in Guangxi, China. The results show that compared to single-model prediction methods, the EMD and ensemble prediction model is more effective for forecasting climate change and abrupt climate shifts when using historical data for multi-step prediction.

• The Bulletin of The Korean Astronomical Society
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• v.41 no.1
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• pp.81.2-81.2
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• 2016

Many signals in the nature have power-law behaviors, namely they are "scale-free". The method of detrended fluctuation analysis (DFA), as one of the popular methods (e.g., Rescaled range analysis and Spectral analysis) for determining scale-free nature of time series, has a very important advantage that the DFA can be applied to both stationary and non-stationary signals. The analysis of time series using the DFA has been broadly used in physiology, finance, hydrology, meteorology, geology, and so on. We performed the DFA of 16 Spaceweather HMI Active Region Patch (SHARP) parameters for 38 HMI Active Region Patches (HARPs) obtained by Solar Dynamics Observatory (SDO) from May 2010 to June 2014. The main results from this study are as follows. (1) The most of the time series data are non-stationary. (2) The DFA scaling exponents of "mean vertical current density" for 38 HARPs have a negative correlation coefficient (-0.41) with flare index. (3) The DFA scaling exponents of parameters such as "Sum of the absolute value of net currents per polarity", "Absolute value of the net current helicity", and "Mean photospheric excess magnetic energy density" for the most active HARPs having more than 10 major flares, have positive correlation coefficients (0.64, 0.59, and 0.53, respectively) with the ratio of "the number of CMEs associated with major flares" to "the number of major flares". Physical interpretations on our results will be discussed.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.611-622
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• 2021

With a wide recognition that financial time series typically exhibits asymmetry patterns in volatility so called leverage effects, various asymmetric GARCH(1, 1) processes have been introduced to investigate asymmetric volatilities. A lot of researches have also been directed to non-stationary volatilities to deal with frequent high ups and downs in financial time series. This article is concerned with both asymmetric and non-stationary GARCH-type models. As a subsequent paper of Choi et al. (2020), we review various asymmetric and non-stationary GARCH(1, 1) processes, and in turn propose how to compare competing models using a parametric bootstrap methodology. As an illustration, Dow Jones Industrial Average (DJIA) is analyzed.

• The Korean Journal of Applied Statistics
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• v.1 no.1
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• pp.57-67
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• 1987

In time series analysis, we usually require the assumption that time series are stationary. But we may often encounter time series whose parameter values subject to change. Inthis paper w propose a method which can detect the variance change point in anAR(1) model which is subjct to changesat non-predictable time points. Proposed method is compared with other methods using the simulated and real data.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.146.4-146
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• 2001

Various methods have been proposed for the time series prediction. Most of the conventional methods only optimize parameters of mathematical models, but to construct an appropriate functional form of the model is more difficult in the first place. We employ the Genetic Programming (GP) to construct the functional form of prediction models. Our method is distinguished because the model parameters are optimized by using Back-Propagation (BP)-like method and the prediction model includes discontinuous functions, such as if and max, as node functions for describing complicated phenomena. The above-mentioned functions are non-differentiable, but the BP method requires derivative. To solve this problem, we develop ...

• Journal of the Korean Statistical Society
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• v.7 no.1
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• pp.11-16
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• 1978

Bayesian procedures are in vogue to revise the parameter estimates of the forecasting model in the light of actual time series data. In this paper, we study the Bayes forecast for demand and the risk when (a) 'noise' and (b) mean demand rate in a constant process model have moderately non-normal probability distributions.