• Journal of KIISE:Information Networking
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• v.37 no.1
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• pp.27-34
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• 2010

In this paper, we examine a long-range dependence in an active measurement of a network traffic which has been a well known characteristic from analyses of a passive network traffic measurement. To this end, we utilize RTT(Round Trip Time), which is a typical active measurement measured by PingER project, and perform a relevant analysis to a time series of both RTT and its volatilities. The RTT time series exhibits a long-range dependence or a 1/f noise. The volatilities, defined as a higher-order variation, follow a log-normal distribution. Furthermore, volatilities show a long-range dependence in relatively short time intervals, and a long-range dependence and/or 1/f noise in long time intervals. From this study, we find that the long-range dependence is a characteristic of not only a passive traffic measurement but also an active measurement of network traffic such as RTT. From these findings, we can infer that the long-range dependence is a characteristic of network traffic independent of a type of measurements. In particular, an active measurement exhibits a 1/f noise which cannot be usually found in a passive measurement.

• The Korean Journal of Applied Statistics
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• v.29 no.3
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• pp.449-462
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• 2016

This paper considers a long-run variance estimation using a block bootstrap method under strong dependence also known as long range dependence. We extend currently available methods in two ways. First, it extends bootstrap methods under short range dependence to long range dependence. Second, to accommodate the observation that strong dependence may come from deterministic trend plus noise models, we propose to utilize residuals obtained from the nonparametric kernel estimation with the bimodal kernel. The simulation study shows that our method works well; in addition, a data illustration is presented for practitioners.

• Communications of the Korean Mathematical Society
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• v.10 no.4
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• pp.963-974
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• 1995

We formulate central limit theorems for non-linear functionals of stationary Gaussian vector processes with long-range dependence.

• Communications for Statistical Applications and Methods
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• v.22 no.5
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• pp.495-506
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• 2015

Volatility is a variation measure in finance for returns of a financial instrument over time. GARCH models have been a popular tool to analyze volatility of financial time series data since Bollerslev (1986) and it is said that volatility is highly persistent when the sum of the estimated coefficients of the squared lagged returns and the lagged conditional variance terms in GARCH models is close to 1. Regarding persistence, numerous methods have been proposed to test if such persistency is due to volatility shifts in the market or natural fluctuation explained by stationary long-range dependence (LRD). Recently, Lee et al. (2015) proposed a residual-based cumulative sum (CUSUM) test statistic to test volatility shifts in GARCH models against LRD. We propose a bootstrap-based approach for the residual-based test and compare the sizes and powers of our bootstrap-based CUSUM test with the one in Lee et al. (2015) through simulation studies.

• The Korean Journal of Applied Statistics
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• v.27 no.2
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• pp.291-305
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• 2014

Persistence is one of the typical characteristics appearing in the volatility of financial time series. According to the recent researches, the volatility persistence may be due to either volatility shifts or long-range dependence. In this paper, we consider residual-based CUSUM tests to distinguish volatility persistence, long-range dependence and volatility shifts in GARCH models. It is observed that this test procedure achieve reasonable powers without a size distortion. Moreover, we employ AIC and BIC criteria to estimate the change points and the number of change points in volatility. We demonstrate the superiority of residual-based CUSUM tests on various Monte Carlo simulations and empirical data analysis.

• The Korean Journal of Applied Statistics
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• v.26 no.6
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• pp.987-998
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• 2013

This paper considers the statistical characteristics on the air quality (PM10) of Korea collected hourly in 2011. PM10 in Korea exhibits very strong correlations even for higher lags, namely, long range dependence. It is power-law tailed in marginal distribution, and generalized Pareto distribution successfully captures the thicker tail than log-normal distribution. However, slowly decaying autocorrelations may confuse practitioners since a non-stationary model (such as changes in mean) can produce spurious long term correlations for finite samples. We conduct a statistical testing procedure to distinguish two models and argue that the high persistency can be explained by non-stationary changes in mean model rather than long range dependent time series models.

• Journal of Sensor Science and Technology
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• v.21 no.6
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• pp.420-424
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• 2012

We have investigated temperature dependence and long-term change of humidity measurement from 32 relative humidity sensors. The readings of the humidity sensors depended not only the reference humidity, but also temperature of the chamber. Approximately, the temperature dependence of the humidity sensor in average was 0.05 %R.H./$^{\circ}C$ in the temperature range from $5^{\circ}C$ to $55^{\circ}C$. For humidity sensors that have an internal temperature compensation circuit, the resulting temperature dependence was weaker by 20%. It should be also noted that for the humidity sensors used in this work underwent ${\pm}3$ %R.H. change per year for level of confidence of 95%. The users of relative humidity sensors may refer this value as a minimum change when they set the calibration interval of the humidity sensors.

• Communications for Statistical Applications and Methods
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• v.24 no.1
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• pp.1-13
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• 2017

This manuscript summarized advances in bootstrap methods for long-range dependent time series data. The stationary linear long-memory process is briefly described, which is a target process for bootstrap methodologies on time-domain and frequency-domain in this review. We illustrate time-domain bootstrap under long-range dependence, moving or non-overlapping block bootstraps, and the autoregressive-sieve bootstrap. In particular, block bootstrap methodologies need an adjustment factor for the distribution estimation of the sample mean in contrast to applications to weak dependent time processes. However, the autoregressive-sieve bootstrap does not need any other modification for application to long-memory. The frequency domain bootstrap for Whittle estimation is provided using parametric spectral density estimates because there is no current nonparametric spectral density estimation method using a kernel function for the linear long-range dependent time process.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.4
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• pp.275-284
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• 2002

Atmospheric concentrations of organochlorine pesticides (OCPs) in Seoul, South Korea between July 1999 and May 2000 were determined to investigate concentration distribution in air, relationship between concentrations and meteorological conditions, and apportionment of sources e.g. local sources (air- surface exchange) and long range transport. Endosulfan and $\alpha$-HCH were the highest concentrations in atmosphere with values typcally ranging from 10s to l00s of pg/㎥. These high concentrations may be attributed to their usage, period and chemical property (Koa). All OCPs also showed elevated levels during the summer and were positively correlated with temperature. This would suggest that a seasonal enhancement was due to (re)volatilization from secondary sources and application during the warmer months. The temperature dependence of atmospheric concentrations of OCPs were investigated using plots of the natural logarithm of partial pressure (In P) vs reciprocal mean temperatures (1/T), and environmental phase-transition energies were calculated for each of the pesticides. For OCPs, temperature dependence was statistically significant (at the 99.99% confidence level) and temperature accounted for 35~95% of the variability in concentrations. The relatively higher slopes and phase-transition energies for $\alpha$-, ${\gamma}$-chlordane, endosulfan and endosulfan sulfate suggested that volatilization from local sources influenced their concentrations. The relatively lower those for $\alpha$-, ${\gamma}$-HCH, p, p'-DDE and heptachlor epoxide also suggested that volatilization from local sources and long range transport influenced their concentrations.

• Communications for Statistical Applications and Methods
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• v.16 no.4
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• pp.639-645
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• 2009

Fractional Brwonian motion(fBm) has properties of behaving tails and exhibiting long memory while remaining Gaussian. In particular, it is well known that interest rates show some long memories and non-Markovian. We present no aribitrage condition for HJM model under the multi-factor fBm reflecting the long range dependence in the interest rate model.