• Journal of Communications and Networks
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• v.13 no.6
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• pp.621-624
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• 2011

With the rapid growth of internet traffic, accurate and reliable prediction of internet traffic has been a key issue in network management and planning. This paper proposes an autoregressive-generalized autoregressive conditional heteroscedasticity (AR-GARCH) error model for forecasting internet traffic and evaluates its performance by comparing it with seasonal autoregressive integrated moving average (ARIMA) models in terms of root mean square error (RMSE) criterion. The results indicated that the seasonal AR-GARCH models outperformed the seasonal ARIMA models in terms of forecasting accuracy with respect to the RMSE criterion.

• Journal of the Korean Statistical Society
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• v.22 no.1
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• pp.125-132
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• 1993

Large sample test of homogeneity for a panel of more than two seasonal autoregressive processes is derived and its limiting distribution is found. Detailed results are shown for the important special case that the seasonal and nonseasonal autoregressive components are both of order one.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.1049-1068
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• 2014

Autoregressive models are used to analyze an univariate time series data; however, these methods can be inappropriate when a structural break appears in a time series since they assume that a trend is consistent. Threshold autoregressive models (popular regime-switching models) have been proposed to address this problem. Recently, the models have been extended to two regime-switching models with delay parameter. We discuss two regime-switching threshold autoregressive models from a Bayesian point of view. For a Bayesian analysis, we consider a parametric threshold autoregressive model and a nonparametric threshold autoregressive model using Dirichlet process prior. The posterior distributions are derived and the posterior inferences is performed via Markov chain Monte Carlo method and based on two Bayesian threshold autoregressive models. We present a simulation study to compare the performance of the models. We also apply models to gross domestic product data of U.S.A and South Korea.

• Journal of the Korean Statistical Society
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• v.22 no.2
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• pp.271-282
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• 1993

Unit root test for temporally aggregated first order autoregressive process is considered. The temporal aggregate of fist order autoregression is an autoregressive moving average of order (1,1) with moving average parameter being function of the autoregressive parameter. One-step Gauss-Newton estimators are proposed and are shown to have the same limiting distribution as the ordinary least squares estimator for unit root when complete observations are available. A Monte-Carlo simulation shows that the temporal aggregation have no effect on the size. The power of the suggested test are nearly the same as the powers of the test based on complete observations.

• Communications for Statistical Applications and Methods
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• v.17 no.3
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• pp.441-450
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• 2010

In this paper we consider the problem of forecasting binomial time series, modelled by the binomial autoregressive model. This paper considers proposed by McKenzie (1985) and is extended to a higher order by $Wei{\ss}$(2009). Since the binomial autoregressive model is a Markov chain, we can apply the earlier work of Bu and McCabe (2008) for integer valued autoregressive(INAR) model to the binomial autoregressive model. We will discuss how to compute the h-step-ahead forecast of the conditional probabilities of $X_{T+h}$ when T periods are used in fitting. Then we obtain the maximum likelihood estimator of binomial autoregressive model and use it to derive the maximum likelihood estimator of the h-step-ahead forecast of the conditional probabilities of $X_{T+h}$. The methodology is illustrated by applying it to a data set previously analyzed by $Wei{\ss}$(2009).

• Journal of the Korean Statistical Society
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• v.24 no.1
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• pp.243-248
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• 1995

In this article we show that a class of random coefficient autoregressive processes including the NEAR (New exponential autoregressive) process has the strong mixing property in the sense of Rosenblatt with mixing order decaying to zero. The result can be used to construct model free prediction interval for the future observation in the NEAR processes.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.949-954
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• 2009

The autoregressive process is applied in this paper to kernel regression in order to infer nonlinear models for predicting responses. We propose a kernel method for the autoregressive data which estimates the mean function by kernel machines. We also present the model selection method which employs the cross validation techniques for choosing the hyper-parameters which affect the performance of kernel regression. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of mean function in the presence of autocorrelation between data.

• Journal of the Korean Data and Information Science Society
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• v.10 no.2
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• pp.397-404
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• 1999

For two-dimensional causal spatial autoregressive processes, we propose and illustrate a method for determining asymptotic simultaneous confidence regions using Yule-Walker, unbiased Yule-Walker and least squres estimators. The spectral density for first-order spatial autoregressive model are looked at in more detail. Finite sample properties based on simulation study we also presented.

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1539-1547
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• 2014

In this paper we apply the autoregressive process to the nonlinear quantile regression in order to infer nonlinear quantile regression models for the autocorrelated data. We propose a kernel method for the autoregressive data which estimates the nonlinear quantile regression function by kernel machines. Artificial and real examples are provided to indicate the usefulness of the proposed method for the estimation of quantile regression function in the presence of autocorrelation between data.

• Communications for Statistical Applications and Methods
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• v.2 no.2
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• pp.94-100
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• 1995

Vector autoregressive process disturbed by measurement error is a vector autoregressive process with nonlineat parametric restrictions on the parameter. A Newton-Raphson procedure for estimating the parameter which take advantage of the information contained in the restrictions is proposed.