• Communications for Statistical Applications and Methods
• /
• v.18 no.1
• /
• pp.13-21
• /
• 2011

This paper studies the analysis of multivariate nonstationary time series with seasonality. Three types of multivariate time series models are considered: seasonal cointegration model, nonseasonal cointegration model with seasonal dummies, and vector autoregressive model in seasonal differences that are compared for forecasting performances using Korean macro-economic time series data. The cointegration models produce smaller forecast errors in short horizons; however, when longer forecasting periods are considered the vector autoregressive model appears preferable.

• Journal of the Korean Statistical Society
• /
• v.26 no.2
• /
• pp.171-180
• /
• 1997

Effects of order misspecification on statistical behavior of unit root tests are studied. We derive the limiting distributions of the Dickey-Fuller test statistics whose numerators are of the form c .int. W dW + .kappa. where W is a standard Brownian motion on [0, 1] and c is a real number. The term .kappa. is a major consequence of order misspecification and its explict expression is derived. Based on an analysis of .kappa., effects of order misspecification on unit root tests for AR(2), ARMA(1, 1), and AR(3) models are investigated.

• Journal of the Korean Statistical Society
• /
• v.21 no.2
• /
• pp.127-137
• /
• 1992

In this paper we investigate scaling limits for associated random measures satisfying some moment conditions. No stationarity is required. Our results imply an improvement of a central limit theorem of Cox and Grimmett to associated random measure and an extension to the nonstationary case of scaling limits of Burton and Waymire. Also we prove an invariance principle for associated random measures which is an extension of the Birkel's invariance principle for associated process.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.58 no.5
• /
• pp.1-10
• /
• 2016

Drought is a slow-varying natural hazard that is characterized by various factors such that reliable drought forecasting along with uncertainties estimation has been a major issue. In this study, we proposed a stochastic simulation technique based scheme for providing a set of drought scenarios. More specifically, this study utilized a nonstationary Hidden markov model that allows us to include predictors such as climate state variables and global climate model's outputs. The simulated rainfall scenarios were then used to generate the well-known meteorological drought indices such as SPI, PDSI and PN for the three dam watersheds in South Korea. It was found that the proposed modeling scheme showed a capability of effectively reproducing key statistics of the observed rainfall. In addition, the simulated drought indices were generally well correlated with that of the observed.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.6 no.4
• /
• pp.639-644
• /
• 2002

Nonlinear filters which are utilized rank-order information and temporal-order information, have many proposed, in order to restore nonstationary signals which are corrupted by additive noise. In this paper, we propose a data-dependent LWOS filter whose coefficients change based on local statistics. LWOS(Linear Combination of Weighted Order Statistics) filters［1］which also utilized two informations, and have properties of efficient impulsive and nonimpulsive noise attenuation and sufficiently details and edges preservation. DD-LWOS filters can remove non-impulsive oises while preserving signal details. DD-LWOS2 filter gets more better performance than DD-LWOS filter when input image corrupted by additive noise which includes Impulsive noise components.

• Communications for Statistical Applications and Methods
• /
• v.4 no.1
• /
• pp.101-109
• /
• 1997

For the detection on variance changes in the nonstationary time series with a unit root two types of test statistics are proposed, of which one is based on the cumulative sum of squares and the other is based on the likelihood ratio test. The properties of the cusum type test statistic are derived and the performance of two tests in small samples are compared through Monte Carlo study. It is ovserved that the test based on the cumulative sum of squares can detect a samll change in the variance faster than the one based on the likelihood ratio.

• Honam Mathematical Journal
• /
• v.24 no.1
• /
• pp.121-130
• /
• 2002

In this paper we prove a functional central limit theorem for a field $\{X_{\underline{j}}:{\underline{j}}{\in}Z_+^d\}$ of nonstationary associated random variables with $EX{\underline{j}}=0,\;E{\mid}X_{\underline{j}}{\mid}^{r+{\delta}}<{\infty}$ for some $r>2,\;{\delta}>0$and $u(n)=O(n^{-{\nu}})$ for some ${\nu}>0$, where $u(n):=sup_{{\underline{i}}{\in}Z_+^d{\underline{j}}:{\mid}{\underline{j}}-{\underline{i}}{\mid}{\geq}n}{\sum}cov(X_{\underline{i}},\;X_{\underline{j}}),\;{\mid}{\underline{x}}{\mid}=max({\mid}x_1{\mid},{\cdots},{\mid}x_d{\mid})\;for\;{\underline{x}}{\in}{\mathbb{R}}^d$. Our investigation implies and analogous result in the case associated random measure.

• Communications for Statistical Applications and Methods
• /
• v.23 no.6
• /
• pp.575-585
• /
• 2016

Longitudinal studies repeatedly measure outcomes over time. Therefore, repeated measurements are serially correlated from same subject (within-subject variation) and there is also variation between subjects (between-subject variation). The serial correlation and the between-subject variation must be taken into account to make proper inference on covariate effects (Diggle et al., 2002). However, estimation of the covariance matrix is challenging because of many parameters and positive definiteness of the matrix. To overcome these limitations, we propose autoregressive moving average Cholesky decomposition (ARMACD) for the linear mixed models. The ARMACD allows a class of flexible, nonstationary, and heteroscedastic models that exploits the structure allowed by combining the AR and MA modeling of the random effects covariance matrix. We analyze a real dataset to illustrate our proposed methods.

• The Korean Journal of Applied Statistics
• /
• v.27 no.4
• /
• pp.655-665
• /
• 2014

An adequate understanding and response to natural hazards such as heat wave, heavy rainfall and severe drought is required. We apply extreme value theory to analyze these abnormal weather phenomena. It is common for extremes in climatic data to be nonstationary in space and time. In this paper, we analyze summer rainfall data in South Korea using exceedance values over thresholds estimated by quantile regression with location information and time as covariates. We group weather stations in South Korea into 5 clusters and t extreme value models to threshold exceedances for each cluster under the assumption of independence in space and time as well as estimates of uncertainty for spatial dependence as proposed in Northrop and Jonathan (2011).

• The Korean Journal of Applied Statistics
• /
• v.22 no.6
• /
• pp.1203-1214
• /
• 2009

In this paper we introduce the time series clustering methods in the time and frequency domains and discuss the merits or demerits of each method. We analyze 15 daily stock prices of KOSPI 200, and the nonparametric method using the wavelet shows the best clustering results. For the clustering of nonstationary time series using the spectral density, the EMD method remove the trend more effectively than the differencing.