• Journal of the Korean Statistical Society
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• v.30 no.3
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• pp.387-406
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• 2001

Adaptive estimators for a class of nonlinear time series models has been proposed by several authors. Koul and Schick(1997) proposed the adaptive estimators without sample splitting for location-type time series models. They also showed by simulation that the adaptive estimators without sample splitting have smaller mean squared errors than those of the adaptive estimators with sample splitting. the present paper generalized the result in a case of location-scale type nonlinear time series models by simulation.

• The Korean Journal of Applied Statistics
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• v.33 no.4
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• pp.467-482
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• 2020

We investigate a monitoring procedure for the early detection of parameter changes in location-scale time series models. We introduce a detector for monitoring procedure based on modified residual cumulative sum (CUSUM). The asymptotic properties of the monitoring procedure are established under the null and alternative hypotheses. Simulation results and data analysis are also provided for illustration.

• Communications for Statistical Applications and Methods
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• v.30 no.4
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• pp.369-388
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• 2023

In this paper, we develop the two-step procedure that detects and estimates the position of structural changes for multivariate nonstationary time series, either on mean parameters or second-order structures. We first investigate the presence of mean structural change by monitoring data through the aggregated cumulative sum (CUSUM) type statistic, a sequential procedure identifying the likely position of the change point on its trend. If no mean change point is detected, the proposed method proceeds to scan the second-order structural change by modeling the multivariate nonstationary time series with a multivariate locally stationary Wavelet process, allowing the time-localized auto-correlation and cross-dependence. Under this framework, the estimated dynamic spectral matrices derived from the local wavelet periodogram capture the time-evolving scale-specific auto- and cross-dependence features of data. We then monitor the change point from the lower-dimensional approximated space of the spectral matrices over time by applying the dynamic principal component analysis. Different from existing methods requiring prior information on the type of changes between mean and covariance structures as an input for the implementation, the proposed algorithm provides the output indicating the type of change and the estimated location of its occurrence. The performance of the proposed method is demonstrated in simulations and the analysis of two real finance datasets.

• Structural Monitoring and Maintenance
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• v.6 no.3
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• pp.237-254
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• 2019

This paper presents an improved time series based damage detection approach with experimental verifications for detection, localization, and quantification of damage in shear-type structures under varying mass effects using output-only vibration data. The proposed method can be very effective for automated monitoring of buildings to develop proactive maintenance strategies. In this method, Auto-Regressive Moving Average models with eXogenous inputs (ARMAX) are built to represent the dynamic relationship of different sensor clusters. The damage features are extracted based on the relative difference of the ARMAX model coefficients to identify the existence, location and severity of damage of stiffness and mass separately. The results from a laboratory-scale shear type structure show that different damage scenarios are revealed successfully using the approach. At the end of this paper, the methodology limitations are also discussed, especially when simultaneous occurrence of mass and stiffness damage at multiple locations.

• Wind and Structures
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• v.26 no.3
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• pp.129-146
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• 2018

Extreme wind speed analysis has been carried out conventionally by assuming the extreme series data is stationary. However, time-varying trends of the extreme wind speed series could be detected at many surface meteorological stations in China. Two main reasons, exposure change and climate change, were provided to explain the temporal trends of daily maximum wind speed and annual maximum wind speed series data, recorded at Hangzhou (China) meteorological station. After making a correction on wind speed series for time varying exposure, it is necessary to perform non-stationary statistical modeling on the corrected extreme wind speed data series in addition to the classical extreme value analysis. The generalized extreme value (GEV) distribution with time-dependent location and scale parameters was selected as a non-stationary model to describe the corrected extreme wind speed series. The obtained non-stationary extreme value models were then used to estimate the non-stationary extreme wind speed quantiles with various mean recurrence intervals (MRIs) considering changing climate, and compared to the corresponding stationary ones with various MRIs for the Hangzhou area in China. The results indicate that the non-stationary property or dependence of extreme wind speed data should be carefully evaluated and reflected in the determination of design wind speeds.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.6
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• pp.19-27
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• 2020

This research investigates the determinants of investment capital size in Vietnam's small and medium-sized firms. The study employs a sample of 458 small and medium-sized enterprises in the country. The study is based on data collects in the areas of Hanoi, Bac Can, Buon Ma Thuot and Pleiku Provinces at time series data of October 2019. This study also identifies the factors that affect the size of investment capital in medium and small-sized enterprises in Vietnam. Data are processed via STATA 14.0 and SPSS 20.0 software. The research results indicate that (1) business lines, (2) import and export business, (3) type of business registration, (4) business location, (5) operating time, and (6) the percentage of the organization's capital contribution are factors that impact on the size of the investment capital of the business. Business line and business location have negative impacts on investment capital size. The operating time, the percentage of the organization's capital contribution, import and export business, and the type of business registration have positive impacts on investment capital size. In addition, the findings of this study also suggest that the operation time has the highest impact on investment capital size of the small and medium-sized firms in Vietnam.

• Journal of Korea Planning Association
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• v.54 no.3
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• pp.75-90
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• 2019

As the 24-hour society arrives, human activities in daytime and nighttime urban spaces are changing drastically, and the need for new urban management policies is steadily increasing. This study analyzes the types and characteristics of Seoul's human dynamics using location-based big data and the results are summarized as follows. First, the pattern of human dynamics in Seoul repeats itself every 7 days. Second, the types of human dynamics in Seoul can be classified into five types, and each of type has its own unique time-series and local characteristics. Third, the degree of match between human dynamics and zoning system in urban planning legislation was highest in 'Type 1' residence pattern and low in other types. The following implications can be drawn from these results. First, This paper examined the methodology of analyzing the regional characteristics of Seoul through the human dynamics and obtained meaningful results. Second, This paper can derive reliable and objective pattern analysis results using Big data that reflect the overall population characteristics. Third, the scale of night-time activity in the urban space of Seoul was understood, and its distribution, patterns and characteristics identified.

• Korean Institute of Interior Design Journal
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• no.41
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• pp.137-145
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• 2003

This research analyzed the space scheme in connection with complexity, one of the new changes in the discount stores, and has a goal of predicting the direction of space scheme in the upcoming complexity era. The research was conducted in the following way. Firstly, this researcher tried to grasp what kinds of changes were required in the overall distribution industry socially and economically. Secondly, the characteristic and situation of discount stores were scrutinized. Thirdly, the domestic stores' complexity status was classified and types of those were elicited. Fourthly, the time-series change and use were analyzed. The result of this analysis reveals that the types of complexity can be divided by location and adjustment to environmental changes. The time-series analysis shows that total operating area, the number of parked cars and the tenant ratio have increased dramatically in 2000 and 2003. And, according to the correlation analysis between factors, the tenant ratio has, a strong correlation with other two factors. Self-complexity takes the basic form of living facilities and complexity with other facilities is combined with other cultural, sales, educational and administrative ones. Mass-complexity is merged with the stadiums, parks or station sites. As you've seen, the concept of complex shopping mall for the realization of one stop shopping and convenience will continue in the days to come. It is desirable that the study on the large-scale shopping spaces will be conducted continually for the preparedness of future life style.

• Journal of Wetlands Research
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• v.14 no.3
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• pp.341-352
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• 2012

The purpose of this study is to statistically project future probable rainfall and to quantitatively assess a future flood vulnerability using flood vulnerability model. To project probable rainfall under non-stationarity conditions, the parameters of General Extreme Value (GEV) distribution were estimated using the 1 yr data added to the initial 30 yr base series. We can also fit a linear regression model between time and location parameters after comparing the linear relationships between time and location, scale, and shape parameters, the probable rainfall in 2030 yr was calculated using the location parameters obtained from linear regression equation. The flood vulnerability in 2030 yr was assessed inputted the probable rainfall into flood vulnerability assessment model suggested by Jang and Kim (2009). As the result of analysis, when a 100 yr rainfall frequency occurs in 2030 yr, it was projected that vulnerability will be increased by spatial average 5 % relative to present.

• Korean Journal of Agricultural and Forest Meteorology
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• v.10 no.2
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• pp.35-46
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• 2008

Soil moisture is one of the important components in hydrological processes and also controls the subsurface flow mechanism at a hillslope scale. In this study, time series of soil moisture were measured at a hillslope located in Gwangneung National Arboretum, Korea using a multiplex Time Domain Reflectometry(TDR) system measuring soil moisture with bi-hour interval. The Box-Jenkins transfer function and noise model was used to estimate spatial distributions of soil moisture histories between May and September, 2007. Rainfall was used as an input parameter and soil moisture at 10 cm depth was used as an output parameter in the model. The modeling process consisted of a series of procedures(e.g., data pretreatment, model identification, parameter estimation, and diagnostic checking of selected models), and the relationship between soil moisture and rainfall was assessed. The results indicated that the patterns of soil moisture at different locations and slopes along the hillslope were similar with those of rainfall during the measurment period. However, the spatial distribution of soil moisture was not associated with the slope of the monitored location. This implies that the variability of the soil moisture was determined more by rainfall than by the slope of the site. Due to the influence of vegetation activity on soil moisture flow in spring, the soil moisture prediction in spring showed higher variability and complexity than that in early autumn did. This indicates that vegetation activity is an important factor explaining the patterns of soil moisture for an upland forested hillslope.