• Journal of Environmental Science International
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• v.11 no.11
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• pp.1141-1147
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• 2002

Based on the tide gauge data from the Permanent Service for Meau Sea Level (PSMSL) collected at 23 locations in the Korean coast, the long-term sea-level trend was computed using a simple linear regression fit over the recorded length of the monthly mean sea-level data. The computed sea-level trend was also corrected for the vertical land movement due to post glacial rebound(PGR) using the ICE-4G(VM2) model output. It was found that the PGR-corrected sea-level trend near Korea was 2.310 $\pm$ 2.220 mm/yr, which is higher than the global average at 1.0∼2.0mm/yr, as assessed by the Intergovernmental Panel on Climate Change(IPCC). The regional distribution of the long-term sea-level trend near Korea revealed that the South Sea had the largest sea-level rise followed by the West Sea and East Sea, respectively, supporting the results of the previous study by Seo et al. However, due to the relatively short record period and large spatial variability, the sea-level trend from the tide gauge data for the Korean coast could be biased with a steric sea-level rise by the global warming during the 20th century.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.14
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• pp.5859-5861
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• 2015

Background: Kidney cancer has shown an increasing trend in recent decades. This study aimed to determine change in the incidence rate between 2003 and 2009 in Iran. Materials and Methods: In this study, national cancer registry data were used. Crude incidence rates were calculated per 100,000 and age-standardized incidence rates (ASRs) were computed using the direct standardization method and the world standard population. Significant trend of incidence rates was examined by the Cochran-Armitage test for linear trend. Results: A total of 6,944 cases of kidney cancer were reported. The incidence cases increased from 595 patients in 2003 to 1,387 patients in 2009. Sex ratio (male to female) was 1.67. ASR also increased from 1.18 in 2003 to 2.52 in 2009 per 100,000, but the increasing trend was not significant. Conclusions: A slow increasing trend of incidence rate was observed in the study population. This may be due to an increase of risk factors. It is suggested to perform a study on risk factors for the cancer.

• Korean Journal of Remote Sensing
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• v.33 no.1
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• pp.25-35
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• 2017

Spatial downscaling with fine resolution auxiliary variables has been widely applied to predict precipitation at fine resolution from coarse resolution satellite-based precipitation products. The spatial downscaling framework is usually based on the decomposition of precipitation values into trend and residual components. The fine resolution auxiliary variables contribute to the estimation of the trend components. The main focus of this study is on quantitative analysis of impacts of trend component estimates on predictive performance in spatial downscaling. Two regression models were considered to estimate the trend components: multiple linear regression (MLR) and geographically weighted regression (GWR). After estimating the trend components using the two models,residual components were predicted at fine resolution grids using area-to-point kriging. Finally, the sum of the trend and residual components were considered as downscaling results. From the downscaling experiments with time-series Tropical Rainfall Measuring Mission (TRMM) 3B43 precipitation data, MLR-based downscaling showed the similar or even better predictive performance, compared with GWR-based downscaling with very high explanatory power. Despite very high explanatory power of GWR, the relationships quantified from TRMM precipitation data with errors and the auxiliary variables at coarse resolution may exaggerate the errors in the trend components at fine resolution. As a result, the errors attached to the trend estimates greatly affected the predictive performance. These results indicate that any regression model with high explanatory power does not always improve predictive performance due to intrinsic errors of the input coarse resolution data. Thus, it is suggested that the explanatory power of trend estimation models alone cannot be always used for the selection of an optimal model in spatial downscaling with fine resolution auxiliary variables.

• Communications for Statistical Applications and Methods
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• v.6 no.2
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• pp.457-466
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• 1999

Centered modified systematic sampling (CMSS)' was proposed by Kim(1985) for estimating the mean of a population with a linear trend. In the present paper a version of this sampling method is suggested. This version turns out to be efficient in the same degree as the original method from the viewpoint of the expected mean square error criterion. It is also shown to be quite an efficient method as compared with other existing methods. An illustrative example is given.

• Atmosphere
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• v.31 no.1
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• pp.101-112
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• 2021

The long-term trend of surface wind speed in Korea is examined for 31 KMA weather stations from 1985 to 2019. Most stations, except Daegwallyeong, have several times of anemometer height changes from tens of centimeters to several meters. To minimize such height change effect on long-term wind trend, the present study adjusts anemometer height in each station to the standard height of 10 m using the power-law wind profile. This adjustment results in non-negligible trend change. For instance, the increasing surface wind speed at Suwon station, which has six times of anemometer height changes in a range of 0.8 m to 20 m, is weakened up to 67% and becomes statistically insignificant. Likewise, the decreasing trend at Andong station, with three times of anemometer height changes in a range of 10 m to 15.5 m, is weakened up to 66%. A similar weakening in long-term trend is observed in most stations regardless of positive and negative trends. However, due to the cancellation between weakened negative trends and weakened positive trends, the station-averaged wind speed trend in Korea does not change much. This result suggests that anemometer height adjustment is crucial for evaluating local wind speed trend but its impact on nation-wide wind speed trend is rather minor.

• Atmosphere
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• v.31 no.5
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• pp.553-562
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• 2021

The long-term trend of surface wind speed in Korea is estimated by correcting wind measurements at 29 KMA weather stations from 1985 to 2019 with physical and statistical homogenization. The anemometer height changes at each station are first adjusted by applying physical homogenization using the power-law wind profile. The statistical homogenization is then applied to the adjusted data. A standard normal homogeneity test (SNHT) is particularly utilized. Approximately 40% of inhomogeneities detected by the SNHT match with the sea-level-height change of each station, indicating that an SNHT is an effective technique for reconciling data inhomogeneity. The long-term trends are compared with homogenized data. Statistically significant negative trends are observed along the coast, while insignificant trends are dominant inland. The mean trend, averaged over all stations, is -0.03 ± 0.07 m s^-1 decade^-1. This insignificant trend is due to a trend change across 2001. A decreasing trend of -0.10 m s^-1 decade^-1 reverses to an increasing trend of 0.03 m s^-1 decade^-1 from 2001. This trend change is consistent with mid-latitude wind change in the Northern hemisphere, indicating that the long-term trend of surface wind speed in Korea is partly determined by large-scale atmospheric circulation.

• The Korean Journal of Applied Statistics
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• v.21 no.1
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• pp.19-33
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• 2008

Linear regression method, proposed by Haseman and Elston(1972), for detecting linkage to a quantitative trait of sib pairs is a linkage testing method for a single locus and a single trait. However, multivariate methods for detecting linkage are needed, when information from each of several traits that are affected by the same major gene are available on each individual. Amos et al. (1990) extended the regression method of Haseman and Elston(1972) to incorporate observations of two or more traits by estimating the principal component linear function that results in the strongest correlation between the squared pair differences in the trait measurements and identity by descent at a marker locus. But, it is impossible to control the probability of type I errors with this method at present, since the exact distribution of the statistic that they use is yet unknown. In this paper, we propose a multivariate nonparametric trend test for detecting linkage to multiple traits. We compared with a simulation study the efficiencies of multivariate nonparametric trend test with those of the method developed by Amos et al. (1990) for quantitative traits data. For multivariate nonparametric trend test, the results of the simulation study reveal that the Type I error rates are close to the predetermined significance levels, and have in general high powers.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1B
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• pp.55-64
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• 2008

The evidence of changes in the climate system is obvious in the world. Nevertheless, at the current techniques for flood frequency analysis, the flood distribution can not reflect climate change or long-term climate cycles. Using a linear regression and a Mann-Kendall test, trends in annual maximum precipitation and flood data for several major gauging sites were evaluated. Moreover, this research considered incorporating flood trends by climate change effects in flood frequency analyses. For five rainfall gauging sites (Seoul, Incheon, Ulleungdo, Jeonju, and Gangneung), upward trends were observed in all gauged annual maximum precipitation records but they were not statistically significant. For three streamflow gauging sites (Andong Dam, Soyanggang Dam, and Daecheong Dam), upward trends were also observed in all gauged annual maximum flood records, but only the flood at Andong Dam was statistically significant. A log-normal trend model was introduced to reflect the observed linear trends in annual maximum flood series and applied to estimate flood frequency and risk for Andong Dam and Soyanggang Dam. As results, when the target year was 2005, 50-year floods of the log-normal trend model were 41% and 21% larger then those of a log-normal model for Andong Dam and Soyanggang Dam, respectively. Moreover, the estimated floods of the log-normal trend model increases as the target year increases.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4B
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• pp.355-362
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• 2006

Several techniques of MK test, Spearman's Rho test, Linear Regression test, CUSUM test, Cumulative Deviation, Worsley Likelihood Ratio test, Rank Sum test, and Students' t test were applied to detect the trends of slope and shift which exist in hydrologic and climate time series. The time series of annual rainfall, inflow, tree ring index, and southern oscillation index (SOI) were used and the trends of these series were compared in the study. From the results, it can be found that the data could be classified into two categories such as linear trend and shift. 4 series data of 8 rainfall series which reveal the trend show the shift and 8 series data of 18 tree ring index and March and April series of monthly SOI data show shift. Moreover, ADF test and BDS test were used to test stationarity and non-linearity of the data. In conclusion, through the study, various trend analysis techniques were compared and 6 kinds of characteristics which can exist in hydrologic time series were identified.

• The Korean Journal of Applied Statistics
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• v.21 no.6
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• pp.933-945
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• 2008

The genetic association test on age of onset trait aims to detect the putative gene by means of linear rank tests for a significant trend of onset distributions with genotypes. However, due to the selective sampling of recruiting subjects with ages less than a pre-specified limit, the genotype groups are subject to substantially different censored distributions and thus this is one reason for the low efficiencies in the linear rank tests. In testing the equality of two survival distributions, log-rank statistic is preferred to the Wilcoxon statistic, when censored observations are nonignorable. Therefore, for more then two groups, we propose a generalized log-rank test for trend as a genetic association test. Monte Carlo studies are conducted to investigate the performances of the test statistics examined in this paper.