• The Korean Journal of Applied Statistics
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• v.20 no.1
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• pp.103-115
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• 2007

In this paper we studied the small sample asymptotic inference for the autoregressive coefficient in AR(1) model. Based on saddlepoint approximations to the distribution of quadratic forms, we suggest a new approximation to the distribution of the estimators of the noncircular autoregressive coefficients. Simulation results show that the suggested methods are very accurate even in the small sample sizes and extreme tail area.

• The Journal of the Acoustical Society of Korea
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• v.18 no.3
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• pp.62-67
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• 1999

In this paper, we investigates a reliable model of the Predictive Recurrent Neural Network for the speech recognition. Predictive Neural Networks are modeled by syllable units. For the given input syllable, then a model which gives the minimum prediction error is taken as the recognition result. The Predictive Neural Network which has the structure of recurrent network was composed to give the dynamic feature of the speech pattern into the network. We have compared with the recognition ability of the Recurrent Network proposed by Elman and Jordan. ETRI's SAMDORI has been used for the speech DB. In order to find a reliable model of neural networks, the changes of two recognition rates were compared one another in conditions of: (1) changing prediction order and the number of hidden units: and (2) accumulating previous values with self-loop coefficient in its context. The result shows that the optimum prediction order, the number of hidden units, and self-loop coefficient have differently responded according to the structure of neural network used. However, in general, the Jordan's recurrent network shows relatively higher recognition rate than Elman's. The effects of recognition rate on the self-loop coefficient were variable according to the structures of neural network and their values.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1B
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• pp.9-20
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• 2012

Recently to overcome limitations of conceptual, hydrological and physics based models for flood stage forecasting, multiple linear regression model as one of data-driven models have been widely adopted for forecasting flood streamflow(stage). The objectives of this study are to compare performance of different multiple linear regression models according to regression coefficient estimation methods and determine most effective multiple linear regression flood stage forecasting models. To do this, the time scale was determined through the autocorrelation analysis of input data and different flood stage forecasting models developed using regression coefficient estimation methods such as LS(least square), WLS(weighted least square), SPW(stepwise) was applied to flood events in Jungrang stream. To evaluate performance of established models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient (NSEC), mean absolute error (MAE), adjusted coefficient of determination($R^{*2}$). The results show that the flood stage forecasting model using SPW(stepwise) parameter estimation can carry out the river flood stage prediction better in comparison with others, and the flood stage forecasting model using LS(least square) parameter estimation is also found to be slightly better than the flood stage forecasting model using WLS(weighted least square) parameter estimation.

• Journal of Digital Convergence
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• v.13 no.6
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• pp.125-132
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• 2015

It is current situation that a number of issues are being raised how the weight is calculated from customer satisfaction survey. This study investigated how the weight of satisfaction for each quantile is different by comparing ordinary least square regression model to quantile regression model and carried out bootstrap verification to find the influence difference of regression coefficient for each quantile. As the analysis result of using R(Quantreg package) that is open software, it appeared that there was the influence size of satisfaction factor along study result and quantile and there was the significant difference statistically regarding regression coefficient for each quantile. So, to use quantile regression model that offers the influence of satisfaction factor for each customer group along satisfaction level would contribute to plan the quantitative convergence policy for customer satisfaction.

• The Korean Journal of Applied Statistics
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• v.25 no.2
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• pp.341-350
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• 2012

Composite quantile regression model is considered for iid error case. Since the regression coefficients are the same across different quantiles, composite quantile regression can be used to combine the strength across multiple quantile regression models. For the composite quantile regression, bootstrap method is examined for statistical inference including the selection of the number of quantiles and confidence intervals for the regression coefficients. Feasibility of the bootstrap method is demonstrated through a simulation study.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.186-186
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• 2015

본 연구에서는 해안도시(부산광역시 수영구) 지역의 토지이용도와 미기상인자를 고려하여 증발산량을 산정하였으며, 증발산량 변동에 대한 미기상인자의 영향성을 구명하였다. 수영구 지역의 토지이용도와 미기상인자는 2001년 12월부터 2011년 11월에 관측된 일별 자료를 사용하였다. 토지이용도는 불투수(건물, 도로 등) 및 산림(임야), 초지(논밭, 공원 등), 수계(하천, 호수 등) 지역으로 분류하였으며, 4개 지역 특성을 고려한 최적의 추정식을 적용하여 증발산량을 산정하였다. 수영구 지역의 전체 증발산량은 4개 지역에서 산정된 증발산량에 토지이용 비율을 곱하여 구하였다. 연간 증발산량 변동은 1월부터 7월까지 증가하다가 8월부터 12월까지 감소하는 형태를 보였다. 수영구 지역에서 증발산량은 강수량의 약 13.3% 정도이었으며, 이는 연구지역의 72%에 해당하는 불투수 지역에서 배수로를 통한 물의 유출이 강우 발생 후 짧은 시간 동안 다량 발생하였기에 지속적인 증발산이 가능한 잠재수량의 저유량이 적었기 때문이다. 증발산량과 미기상인자 간의 상관분석을 수행하였으며, 증발산량과 이슬점 온도의 상관계수가 0.63으로 가장 높았다. 증발산량에 대한 기온 및 강수량, 순복사 인자의 상관계수는 0.5 이상으로 양의 상관성을, 기압 및 일조시간은 0.5 이상의 음의 상관성을 보였다. 증발산량에 대한 상관계수가 0.5 이상인 미기상인자(이슬점온도와 기온, 순복사, 기압, 강수량)에 대한 회귀 분석을 수행하였다. 이슬점온도와 기온, 순복사, 기압에 대한 증발산량 회귀함수 그래프는 강수의 유무에 따라 2가지 경향을 보였다. 이슬점온도에 따른 증발산량 회귀함수는 강수 발생 시에는 $ET=0.004x+0.7$, 무강수 시에는 $ET=0.25{\times}e^{0.04x}$로 추정되었으며, 결정계수는 각각 0.48과 0.96 정도로서 무강수 시에 높게 나타났다. 기온에 따른 증발산량 회귀함수는 강수 발생 시에는 $ET=0.004x+0.53$, 무강수 시에는 $ET=0.13{\times}e^{0.06x}$로 추정되었으며, 결정계수는 각각 0.39와 0.89 정도로서 무강수 시에 높게 나타났다. 순복사에 따른 증발산량 회귀함수는 강수 발생 시에는 $ET=0.79x+0.49$, 무강수 시에는 $ET=0.22x+0.03$로 추정되었으며, 결정계수는 각각 0.34와 0.89 정도로서 무강수 시에 높게 나타났다. 기압에 따른 증발산량 회귀함수는 강수 발생 시에는 $ET=-0.04x+37.91$, 무강수 시에는 $ET=5.18{\times}10^{22}{\times}e^{-0.05x}$로 추정되었으며, 결정계수는 각각 0.25와 0.45 정도로 나타났다. 강수량에 따른 증발산량 회귀함수는 $ET=0.23lnx+0.90$으로 추정되었으며, 결정계수 0.61정도 나타났다.

• Environmental and Resource Economics Review
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• v.22 no.2
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• pp.215-250
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• 2013

The term, "resource curse", is widely used to describe how countries rich in natural resources, such as oil, natural gas, and certain minerals, are unable to utilize that wealth to boost their economies. Contrary to previous research on the topic, this study has demonstrated that natural resources have a strong positive correlation with a country's economy. It likewise confirmed that this result is robust with broad sets of exogenous variables, and that the positive impact of natural resources on the economy remains significant with the inclusion of capital stock per worker. In this sense, it is doubtful that resource curse actually exists in the long-run. On the other hand, this study tested whether the quality of institutions has any relation with natural resource endowments if the positive effect of natural resource endowments on the gross domestic product (GDP) is adequately controlled for. In contrast to findings of Alexeev and Conrad (2009), if the former Soviet Union (FSU) countries are included, it seems that there might be a negative and statistically significant relationship between large endowments of natural resources and the quality of institutions. However, this negative relationship loses its significance and some positive albeit insignificant relationships are confirmed in a considerable number of cases when the FSU countries are excluded in the sample. That is, the negative relationship results from the inclusion of the FSU countries. This result is believed to happen by a temporary coincidence of events, a natural resource windfall and political and economic instability during the transition of the FSU countries. Therefore, the argument that resource abundance harms the institutional quality is confirmed to be a little groundless.

• Journal of Korea Water Resources Association
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• v.55 no.3
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• pp.229-243
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• 2022

The effects of monthly meteorological data measured at 11 stations in South Korea on pan coefficient were analyzed to develop the four types of multiple linear regression models for estimating pan coefficients. To evaluate the applicability of developed models, the models were compared with six previous models. Pan coefficients were most affected by air temperature for January, February, March, July, November and December, and by solar radiation for other months. On the whole, for 12 months of the year, the effects of wind speed and relative humidity on pan coefficient were less significant, compared with those of air temperature and solar radiation. For all meteorological stations and months, the model developed by applying 5 independent variables (wind speed, relative humidity, air temperature, ratio of sunshine duration and daylight duration, and solar radiation) for each station was the most effective for evaporation estimation. The model validation results indicate that the multiple linear regression models can be applied to some particular stations and months.

• Journal of the Korea Concrete Institute
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• v.14 no.5
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• pp.782-788
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• 2002

Several non-destructive test methods have been developed to estimate compressive strength of concrete in other countries. However, their applications are limited in domestic concrete due to their inaccuracies. The purpose of this study is to propose an aging coefficient of compressive strength of structural concrete in rebound number method and ultrasonic pulse velocity method for domestic concrete. The test variables include type of aggregate, curing condition, and compressive strength. Two approaches are used to estimate aging coefficient. One is evaluated by uniform linear regression equation for all ages and shows uniform strength reduction coefficient regardless of material properties and the other is evaluated by individual regression equation for each ages and shows nonuniform strength reduction and rebound increasing coefficients which decrease with increasing of rebound number and compressive strength. The latter result which can include the effect of rebound number and compressive strength is more resonable than the former.

• KIISE Transactions on Computing Practices
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• v.23 no.9
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• pp.557-563
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• 2017

When large-scale gene expression data are analyzed using lasso regression, the estimation of regression coefficients may be unstable due to the highly correlated expression values between associated genes. This irregularity, in which the coefficients are reduced by L1 regularization, causes difficulty in variable selection. To address this problem, we propose a regression model which exploits the repetitive bootstrapping of gene expression values prior to lasso regression. The genes selected with high frequency were used to build each regression model. Our experimental results show that several genes were consistently selected in all regression models and we verified that these genes were not false positives. We also identified that the sign distribution of the regression coefficients of the selected genes from each model was correlated to the real dependent variables.