• Title/Summary/Keyword: 오차자기상관 회귀모형

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Improving Forecasts of Dam Inflow Using Rescaling Errors From ANN and Regression Model (ANN과 회귀모형의 오차 수정을 통한 댐 유입량 예측 향상)

  • Jang, Sun-Woo;Yoo, Ji-Young;Kim, Tae-Woong
    • Proceedings of the Korea Water Resources Association Conference
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    • 2010.05a
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    • pp.1164-1168
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    • 2010
  • 수자원이 우리 생활의 전반적으로 중요한 역할을 차지하면서 댐의 효율적인 운영과 안정적인 용수공급에 대한 연구는 지속적으로 수행되어지고 있다. 1990년대 이후 비선형적인 특성을 잘 모의하는 장점을 가진 인공신경망(ANN)을 이용하여 유입량 예측에 대한 많은 연구가 수행되었다. 하지만 ANN 모형을 포함한 회귀모형은 월 강우 및 유입량의 예측에 대해 간편하게 사용을 할 수 있지만, 예측의 정확성에 한계를 가지고 있다. 본 연구에서는 ANN 모형과 회귀모형의 예측오차를 후처리 과정을 통하여 오차를 줄임으로써 예측모형의 성과를 향상시키는 방법을 제안하였다. 연구지역은 금강수계의 대청댐 유역으로, 1982년 9월부터 2005년 12월에 해당하는 유역 내 11개 지점의 강우관측소에서 관측한 월 강우와 댐 유입량을 수집하여 모형을 구축하였다. 강우량과 유입량 자료에 대해 자기상관함수와 교차상관함수를 이용하여 입력변수를 결정하였고, 정규화를 통한 전처리 과정을 거쳐 ANN 모형과 회귀모형을 이용한 예측모형을 구축하였으며, 예측성과의 향상을 위하여 군집 분석을 이용하여 오차를 재조정하였다. 이러한 오차 후처리 과정을 포함한 모형은 RMSE와 상관계수를 이용하여 비교 평가한 결과, 예측성과를 약 40% 정도 향상시켰다.

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Efficient Estimation of Regression Coefficients in Regression Model with Moving Average Process (오차항이 이동평균과정을 따르는 회귀모형에서 회귀계수의 효율적 추정에 관한 연구)

  • 송석현;이종협;김기환
    • The Korean Journal of Applied Statistics
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    • v.12 no.1
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    • pp.109-124
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    • 1999
  • 일반적으로 오차항이 자기상관되어 있는 선형회귀 모형에서는 회귀계수에 대한 보통최소제곱추정량이 효율적이지 못 하다고 알려져 있다. 그러나 이러한 일반화선형회귀모형에서 독립변수의 형태에 따라서는 OLSE의 사용 가능성을 제시하는 모형이 있다. 본 연구에서는 오차항이 일차 이동평균 과정을 따르는 선형회귀모형에서 여러 추정량들 (GLSE, APX, MAPX)에 대한 OLSE의 상대효율함수를 유도하고 비교 분석하고자 한다. 특히 소표본에서 정확한 상대효율값을 구하여 OLSE의 효율성이 크게 떨어지지 않거나 효율성이 나은 회귀모형들을 제시한다.

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A comparison study on regression with stationary nonparametric autoregressive errors (정상 비모수 자기상관 오차항을 갖는 회귀분석에 대한 비교 연구)

  • Yu, Kyusang
    • The Korean Journal of Applied Statistics
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    • v.29 no.1
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    • pp.157-169
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    • 2016
  • We compare four methods to estimate a regression coefficient under linear regression models with serially correlated errors. We assume that regression errors are generated with nonlinear autoregressive models. The four methods are: ordinary least square estimator, general least square estimator, parametric regression error correction method, and nonparametric regression error correction method. We also discuss some properties of nonlinear autoregressive models by presenting numerical studies with typical examples. Our numerical study suggests that no method dominates; however, the nonparametric regression error correction method works quite well.

On Testing the First-order Autocorrelation of the Error Term in a Regression Model via Multiple Bayes Factor (다중 베이즈요인에 의한 회귀모형 오차항의 자기상관 검정)

  • 한성실;김혜중
    • The Korean Journal of Applied Statistics
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    • v.12 no.2
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    • pp.605-619
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    • 1999
  • 본 논문은 회귀분석에서 오차항의 1차 자기상관 존재 여부 및 그 값을 검정하는 방법을 베이지안 접근법으로 제안하였다. 이 방법은 모수공간의 다중분할로 인해 얻어진 여러 가설들에 대한 다중결정문제를 다중 베이즈요인에 관한 이론과 일반화 Savage-Dickey 밀도비를 이용한 사후확률 추정법을 합성하여 개발되었다. 이 방법은 기존의 검정법들에서 가능한 검정 뿐 아니라 이들이 해결할 수 없는 자기상관에 대한 다중결정문제에도 사용이 가능한데 그 효용성이 있다. 모의실험을 통하여 제안된 검정법의 유효성을 평가하였다.

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A Bayesian test for the first-order autocorrelations in regression analysis (회귀모형 오차항의 1차 자기상관에 대한 베이즈 검정법)

  • 김혜중;한성실
    • The Korean Journal of Applied Statistics
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    • v.11 no.1
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    • pp.97-111
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    • 1998
  • This paper suggests a Bayesian method for testing first-order markov correlation among linear regression disturbances. As a Bayesian test criterion, Bayes factor is derived in the form of generalized Savage-Dickey density ratio that is easily estimated by means of posterior simulation via Gibbs sampling scheme. Performance of the Bayesian test is evaluated and examined based upon a Monte Carlo experiment and an empirical data analysis. Efficiency of the posterior simulation is also examined.

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Wild bootstrap Ljung-Box test for autocorrelation in vector autoregressive and error correction models (벡터자기회귀모형과 오차수정모형의 자기상관성을 위한 와일드 붓스트랩 Ljung-Box 검정)

  • Lee, Myeongwoo;Lee, Taewook
    • The Korean Journal of Applied Statistics
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    • v.29 no.1
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    • pp.61-73
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    • 2016
  • We consider the wild bootstrap Ljung-Box (LB) test for autocorrelation in residuals of fitted multivariate time series models. The asymptotic chi-square distribution under the IID assumption is traditionally used for the LB test; however, size distortion tends to occur in the usage of the LB test, due to the conditional heteroskedasticity of financial time series. In order to overcome such defects, we propose the wild bootstrap LB test for autocorrelation in residuals of fitted vector autoregressive and error correction models. The simulation study and real data analysis are conducted for finite sample performance.

Busan Housing Market Dynamics Analysis with ESDA using MATLAB Application (공간적탐색기법을 이용한 부산 주택시장 다이나믹스 분석)

  • Chung, Kyoun-Sup
    • The Journal of the Korea Contents Association
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    • v.12 no.2
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    • pp.461-471
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    • 2012
  • The purpose of this paper is to visualize the housing market dynamics with ESDA (Exploratory Spatial Data Analysis) using MATLAB toolbox, in terms of the modeling housing market dynamics in the Busan Metropolitan City. The data are used the real housing price transaction records in Busan from the first quarter of 2006 to the second quarter of 2009. Hedonic house price model, which is not reflecting spatial autocorrelation, has been a powerful tool in understanding housing market dynamics in urban housing economics. This study considers spatial autocorrelation in order to improve the traditional hedonic model which is based on OLS(Ordinary Least Squares) method. The study is, also, investigated the comparison in terms of $R^2$, Sigma Square(${\sigma}^2$), Likelihood(LR) among spatial econometrics models such as SAR(Spatial Autoregressive Models), SEM(Spatial Errors Models), and SAC(General Spatial Models). The major finding of the study is that the SAR, SEM, SAC are far better than the traditional OLS model, considering the various indicators. In addition, the SEM and the SAC are superior to the SAR.

Forecasting drug expenditure with transfer function model (전이함수모형을 이용한 약품비 지출의 예측)

  • Park, MiHai;Lim, Minseong;Seong, Byeongchan
    • The Korean Journal of Applied Statistics
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    • v.31 no.2
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    • pp.303-313
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    • 2018
  • This study considers time series models to forecast drug expenditures in national health insurance. We adopt autoregressive error model (ARE) and transfer function model (TFM) with segmented level and trends (before and after 2012) in order to reflect drug price reduction in 2012. The ARE has only a segmented deterministic term to increase the forecasting performance, while the TFM explains a causality mechanism of drug expenditure with closely related exogenous variables. The mechanism is developed by cross-correlations of drug expenditures and exogenous variables. In both models, the level change appears significant and the number of drug users and ratio of elderly patients variables are significant in the TFM. The ARE tends to produce relatively low forecasts that have been influenced by a drug price reduction; however, the TFM does relatively high forecasts that have appropriately reflected the effects of exogenous variables. The ARIMA model without the exogenous variables produce the highest forecasts.

Estimation of the Natural Damage Disaster Considering the Spatial Autocorrelation and Urban Characteristics (공간적 자기상관성과 도시특성 요소를 고려한 자연재해 피해 분석)

  • Seo, Man Whoon;Lee, Jae Song;Choi, Yeol
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.36 no.4
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    • pp.723-733
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    • 2016
  • This study aims to analyze the effects of urban characteristics on the amount of damage caused by natural disasters. It is focused on the areas of a municipal level in Korea. Also, it takes into account the spatial autocorrelation of the damage caused by natural disasters. Moran's I statistics was estimated to examine the spatial autocorrelation in the damage from the study area. Subsequent to evaluating the suitability for spatial regression models and the OLS regression model, the spatial lag model was employed as an empirical analysis for the study. It showed that the increase in residential area leads to the decrease in the amount of natural disaster damage. On the other hand, the increase in green area and river basin is associated with the increase in the damage. As a result of empirical analysis, appropriate policy establishment and implementation about the damage-adding factors is needed in order to reduce the amount of damage in the future.

Population Distribution Estimation Using Regression-Kriging Model (Regression-Kriging 모형을 이용한 인구분포 추정에 관한 연구)

  • Kim, Byeong-Sun;Ku, Cha-Yong;Choi, Jin-Mu
    • Journal of the Korean Geographical Society
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    • v.45 no.6
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    • pp.806-819
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    • 2010
  • Population data has been essential and fundamental in spatial analysis and commonly aggregated into political boundaries. A conventional method for population distribution estimation was a regression model with land use data, but the estimation process has limitation because of spatial autocorrelation of the population data. This study aimed to improve the accuracy of population distribution estimation by adopting a Regression-Kriging method, namely RK Model, which combines a regression model with Kriging for the residuals. RK Model was applied to a part of Seoul metropolitan area to estimate population distribution based on the residential zones. Comparative results of regression model and RK model using RMSE, MAE, and G statistics revealed that RK model could substantially improve the accuracy of population distribution. It is expected that RK model could be adopted actively for further population distribution estimation.