• Survey Research
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• v.11 no.1
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• pp.1-18
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• 2010

We compare various imputation methods for compensating wave nonresponse that are commonly adopted in many panel surveys. Unlike the cross-sectional survey, the panel survey is involved a time-effect in nonresponse in a sense that nonresponse may happen for some but not all waves. Thus, responses in neighboring waves can be used as powerful predictors for imputing wave nonresponse such as in longitudinal regression imputation, carry-over imputation, nearest neighborhood regression imputation and row-column imputation method. For comparison, we carry out a simulation study on a few income data from the Korean Welfare Panel Study based on two performance criteria: predictive accuracy and estimation accuracy. Our simulation shows that the ratio and row-column imputation methods are much more effective in terms of both criteria. Regression, longitudinal regression and carry-over imputation methods performed better in predictive accuracy, but less in estimation accuracy. On the other hand, nearest neighborhood, nearest neighbor regression and hot-deck imputation show higher performance in estimation accuracy but lower predictive accuracy. Finally, the mean imputation shows much lower performance in both criteria.

• Survey Research
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• v.12 no.3
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• pp.49-62
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• 2011

In this paper we investigate design-based properties of both the ordinary least square estimator and the weighted least square estimator for regression coefficients in panel regression model. We derive formulas of approximate bias, variance and mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator after linearization of least square estimators. Also we compare their magnitudes each other numerically through a simulation study. We consider a three years data of Korean Welfare Panel Study as a finite population and take household income as a dependent variable and choose 7 exploratory variables related household as independent variables in panel regression model. Then we calculate approximate bias, variance, mean square error for the ordinary least square estimator and approximate variance for the weighted least square estimator based on several sample sizes from 50 to 1,000 by 50. Through the simulation study we found some tendencies as follows. First, the mean square error of the ordinary least square estimator is getting larger than the variance of the weighted least square estimator as sample sizes increase. Next, the magnitude of mean square error of the ordinary least square estimator is depending on the magnitude of the bias of the estimator, which is large when the bias is large. Finally, with regard to approximate variance, variances of the ordinary least square estimator are smaller than those of the weighted least square estimator in many cases in the simulation.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2010.09a
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• pp.134-141
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• 2010

임분 단위의 재적 및 생체량은 LiDAR 자료의 높이 분포변수들로부터 추정될 수 있다. LiDAR 자료의 높이 분포변수들은 재적을 측정하는 임분고(stand height)와 임분평균 지하고(mean crown base height), 그리고 수관형태에 따른 평균수관장(mean crown depth) 등의 변수와 직 간접적인 연관성이 있다. 그러므로, 본 연구에서는 잣나무림의 샘플지역에서 반사된 LiDAR 자료의 높이분포변수를 이용하여 임분단위의 수간재적을 추정한 다음, 앞 세부연구에서 수행한 방법을 이용하여 임분의 생체량을 추정하였다. 변수는 임분 내에서 반사되는 LiDAR 자료의 평균높이, 최대 최소높이, 높이값들의 표준편차, 변이계수, 첨도, 왜도, 식생반사비율, 10분위 높이자료와 강도데이터의 기술통계량 등을 사용하였다. 그리고, 최종적인 임분수간재적은 다중회귀분석을 통하여 수행되었다. 다중회귀분석을 통하여 각 변수들은 임분수간재적과 가장 관련있는 2~3개의 변수들로 추려졌으며, 추정된 회귀식의 결정계수는 0.66으로 분석되었다. 또한 유보표본을 이용하여 검증한 결과의 결정계수는 0.59로 분석되어 LiDAR 자료의 높이분포변수들은 임분의 재적을 비교적 잘 설명할 수 있음이 밝혀졌다.

• Communications for Statistical Applications and Methods
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• v.19 no.1
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• pp.107-115
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• 2012

Yoo and Cook (2007) developed an optimal sufficient dimension reduction methodology for the conditional mean in multivariate regression and it is known that their method is asymptotically optimal and its test statistic has a chi-squared distribution asymptotically under the null hypothesis. To check the effect of dimension used in estimation on regression coefficients and the explanatory power of the conditional mean model in multivariate regression, we applied their method to several simulated data sets with various dimensions. A small simulation study showed that it is quite helpful to search for an appropriate dimension for a given data set if we use the asymptotic test for the dimension as well as results from the estimation with several dimensions simultaneously.

• Journal of the Korean Data and Information Science Society
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• v.20 no.5
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• pp.857-867
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• 2009

The purpose of this paper is to suggest the methods of forecasting for the number of the elementary, middle and high-school student based on the proportion of promotion until 2026 year. The suggested methods are the proportion of promotion, mov baseverage, Holt-W bters model, SARIMA, regression fit. As the result, the abilities of forecasting by the method of moving average are better than those of other methods.

• The Korean Journal of Applied Statistics
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• v.21 no.3
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• pp.537-546
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• 2008

One of main goals of time series analysis is to estimate prediction of future values. In this paper, we investigate the bias problem when the transformation and back- transformation approach is applied in ARMA models and introduce a modified smearing estimation to reduce the bias. An empirical study on the returns of KOSDAQ index via Yeo-Johnson transformation was executed to compare the performance of existing methods and proposed methods and showed that proposed approaches provide a bias-reduced estimation of the prediction value.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.307-307
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• 2011

ARF(Areal Reduction Factor, 면적우량감소계수)는 지점강우량을 면적 평균 강우량으로 변환하는 환산계수로 정의되며, 유역의 지형학적 특성과 강우의 공간적 분포특성을 반영한 유역단위의 ARF의 개발이 요구된다. 하지만 국내의 ARF는 대부분 한강유역을 대상으로 하고 있어 한강유역과 지형학적, 수문 기상학적 특징이 상이한 유역에 대하여 연구 결과를 적용하기는 많은 제약이 따를 것으로 판단된다. 따라서 본 연구에서는 남강댐 유역의 ARF를 산정하기 위해 7개의 강우관측소(산청, 삼가, 신안, 안의, 운봉, 태수, 함양)로부터 시강우자료(1990년~2010년)를 수집한 후 14개의 재현기간, 6개의 지속시간에 대한 지수형 ARF 회귀식을 산정하였다. 그 결과 남강댐 유역의 지수형 ARF 회귀식의 결정계수는 0.80~0.99로 높은 상관성을 나타내었다. 그리고 남강댐 유역의 ARF와 첨두홍수량의 관계를 분석하기 위해 남강댐 유역내의 산청유역을 대상으로 재현기간 100년, 지속시간 24시간에 대한 홍수량을 모의하였다. 그 결과 ARF의 적용 전 후의 첨두홍수량은 10% 이상 감소하는 것으로 나타났다. 따라서 남강댐 유역의 기상학적 특성을 고려한 첨두홍수량 산정을 위해서는 본 연구에서 제안한 ARF 회귀식이 유용할 것으로 판단된다.

• Journal of the Korean Data and Information Science Society
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• v.22 no.4
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• pp.717-726
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• 2011

The present study was carried out to summarize the effect of green tea in the diabetic rats by meta-analysis related studies. The association measure to test effect of green tea was Hedges' standardized mean difference. In this particular fixed effect model, body weight was significantly increased. Also, blood glucose, triglycerides were significantly decreased. In this case of heterogeneous variable, random effect model was applied. In this model, body weight was significantly increased. Also, blood glucose was significantly decreased in green tea treated group. According to the Meta-regression analysis, duration of injection was not significant for variables.

• 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.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.123-132
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• 2016

We can achieve the principle of parsimony and efficiency if homogeneity for panel time series model is satisfied. We suggest a Rao test statistic and a Wald test statistic for the test of homogeneity for panel AR(1) and derived the limit distribution. We performed a simulation to examine statistics with the same chisquare distribution when number of the individual is small and in common with large. We also simulated to compare the empirical power of the statistics in a small panel. In application, we fit panel AR(1) model using regional monthly economical active population data and test homogeneity for panel AR(1). It is satisfied homogeneity, so it could be fitted AR(1) using the sample mean at the time point. We also compare the power of prediction between each individual and pooled model.