• Proceedings of the Korean Association for Survey Research Conference
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• 2001.11a
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• pp.77-85
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• 2001

In this paper we study the calibration estimator and its variance estimator for the population total using a bootstrap method according to the levels of an auxiliary information having strong correlation with an interested variable in nonresponse situation. At this point, we find tire calibration estimator in case of auxiliary information for population and sample, and then we drive the bootstrap variance estimator of it. By simulation study we compare the efficiencies with the Taylor and Jackknife variance estimators.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.13-18
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• 2002

이중추출(two-phase)접근방법 이용의 주목적은 관심변수와 보조변수사이의 관계를 이용해서 더 좋은 추정을 하고자 하는 것이다. 특히 이 방법은 층화, 무응답 문제에 적용하는 경우 상당히 효과적이다. 본 논문에서는 무시할 수 있는 무응답이 발생했을 때 이중추출기법을 이용해서 g-가중치와 응답확률을 각 단계별로 조정해줌으로써 무응답 보정추정량과 분산추정량을 구했다.

• Proceedings of the Korean Statistical Society Conference
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• 2001.11a
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• pp.63-68
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• 2001

조사과정에서 필연적으로 발생하는 무응답을 보정하기 위해 보조정보를 사용한다. 이 때, 이용 가능한 보조정보의 차원이 크면, 계산과정에서 많은 시간이 소요되며 데이터를 다루기가 매우 어렵다. 또한 추정량의 분산이 보조정보의 차원에 의존하기 때문에 과소추정의 문제가 발생한다. 이러한 문제를 해결하기 위해 무응답 보정에서 적절한 보조정보의 선택 방법을 제안하였고, 이에 대한 효율성을 모의실험을 통해 살펴보았다.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.281-290
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• 2007

Linear estimator, a weighted sum of the sample observation, is commonly adopted to estimate the finite population parameters such as population totals in survey sampling. The weight for a sampled unit is often constructed by multiplying the base weight, which is the inverse of the first-order inclusion probability, by an adjustment term that takes into account of the auxiliary information obtained throughout the population. The linear estimator using the weight adjustment is often more efficient than the one using only the bare weight, but its valiance estimation is more complicated. We discuss variance estimation for a general class of weight-adjusted estimator. By identifying that the weight-adjusted estimator can be viewed as a function of estimated nuisance parameters, where the nuisance parameters were used to incorporate the auxiliary information, we derive a linearization of the weight-adjusted estimator using a Taylor expansion. The method proposed here is quite general and can be applied to wide class of the weight-adjusted estimators. Some examples and results from a simulation study are presented.

• The Korean Journal of Applied Statistics
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• v.36 no.6
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• pp.547-559
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• 2023

Proper handling of nonresponse in sample survey improves the accuracy of the parameter estimation. Various studies have been conducted to properly handle MAR (missing at random) nonresponse or MCAR (missing completely at random) nonresponse. When nonresponse occurs, the PSA (propensity score adjusted) estimator is commonly used as a mean estimator. The PSA estimator is known to be unbiased when known sample weights and properly estimated response probabilities are used. However, for MNAR (missing not at random) nonresponse, which is affected by the value of the study variable, since it is very difficult to obtain accurate response probabilities, bias may occur in the PSA estimator. Chung and Shin (2017, 2022) proposed a post-stratification method to improve the accuracy of mean estimation when MNAR nonresponse occurs under a non-informative sample design. In this study, we propose a double post-stratification method to improve the accuracy of the naive PSA estimator for MNAR nonresponse under an informative sample design. In addition, we perform simulation studies to confirm the superiority of the proposed method.

• The Korean Journal of Applied Statistics
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• v.16 no.1
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• pp.33-44
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• 2003

Typically, it should be use auxiliary variable for calibrating the survey nonreponse in census or sampling survey. Where, if the dimension of auxiliary information is large, then it nay be spend a lot of computing time, and difficult to handle data set. Also because the variance estimator depends on the dimension of auxiliary variables, the variance estimator becomes underestimator. To deal with this problem, we propose the variable selection methods for calibration estimation procedure in unit nonreponse situation and we compare the efficiency by simulation study.

• Survey Research
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• v.5 no.2
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• pp.49-70
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• 2004

This paper is a broad review about general regression estimators, which are very useful when auxiliary variables are available in survey sampling. We investigate the process of development of general regression estimators from birth to suggestion of variance estimation method and examine some properties of general regression estimators by comparing with calibration and QR estimators. We also present some forms of general regression estimators available under complex sampling designs such as stratified sampling and cluster sampling. Finally, we comment some advantages as well as disadvantages of general regression estimators and theoretical and practical development in the future.

• Journal of Korean Society of Rural Planning
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• v.9 no.4 s.21
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• pp.59-64
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• 2003

U.S. EPA의 BASINS (Better Assessment Science Integrating Point and Nonpoint Sources)에 통합되어 있는 HSPF (Hydrologic Simulation Program-Fortran)와 SWAT (Soil and Water Assessment Tool) 모형을 이용하여 Polecat Creek 유역의 유출과 유사량을 모의하였다. 모형의 보정을 위하여 1996년 9월부터 2000년 6월까지의 하천 유량 및 유사 농도 자료를 이용하였으며, 1994년 10월부터 1995년 12월까지의 관측자료를 이용하여 모형의 검정을 실시하였다. HSPF 모형에 의해 추정된 연 평균 유출량의 상대오차는 보정 및 검정기간에 각각 0.8%, 0.5%이었으며, S WAT 모형에 의해 추정된 연평균 유출량은 실측치와 각각 2.1%, 16.1%의 오차를 보였다. 연 평균 유사량을 비교하면, HSPF 모형이 보정 및 검정기 간에 각각 8.8%와 7.2%의 오차를 보인 반면에 SWAT 모형은 각각 40.0%, 188.4%의 차이를 보였다. HSPF 모형에 의해 추정된 월 평균 유출량 및 유사량의 상관계수는 보정기간에 대하여 0.94와 0.52이었으며, SWAT 모형에 의한 결과는 상관계수가 각각 0.84와 0.39이었다. 이상의 연구 결과에 의하면, HSPF 모형이 SWAT 모형보다 유출과 유사량을 관측치와 유사하게 모의함을 알 수 있었다. 하지만 입력 자료의 구축 및 모형의 적용에는 SWAT모형보다 많은 시간과 노력을 필요로 하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.2
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• pp.10-20
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• 2012

This study shows how to estimate AADT(Annual Average Daily Traffic) on temporary count data using new grouping method. This study deals with clustering permanent traffic counts using monthly adjustment factor, daily adjustment factor and a percentage of hourly volume. This study uses a percentage of hourly volume comparing with other studies. Cluster analysis is used and 5 groups is suitable. First, make average of monthly adjustment factor, average of daily adjustment factor, a percentage of hourly volume for each group. Next estimate AADT using 24 hour volume(not holiday) and two adjustment factors. Goodness of fit test is used to find what groups are applicable. MAPE(Mean Absolute Percentage Error) is 8.7% in this method. It is under 1.5% comparing with other method(using adjustment factors in same section). This method is better than other studies because it can apply all temporary counts data.

• Water for future
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• v.26 no.3
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• pp.47-61
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• 1993

A menu-driven shell program DSFS (Daily Streamflow Simulation Model), that can process the input data, optimize the parameters, execute the program, and graphically display the results of a modified tank model, was developed and applied to simulating daily streamflow from small watersheds. The model defines daily watershed evapotranspiration losses from potential values multiplied by monthly landuse coefficients and correction factors for soil water storage levels. The parameters were calibrated using observed hydrologic data for fifteen watersheds, and the results were correlated with watershed parameters to define empirical relationships. The proposed model was tested with streamflow data of ungaged conditions, and the simulation results overestimated the annual runoff.