• Title/Summary/Keyword: 핫덱

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농가경제조사의 무응답 대체군 형성 방안

  • 이기재;김규성;김진
    • Proceedings of the Korean Statistical Society Conference
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    • 2004.11a
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    • pp.49-54
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    • 2004
  • 본 연구에서는 표본 농가의 교체나 무응답으로 인한 데이터의 손실을 최소화하기 위하여 핫덱방법을 적용할 때 필요한 무응답 대체군 형성 방안을 제안하였다. 농가경제조사의 무응답 현황과 특성을 살펴보고, 대체군 형성 방안들을 비교할 수 있는 측도를 제안하였다. 제안된 비교 측도를 이용해서 대체군 형성 방안들을 비교하였다.

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Weighted Hot-Deck Imputation in Farm and Fishery Household Economy Surveys (농어가경제조사에서 가중핫덱 무응답 대체법의 활용)

  • Kim Kyu-Seong;Lee Kee-Jae;Kim Jin
    • The Korean Journal of Applied Statistics
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    • v.18 no.2
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    • pp.311-328
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    • 2005
  • This paper deals with a treatment of nonresponse in farm and fishery household economy surveys in Korea. Since the samples in two surveys were selected by stratified multi-stage sampling and weighted sample means has been used to estimate the population means, we choose a weighted hot-deck imputation method as an appropriate method for two surveys. We investigate the procedure of the weighted hot-deck as well as an adjusted jackknife method for variance estimation. Through an empirical study we found that the method worked very well in both mean and variance estimation in two surveys. In addition, we presented a procedure of forming imputation class and formed four imputation classes for each survey and then compared them with analysis. As a result, we presented two most efficient imputation classes for two surveys.

Missing Value Imputation Method Using CART : For Marital Status in the Population and Housing Census (CART를 활용한 결측값 대체방법 : 인구주택총조사 혼인상태 항목을 중심으로)

  • 김영원;이주원
    • Survey Research
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    • v.4 no.2
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    • pp.1-21
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    • 2003
  • We proposed imputation strategies for marital status in the Population and Housing Census 2000 in Korea to illustrate the effective missing value imputation methods for social survey. The marital status which have relatively high non-response rates in the Census are considered to develope the effective missing value imputation procedures. The Classification and Regression Tree(CART)is employed to construct the imputation cells for hot-deck imputation, as well as to predict the missing value by model-based approach. We compare to imputation methods which include the CART model-based imputation and the sequential hot-deck imputation based on CART. Also we check whether different modeling for each region provides the more improved results. The results suggest that the proposed hot-deck imputation based on CART is very efficient and strongly recommendable. And the results show that different modeling for each region is not necessary.

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An Imputation for Nonresponses in the Survey on the Rural Living Indicators (농촌생활지표조사에서 무응답 대체 : 사례)

  • Cho, Young-Sook;Chun, Young-Min;Hwang, Dae-Yong
    • The Korean Journal of Applied Statistics
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    • v.21 no.1
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    • pp.95-107
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    • 2008
  • Survey on the rural living indicators was the statistic approved from National Statistical Office and the survey executed by rural resources development institute. This study was used the raw data of survey on the rural living indicators in 2005. After editing procedure for raw data, we were studied 1,582 households which is acquired through elimination of case included nonresponses, and imputed a nonresponses of 15 item selected from 146 item. The imputation methods and efficiency of imputation for simulation was adapted differently from type of data. For continuous data, we imputed the nonresponses with mean imputation, regression imputation, adjusted grey-based k-NN imputation(DU, DW, WU, WW) and compared the results with RMSE. For categorical data, we imputed the nonresponses with mode method, probability imputation, conditional mode method, conditional probability method, hot-deck imputation, and compared the results with Accuracy. By the results, regression imputation and adjusted grey-based k-NN imputation appropriated for continuous data and hot-deck imputation appropriated for categorical data.

Comparisons of Imputation Methods for Wave Nonresponse in Panel Surveys (패널조사 웨이브 무응답의 대체방법 비교)

  • Kim, Kyu-Seong;Park, In-Ho
    • 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.

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A Study on Missing Data Imputation for Water Demand in 112 Block of Yoengjong Island, Korea (영종도 112블록 AMI 물 수요량 결측 자료 보정기법 연구)

  • Koo, Kang Min;Han, Kuk Heon;Yum, Kyung Taek;Jun, Kyung Soo
    • Proceedings of the Korea Water Resources Association Conference
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    • 2019.05a
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    • pp.3-3
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    • 2019
  • 최근 기후변화로 인한 집중호우, 가뭄 등 예측하기 어려운 사태가 발생하면서 깨끗하고 안정적인 용수공급 기술의 필요성이 대두되고 있다. 이에 IoT와 기존 물관리시스템을 결합한 스마트워터그리드 출범은 실시간으로 수요와 공급량의 정보를 취득하여 물 관리 효율성을 제고 할 수 있게 되었다. 실시간 수요량 자료를 이용하여 물 수요량 예측을 통한 최적의 물 공급량을 결정할 수 있다. 이 때 스마트워터그리드의 핵심 기술은 실시간으로 취득한 자료의 품질관리라 할 수 있다. 본 연구 대상지역인 영종도 112 블록에는 528개 AMI 스마트 미터를 이용하여 1시간 단위의 물 수요량 자료를 원격 검침하고 있다. 각 수용가에 설치된 AMI 센서를 통해 수집된 자료에는 오류를 포함할 수 있는데 통신 장애, 미터기 고장 및 교체 등으로 발생된다. 결측된 수요량 자료는 상수관망 수리해석에 사용되는 기본자료로서 비표본오차를 증가시켜 검정력과 정확성을 결여시키는 문제가 있다. 이에 본 연구에서는 수집된 자료를 가용할 수 있는 자료로 정제하고 대체하기 위해 완전히 관찰된 자료(complete data)만을 이용하여 각 시간에 따른 관경별, 용도별 그리고 요일별 수요패턴을 추정한다. 결측된 자료는 기존에 사용되는 평균대체법과 핫덱 대체(hot deck imputation) 등과 비교 검증한다.

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Comparison of imputation methods for item nonresponses in a panel study (패널자료에서의 항목무응답 대체 방법 비교)

  • Lee, Hyejung;Song, Juwon
    • The Korean Journal of Applied Statistics
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    • v.30 no.3
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    • pp.377-390
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    • 2017
  • When conducting a survey, item nonresponse occurs if the respondent does not respond to some items. Since analysis based only on completely observed data may cause biased results, imputation is often conducted to analyze data in its complete form. The panel study is a survey method that examines changes of responses over time. In panel studies, there has been a preference for using information from response values of previous waves when the imputation of item nonresponses is performed; however, limited research has been conducted to support this preference. Therefore, this study compares the performance of imputation methods according to whether or not information from previous waves is utilized in the panel study. Among imputation methods that utilize information from previous responses, we consider ratio imputation, imputation based on the linear mixed model, and imputation based on the Bayesian linear mixed model approach. We compare the results from these methods against the results of methods that do not use information from previous responses, such as mean imputation and hot deck imputation. Simulation results show that imputation based on the Bayesian linear mixed model performs best and yields small biases and high coverage rates of the 95% confidence interval even at higher nonresponse rates.

The Validity Test of Statistical Matching Simulation Using the Data of Korea Venture Firms and Korea Innovation Survey (벤처기업정밀실태조사와 한국기업혁신조사 데이터를 활용한 통계적 매칭의 타당성 검증)

  • An, Kyungmin;Lee, Young-Chan
    • Knowledge Management Research
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    • v.24 no.1
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    • pp.245-271
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    • 2023
  • The change to the data economy requires a new analysis beyond ordinary research in the management field. Data matching refers to a technique or processing method that combines data sets collected from different samples with the same population. In this study, statistical matching was performed using random hotdeck and Mahalanobis distance functions using 2020 Survey of Korea Venture Firms and 2020 Korea Innovation Survey datas. Among the variables used for statistical matching simulation, the industry and the number of workers were set to be completely consistent, and region, business power, listed market, and sales were set as common variables. Simulation verification was confirmed by mean test and kernel density. As a result of the analysis, it was confirmed that statistical matching was appropriate because there was a difference in the average test, but a similar pattern was shown in the kernel density. This result attempted to expand the spectrum of the research method by experimenting with a data matching research methodology that has not been sufficiently attempted in the management field, and suggests implications in terms of data utilization and diversity.