• Communications for Statistical Applications and Methods
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• 제17권6호
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• pp.899-907
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• 2010

무응답 대체(non-response imputation) 방법에 관한 많은 이론과 방법이 제안되었으며 실제 자료 분석에 이용되고 있다. 흔히 횡단면 무응답 대체를 위하여 다중대체법(multiple imputation)이 사용되고 있으며 2차년도 이상의 패널자료에는 종시점회귀대체법(cross-wave regression imputation)이 사용되고 있다. 본 연구에서는 패널자료 분석을 위하여 종시점회귀대체법의 일반형태인 시계열 대체법과 횡단면 무응답 대체법을 결합한 시계열-횡단면 다중 대체법을 제안하였다. 노동부의 매월노동통계 자료를 이용하여 제안한 방법과 기존의 종시점회귀대체법을 비교하여 우수함을 보였다.

• Journal of the Korean Statistical Society
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• 제36권3호
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• pp.423-434
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• 2007

Imputation using a regression model is a method to preserve the correlation among variables and to provide imputed point estimators. We discuss the implementation of regression imputation using fractional imputation. By a suitable choice of fractional weights, the fractional regression imputation can take the form of hot deck fractional imputation, thus no artificial values are constructed after the imputation. A variance estimator, which extends the method of Kim and Fuller (2004), is also proposed. Results from a limited simulation study are presented.

• Journal of the Korean Data and Information Science Society
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• 제14권2호
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• pp.143-152
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• 2003

A data set having missing observations is often completed by using imputed values. In this paper, performances and accuracy of five imputation procedures are evaluated when missing values exist only on the response variable in the exponential regression model. Our simulation results show that adjusted exponential regression imputation procedure can be well used to compensate for missing data, in particular, compared to other imputation procedures. An illustrative example using real data is provided.

• 한국통계학회:학술대회논문집
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• 한국통계학회 2001년도 추계학술발표회 논문집
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• pp.143-148
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• 2001

A dataset having missing observations is often completed by using imputed values. In this paper the performances and accuracy of complete case methods and four imputation procedures are evaluated when missing values exist only on the response variables in the Weibull regression model. Our simulation results show that compared to other imputation procedures, in particular, hotdeck and Weibull regression imputation procedure can be well used to compensate for missing data. In addition an illustrative real data is given.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2003년도 춘계학술대회
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• pp.135-144
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• 2003

A data set having missing observations is often completed by using imputed values. In this paper, performances and accuracy of five imputation procedures are evaluated when missing values exist only on the response variable in the exponential regression model. Our simulation results show that adjusted exponential regression imputation procedure can be well used to compensate for missing data, in particular, compared to other imputation procedures. An illustrative example using real data is provided.

• Communications for Statistical Applications and Methods
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• 제26권3호
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• pp.325-336
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• 2019

In this article we extend the discrete Weibull regression model in the presence of missing data. Discrete Weibull regression models can be adapted to various type of dispersion data however, it is not widely used. Recently Yoo (Journal of the Korean Data and Information Science Society, 30, 11-22, 2019) adapted the discrete Weibull regression model using single imputation. We extend their studies by using multiple imputation also with several various settings and compare the results. The purpose of this study is to address the merit of using multiple imputation in the presence of missing data in discrete count data. We analyzed the seventh Korean National Health and Nutrition Examination Survey (KNHANES VII), from 2016 to assess the factors influencing the variable, 1 month hospital stay, and we compared the results using discrete Weibull regression model with those of Poisson, negative Binomial and zero-inflated Poisson regression models, which are widely used in count data analyses. The results showed that the discrete Weibull regression model using multiple imputation provided the best fit. We also performed simulation studies to show the accuracy of the discrete Weibull regression using multiple imputation given both under- and over-dispersed distribution, as well as varying missing rates and sample size. Sensitivity analysis showed the influence of mis-specification and the robustness of the discrete Weibull model. Using imputation with discrete Weibull regression to analyze discrete data will increase explanatory power and is widely applicable to various types of dispersion data with a unified model.

• 한국조사연구학회지:조사연구
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• 제11권1호
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• pp.1-18
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• 2010

본 논문에서는 패널조사에서 발생하는 웨이브 무응답을 대체하는 방법을 고찰하였다. 패널조사에서는 이전 조사 데이터를 무응답 대체에 활용할 수 있기 때문에 이러한 성질을 이용하면 횡단면 무응답 대체보다 더 효과적인 웨이브 무응답 대체법을 찾을 수 있다. 먼저 웨이브 무응답 대체를 사용하는 해외의 주요 패널조사를 살펴보고, 웨이브 무응답 대체방법 중 종단면 회귀대체법, 이월대체법, 최근방 회귀대체법, 그리고 행렬대체법을 고찰하였다. 그리고 웨이브 무응답 대체법의 성능을 비교하기 위하여 한국복지패널 데이터를 대상으로 모의실험을 실시하였다. 성능을 비교하기 위하여 평균대체, 회귀대체, 비대체, 최근방 대체, 핫덱 대체를 고려하였고 성능평가 지표로는 예측 정확성 지표와 추정 정확성 지표를 이용하였다. 모의실험 결과 비대체, 행렬대체는 두 지표 모두 우수했고, 회귀대체, 종단면 회귀대체, 이월대체는 예측 정확성은 우수한 반면 추정 정확성은 다소 떨어졌으며, 반대로 최근방 회귀대체, 최근방 대체, 핫덱 대체는 예측 정확성은 떨어지나 추정 정확성은 높은 것으로 나타났다. 마지막으로 평균 대체는 두 지표 모두 좋지 않았다.

• Communications for Statistical Applications and Methods
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• 제30권6호
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• pp.577-587
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• 2023

Conventional categorical data imputation techniques, such as mode imputation, often encounter issues related to overestimation. If the variable has too many categories, multinomial logistic regression imputation method may be impossible due to computational limitations. To rectify these limitations, we propose a two-stage imputation method. During the first stage, we utilize the Boruta variable selection method on the complete dataset to identify significant variables for the target categorical variable. Then, in the second stage, we use the important variables for the target categorical variable for logistic regression to impute missing data in binary variables, polytomous regression to impute missing data in categorical variables, and predictive mean matching to impute missing data in quantitative variables. Through analysis of both asymmetric and non-normal simulated and real data, we demonstrate that the two-stage imputation method outperforms imputation methods lacking variable selection, as evidenced by accuracy measures. During the analysis of real survey data, we also demonstrate that our suggested two-stage imputation method surpasses the current imputation approach in terms of accuracy.

• Journal of information and communication convergence engineering
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• 제21권1호
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• pp.62-67
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• 2023

This paper proposes an imputation method using a bidirectional k-nearest components search based local linear regression method. The bidirectional k-nearest-components search method selects components in the dynamic range from the missing points. Unlike the existing methods, which use a fixed-size window, the proposed method can flexibly select adjacent components in an imputation problem. The weight values assigned to the components around the missing points are calculated using local linear regression. The local linear regression method is free from the rank problem in a matrix of dependent variables. In addition, it can calculate the weight values that reflect the data flow in a specific environment, such as a blackout. The original missing values were estimated from a linear combination of the components and their weights. Finally, the estimated value imputes the missing values. In the experimental results, the proposed method outperformed the existing methods when the error between the original data and imputation data was measured using MAE and RMSE.

• Communications for Statistical Applications and Methods
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• 제16권2호
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• pp.317-323
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• 2009

Recently not even government polices but small town decisions are based on the survey data/information, so the most of government agencies/organizations demand various sample surveys in each fields for more detail information. However in conducting the sample survey, nonresponse problem rises very often and it becomes a major issue on judging the accuracy of survey. For that matters, one solution ran be using the administration data. However unfortunately most of administration data are restricted to the common users. The other solution can be the imputation. Therefore several method, of imputation are studied in various fields. In this study, in stead of the simple regression imputation method which is commonly used, factor score regression method is applied specially to the incomplete data which have the unit and item misting values in survey data. Here for simulation study, Consumer Expenditure Surveys in Korea are used.