• Title, Summary, Keyword: imputation

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Non-Response Imputation for Panel Data (패널자료의 무응답 대체법)

  • Pak, Gi-Deok;Shin, Key-Il
    • Communications for Statistical Applications and Methods
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    • v.17 no.6
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    • pp.899-907
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    • 2010
  • Several non-response imputation methods are suggested, however, mainly cross-sectional imputations are studied and applied to this analysis. A simple and common imputation method for panel data is the cross-wave regression imputation or carry-over imputation as a special case of cross-wave regression imputation. This study suggests a multiple imputation method combined time series analysis and cross-sectional multiple imputation method. We compare this method and the cross-wave regression imputation method using MSE, MAE, and Bias. The 2008 monthly labor survey data is used for this study.

Imputation Methods for the Population and Housing Census 2000 in Korea

  • Kim, Young-Won;Ryu, Jeabok;Park, Jinwoo;Lee, Jaewon
    • Communications for Statistical Applications and Methods
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    • v.10 no.2
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    • pp.575-583
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    • 2003
  • We proposed imputation strategies for the Population and Housing Census 2000 in Korea. The total area of floor space and 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 missing value by model-based approach. We compare three imputation methods which include CART model-based imputation, hot-deck imputation based on CART and logical hot-deck imputation proposed by The Korea National Statistical Office. The results suggest that the proposed hot-deck imputation based on CART is very efficient and strongly recommendable.

A Computational Intelligence Based Online Data Imputation Method: An Application For Banking

  • Nishanth, Kancherla Jonah;Ravi, Vadlamani
    • Journal of Information Processing Systems
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    • v.9 no.4
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    • pp.633-650
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    • 2013
  • All the imputation techniques proposed so far in literature for data imputation are offline techniques as they require a number of iterations to learn the characteristics of data during training and they also consume a lot of computational time. Hence, these techniques are not suitable for applications that require the imputation to be performed on demand and near real-time. The paper proposes a computational intelligence based architecture for online data imputation and extended versions of an existing offline data imputation method as well. The proposed online imputation technique has 2 stages. In stage 1, Evolving Clustering Method (ECM) is used to replace the missing values with cluster centers, as part of the local learning strategy. Stage 2 refines the resultant approximate values using a General Regression Neural Network (GRNN) as part of the global approximation strategy. We also propose extended versions of an existing offline imputation technique. The offline imputation techniques employ K-Means or K-Medoids and Multi Layer Perceptron (MLP)or GRNN in Stage-1and Stage-2respectively. Several experiments were conducted on 8benchmark datasets and 4 bank related datasets to assess the effectiveness of the proposed online and offline imputation techniques. In terms of Mean Absolute Percentage Error (MAPE), the results indicate that the difference between the proposed best offline imputation method viz., K-Medoids+GRNN and the proposed online imputation method viz., ECM+GRNN is statistically insignificant at a 1% level of significance. Consequently, the proposed online technique, being less expensive and faster, can be employed for imputation instead of the existing and proposed offline imputation techniques. This is the significant outcome of the study. Furthermore, GRNN in stage-2 uniformly reduced MAPE values in both offline and online imputation methods on all datasets.

Jackknife Variance Estimation under Imputation for Nonrandom Nonresponse with Follow-ups

  • Park, Jinwoo
    • Journal of the Korean Statistical Society
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    • v.29 no.4
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    • pp.385-394
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    • 2000
  • Jackknife variance estimation based on adjusted imputed values when nonresponse is nonrandom and follow-up data are available for a subsample of nonrespondents is provided. Both hot-deck and ratio imputation method are considered as imputation method. The performance of the proposed variance estimator under nonrandom response mechanism is investigated through numerical simulation.

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REGRESSION FRACTIONAL HOT DECK IMPUTATION

  • Kim, Jae-Kwang
    • Journal of the Korean Statistical Society
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    • v.36 no.3
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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.

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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Imputation Procedures in Weibull Regression Analysis in the presence of missing values

  • Kim Soon-kwi;Jeong Bong-Bin
    • Proceedings of the Korean Statistical Society Conference
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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.

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Imputation Procedures in Exponential Regression Analysis in the presence of missing values

  • Park, Young-Sool
    • 한국데이터정보과학회:학술대회논문집
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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.

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Fully Efficient Fractional Imputation for Incomplete Contingency Tables

  • Kang, Shin-Soo
    • Journal of the Korean Data and Information Science Society
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    • v.15 no.4
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    • pp.993-1002
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    • 2004
  • Imputation procedures such as fully efficient fractional imputation(FEFI) or multiple imputation(MI) can be used to construct complete contingency tables from samples with partially classified responses. Variances of FEFI estimators of population proportions are derived. Simulation results, when data are missing completely at random, reveal that FEFI provides more efficient estimates of population than either multiple imputation(MI) based on data augmentation or complete case analysis, but neither FEFI nor MI provides an improvement over complete-case(CC) analysis with respect to accuracy of estimation of some parameters for association between two variables like $\theta_{i+}\theta_{+i}-\theta_{ij}$ and log odds-ratio.

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Comparative Study on Imputation Procedures in Exponential Regression Model with missing values

  • Park, Young-Sool;Kim, Soon-Kwi
    • Journal of the Korean Data and Information Science Society
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    • v.14 no.2
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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.

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