Parametric fractional imputation for missing data analysis

Kim, Jae Kwang

doi:10.1093/biomet/asq073

Cited by 109 publications

(124 citation statements)

References 17 publications

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“…Imputed values are not changed, only fractional weights are updated for each EM iteration. The proposed method is an application of the parametric fractional imputation of Kim (2011), but instead of assuming a parametric model for the marginal distribution of x, we used a nonparametric model. Paik (2000) proposed the same method in the context of missing covariates in logistic regression.…”

Section: Computationmentioning

confidence: 99%

“…The proposed method can be implemented using a version of the fractional imputation of Kim (2011), semiparametric fractional imputation, where the imputed values for each missing value are from observed values. Fractional weights of the imputed values are calculated by incorporating the regression model and the nonparametric maximum likelihood estimates of the covariate distribution.…”

Section: Introductionmentioning

confidence: 99%

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A semiparametric inference to regression analysis with missing covariates in survey data

Yang¹,

Kim²

2017

STAT SINICA

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Parameter estimation in parametric regression models with missing covariates is considered under a survey sampling setup. Under missingness at random, a semiparametric maximum likelihood approach is proposed which requires no parametric specification of the marginal covariate distribution. By drawing from the von Mises calculus and V-Statistics theory, we obtain an asymptotic linear representation of the semiparametric maximum likelihood estimator (SMLE) of the regression parameters, which allows for a consistent estimator of asymptotic variance. An EM algorithm for computation is then developed to implement the proposed method using fractional imputation. Simulation results suggest that the SMLE method is robust, whereas the fully parametric method is subject to severe bias under model misspecification. A rangeland study from the National Resources Inventory (NRI) is used to illustrate the practical use of the proposed methodology.

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Section: Computationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A semiparametric inference to regression analysis with missing covariates in survey data

Yang¹,

Kim²

2017

STAT SINICA

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“…Fractional imputation (FI), originally proposed by Kalton and Kish (1984) and later further studied by Kim and Fuller (2004) and Kim (2011), has surged as an attractive alternative to multiple imputation for reducing the imputation variance. It replaces each missing observation by a cluster of plausible values with each imputed value also receiving a fractional weight.…”

Section: Introductionmentioning

confidence: 99%

Fully Efficient Joint Fractional Imputation for Incomplete Bivariate Ordinal Responses

She¹,

Wu²

2018

STAT SINICA

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We propose a fully efficient joint fractional imputation method for handling bivariate ordinal responses with missing observations. We show that the method is ideally suited for bivariate ordinal responses to create a single imputed data file and provides valid and efficient inferences for the joint and marginal probabilities, association measures as well as regression analysis. Asymptotic properties of estimators based on the joint fractionally imputed data set are developed and their superiority over existing methods, including available-case analysis, propensity score adjustment and sequential regression multiple imputation methods, is demonstrated through both theoretical results and simulation studies. The proposed joint fractional imputation strategy employs modelling procedures that could be used for the sequential regression multiple imputation method but creates a single imputed data set which can be easily analyzed using existing softwares with minor modifications. Variance estimation and test of independence are also discussed under the proposed joint fractional imputation method.Statistica Sinica: Newly accepted Paper (accepted version subject to English editing) 2 XICHEN SHE AND CHANGBAO WU

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“…Parametric fractional imputation (PFI in the sequel), proposed by Kim (2011), is a frequentist version of MI, where fractional weights are assigned to the imputed values to properly represent the point mass of the imputed values.…”

Section: Introductionmentioning

confidence: 99%

Imputation methods for quantile estimation under missing at random

Yang

Kim

Shin

2013

Statistics and Its Interface

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Imputation is frequently used to handle missing data for which multiple imputation is a popular technique. We propose a fractional hot deck imputation which produces a valid variance estimator for quantiles. In the proposed method, the imputed values are chosen from the set of respondents and are assigned with proper fractional weights that use a density function for the working model. In addition, we consider a nonparametric fractional imputation method based on nonparametric kernel regression, avoiding a parametric distribution assumption and thus giving more robustness. The resulting estimator can be called nonparametric fractionally imputation estimator. Valid variance estimation is also discussed. A limited simulation study compares the proposed methods favorably with other existing methods.

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Parametric fractional imputation for missing data analysis

Cited by 109 publications

References 17 publications

A semiparametric inference to regression analysis with missing covariates in survey data

A semiparametric inference to regression analysis with missing covariates in survey data

Fully Efficient Joint Fractional Imputation for Incomplete Bivariate Ordinal Responses

Imputation methods for quantile estimation under missing at random

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