Add to ORKGImputation 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 θ i+ θ + j- θ ij and log odds-ratio
The application of multiple imputation (MI) techniques as a preliminary step to handle missing value...
Multiple imputation (MI) is increasingly used for handling missing data in medical research. The sta...
Missing data commonly occur in large epidemiologic studies. Ignoring incompleteness or handling the ...
Sample surveys typically gather information on a sample of units from a finite population and assign...
Fractional imputation (FI) is a relatively new method of imputation for handling item nonresponse in...
Multiple imputation (MI) has become the most popular approach in handling missing data. Closely asso...
Abstract Multiple imputation (MI) and full information maximum likelihood (FIML) are the two most co...
While Multiple Imputation (MI) has become one of the most broadly used methods for handling incomple...
iAbstract We consider estimating the cell probabilities and testing hypotheses in a two-way continge...
Maximum likelihood estimate(MLE) is obtained from the partial log-likelihood function for the cell p...
Analysis of matched case-control studies is often complicated by missing data on covariates. Analysi...
A simple multiple imputation-based method is proposed to deal with missing data in exploratory facto...
The problem of missingness in observational data is ubiquitous. When the confounders are missing at ...
We describe and illustrate approaches to Bayesian inference in partially observed contingency tables...
Multivariable fractional polynomial (MFP) models are commonly used in medical research. The datasets...
The application of multiple imputation (MI) techniques as a preliminary step to handle missing value...
Multiple imputation (MI) is increasingly used for handling missing data in medical research. The sta...
Missing data commonly occur in large epidemiologic studies. Ignoring incompleteness or handling the ...
Sample surveys typically gather information on a sample of units from a finite population and assign...
Fractional imputation (FI) is a relatively new method of imputation for handling item nonresponse in...
Multiple imputation (MI) has become the most popular approach in handling missing data. Closely asso...
Abstract Multiple imputation (MI) and full information maximum likelihood (FIML) are the two most co...
While Multiple Imputation (MI) has become one of the most broadly used methods for handling incomple...
iAbstract We consider estimating the cell probabilities and testing hypotheses in a two-way continge...
Maximum likelihood estimate(MLE) is obtained from the partial log-likelihood function for the cell p...
Analysis of matched case-control studies is often complicated by missing data on covariates. Analysi...
A simple multiple imputation-based method is proposed to deal with missing data in exploratory facto...
The problem of missingness in observational data is ubiquitous. When the confounders are missing at ...
We describe and illustrate approaches to Bayesian inference in partially observed contingency tables...
Multivariable fractional polynomial (MFP) models are commonly used in medical research. The datasets...
The application of multiple imputation (MI) techniques as a preliminary step to handle missing value...
Multiple imputation (MI) is increasingly used for handling missing data in medical research. The sta...
Missing data commonly occur in large epidemiologic studies. Ignoring incompleteness or handling the ...