Fractional imputation using regression imputation model
نویسنده
چکیده
Consider a finite population of N elements identified by a set of indices U = {1, 2, ..., N}. Associated with each unit i in the population there is a study variable yi and a vector xi of auxiliary variables. Let A denote the set of indices for the elements in a sample selected by a set of probability rules called the sampling mechanism. Let the population quantity of interest be θN = ∑N i=1 yi or θN = N −1 ∑N i=1 yi and let θ̂n be a linear estimator of θN based on the full sample, θ̂n = ∑
منابع مشابه
Regression Fractional Hot Deck Imputation
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 afte...
متن کاملAn Empirical Comparison of Performance of the Unified Approach to Linearization of Variance Estimation after Imputation with Some Other Methods
Imputation is one of the most common methods to reduce item non_response effects. Imputation results in a complete data set, and then it is possible to use naϊve estimators. After using most of common imputation methods, mean and total (imputation estimators) are still unbiased. However their variances (imputation variances) are underestimated by naϊve variance estimators. Sampling mechanism an...
متن کاملFractional Regression Nearest Neighbor Imputation
Sample surveys typically gather information on a sample of units from a finite population and assign survey weights to the sampled units. Survey frequently have missing values for some variables for some units. Fractional regression imputation creates multiple values for each missing value by adding randomly selected empirical residuals to predicted values. Fractional imputation methods assign ...
متن کاملEffect of Reference Population Size and Imputation Methods on the Accuracy of Imputation in Pure and Mixed Populations
Imputation as a method of creating low-density chips to high-density chips has been introduced to increase the accuracy of genomic selection in animals. In the current study, to investing imputation accuracy, three populations of mixed (scenario 1), pure (scenario 2) and mixed + pure (scenario 3) were simulated using QMSim. Two methods of imputation including Beagle and Flmpute were used fo...
متن کاملImputation of parent-offspring trios and their effect on accuracy of genomic prediction using Bayesian method
The objective of this study was to evaluate the imputation accuracy of parent-offspring trios under different scenarios. By using simulated datasets, the performance Bayesian LASSO in genomic prediction was also examined. The genome consisted of 5 chromosomes and each chromosome was set as 1 Morgan length. The number of SNPs per chromosome was 10000. One hundred QTLs were randomly distributed a...
متن کاملImputation methods for quantile estimation under missing at random
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 addit...
متن کامل