ABB {LaplacesDemon}

Approximate Bayesian Bootstrap

Description

This function performs multiple imputation (MI) with the Approximate Bayesian Bootstrap (ABB) of Rubin and Schenker (1986).

Usage

ABB(X, K=1)

Arguments

X

This is a vector or matrix of data that must include both observed and missing values. When X is a matrix, missing values must occur somewhere in the set, but are not required to occur in each variable.

K

This is the number of imputations.

Details

The Approximate Bayesian Bootstrap (ABB) is a modified form of the BayesianBootstrap (Rubin, 1981) that is used for multiple imputation (MI). Imputation is a family of statistical methods for replacing missing values with estimates. Introduced by Rubin and Schenker (1986) and Rubin (1987), MI is a family of imputation methods that includes multiple estimates, and therefore includes variability of the estimates.

The data, \textbf{X}, are assumed to be independent and identically distributed (IID), contain both observed and missing values, and its missing values are assumed to be ignorable (meaning enough information is available in the data that the missingness mechanism can be ignored, if the information is used properly) and Missing Completely At Random (MCAR). When ABB is used in conjunction with a propensity score (described below), missing values may be Missing At Random (MAR).

ABB does not add auxiliary information, but performs imputation with two sampling (with replacement) steps. First, \textbf{X}^\star_{obs} is sampled from \textbf{X}_{obs}. Then, \textbf{X}^\star_{mis} is sampled from \textbf{X}^\star_{obs}. The result is a sample of the posterior predictive distribution of (\textbf{X}_{mis}|\textbf{X}_{obs}). The first sampling step is also known as hotdeck imputation, and the second sampling step changes the variance. Since auxiliary information is not included, ABB is appropriate for missing values that are ignorable and MCAR.

Auxiliary information may be included in the process of imputation by introducing a propensity score (Rosenbaum and Rubin, 1983; Rosenbaum and Rubin, 1984), which is an estimate of the probability of missingness. The propensity score is often the result of a binary logit model, where missingness is predicted as a function of other variables. The propensity scores are discretized into quantile-based groups, usually quintiles. Each quintile must have both observed and missing values. ABB is applied to each quintile. This is called within-class imputation. It is assumed that the missing mechanism depends only on the variables used to estimate the propensity score.

With K=1, ABB may be used in MCMC, such as in LaplacesDemon, more commonly along with a propensity score for missingness. MI is performed, despite K=1, because imputation occurs at each MCMC iteration. The practical advantage of this form of imputation is the ease with which it may be implemented. For example, full-likelihood imputation should perform better, but requires a chain to be updated for each missing value.

An example of a limitation of ABB with propensity scores is to consider imputing missing values of income from age in a context where age and income have a positive relationship, and where the highest incomes are missing systematically. ABB with propensity scores should impute these highest missing incomes given the highest observed ages, but is unable to infer beyond the observed data.

ABB has been extended (Parzen et al., 2005) to reduce bias, by introducing a correction factor that is applied to the MI variance estimate. This correction may be applied to output from ABB.

Value

This function returns a list with K components, one for each set of imputations. Each component contains a vector of imputations equal in length to the number of missing values in the data.

ABB does not currently return the mean of the imputations, or the between-imputation variance or within-imputation variance.

Author(s)

Statisticat, LLC [e-mail]

References

Parzen, M., Lipsitz, S.R., and Fitzmaurice, G.M. (2005). "A Note on Reducing the Bias of the Approximate Bayesian Bootstrap Imputation Variance Estimator". Biometrika, 92, 4, p. 971–974.

Rosenbaum, P.R. and Rubin, D.B. (1983). "The Central Role of the Propensity Score in Observational Studies for Causal Effects". Biometrika, 70, p. 41–55.

Rosenbaum, P.R. and Rubin, D.B. (1984). "Reducing Bias in Observational Studies Using Subclassification in the Propensity Score". Journal of the American Statistical Association, 79, p. 516–524.

Rubin, D.B. (1981). "The Bayesian Bootstrap". Annals of Statistics, 9, p. 130–134.

Rubin, D.B. (1987). "Multiple Imputation for Nonresponse in Surveys". John Wiley and Sons: New York, NY.

Rubin, D.B. and Schenker, N. (1986). "Multiple Imputation for Interval Estimation from Simple Random Samples with Ignorable Nonresponse". Journal of the American Statistical Association, 81, p. 366–374.

See Also

BayesianBootstrap, LaplacesDemon, and MISS.

Examples

library(LaplacesDemon)

### Create Data
J <- 10 #Number of variables
m <- 20 #Number of missings
N <- 50 #Number of records
mu <- runif(J, 0, 100)
sigma <- runif(J, 0, 100)
X <- matrix(0, N, J)
for (j in 1:J) X[,j] <- rnorm(N, mu[j], sigma[j])

### Create Missing Values
M1 <- rep(0, N*J)
M2 <- sample(N*J, m)
M1[M2] <- 1
M <- matrix(M1, N, J)
X <- ifelse(M == 1, NA, X)

### Approximate Bayesian Bootstrap
imp <- ABB(X, K=1)

### Replace Missing Values in X (when K=1)
X.imp <- X
X.imp[which(is.na(X.imp))] <- unlist(imp)
X.imp