Nonparametric imputation method for nonresponse in surveys

Nonparametric imputation method for nonresponse in surveys

Many imputation methods are based on statistical models that assume that the variable of interest is a noisy observation of a function of the auxiliary variables or covariates. Misspecification of this model may lead to severe errors in estimates and to misleading conclusions. A new imputation method for item nonresponse in surveys is proposed based on a nonparametric estimation of the function...

Designing Surveys Acknowledging Nonresponse

Nonresponse Rates and Nonresponse Bias In Household Surveys

Many surveys of the U.S. household population are experiencing higher refusal rates. Nonresponse can, but need not, induce nonresponse bias in survey estimates. Recent empirical findings illustrate cases when the linkage between nonresponse rates and nonresponse biases is absent. Despite this, professional standards continue to urge high response rates. Statistical expressions of nonresponse bi...

Nonparametric Bayesian Multiple Imputation for Incomplete Categorical Variables in Large-Scale Assessment Surveys

In many surveys, the data comprise a large number of categorical variables that suffer from item nonresponse. Standard methods for multiple imputation, like log-linear models or sequential regression imputation, can fail to capture complex dependencies and can be difficult to implement effectively in high dimensions. We present a fully Bayesian, joint modeling approach to multiple imputation fo...

Di erential Nonresponse in Sample Surveys

The standard analysis of unit nonresponse in sample surveys is to assume missing at random| that is, that the probability a person responds is independent of their response to the question of interest, y, conditional on fully-observed covariates x or on sampling weights w. In this paper, we discuss weakening these assumptions without the use of additional covariates in the special case of a bin...