Missing data is a common feature of large data sets in general and medical data sets in particular. Depending on the goal of statistical analysis, various techniques can be used to tackle this problem. Imputation methods consist in substituting the missing values with plausible or predicted values so that the completed data can then be analysed with any chosen data mining procedure. In this wor...

An n-tuple is defined for each n-person monotonic characteristic function game, This n-tuple is an imputation when the sum of the components of it is equal to v( N). On the boundary of the set of all monotonic games" we can obtain a condition for the n-tuple being an imputation. The n-tuple belongs to the core when it is an imputation. If the sum of the components of it exceeds v( N), the kerne...

Multiple imputation has become viewed as a general solution to missing data problems in statistics. However, in order to lead to consistent asymptotically normal estimators, correct variance estimators and valid tests, the imputations must be proper. So far it seems that only Bayesian multiple imputation, i.e. using a Bayesian predictive distribution to generate the imputations, or approximatel...

The choice between weighting adjustments and imputation for handling missing survey data is generally straightforward: as a rule, weighting adjustments are used for total nonresponse and imputation is used for item nonresponses. There are, however, several situations where the choice is debatable. In general, these are situations of what might be termed partial nonresponse, where some data are ...

3 Results 6 3.1 Fully observed variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.2 Partially observed variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.3 Pairwise comparisons between methods . . . . . . . . . . . . . . . . . . . . 7 3.3.1 Comparison of bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.3.2 Comparison of precision . . . . . ...

Consider the random vector (X,Y ), where Y represents a response variable and X an explanatory variable. The response Y is subject to random right censoring, whereas X is completely observed. Let m(x) be a conditional location function of Y given X = x. In this paper we assume that m(·) belongs to some parametric class M = {mθ : θ ∈ Θ} and we propose a new method for estimating the true unknown...

Fractional regression hot deck imputation (FRHDI), suggested by J. K. Kim, imputes multiple values for each instance of a missing dependent variable. The imputed values are equal to the predicted value based on the fully observed cases plus multiple random residuals chosen from the set of empirical residuals. Fractional weights are chosen to enable variance estimation and to preserve the correl...

2015 Sensitivity analyses are commonly requested as part of the analysis of longitudinal clinical trials when data are missing. There are many ways in which such sensitivity analyses can be constructed. This article focuses on one particular approach, socalled controlled imputation. This combines two statistical ingredients, patternmixture models and multiple imputation. The aim is to assess se...