W e propose a remedy for the discrepancy between the way political scientists analyze data with missing values and the recommendations of the statistics community. Methodologists and statisticians agree that “multiple imputation” is a superior approach to the problem of missing data scattered through one’s explanatory and dependent variables than the methods currently used in applied data analy...

Missing values arise in many research fields and is a common problem in analysis. Unlike linear data, the methods proposed to handle missing values for circular variables have not been developed. This could be contributed to the closed form of circular variables. An imputation methods based on circular mean by column and sample circular mean are proposed for the simultaneous linear functional r...

In the online supplement we present details of additional simulations and results that have been referred to in the main text of the paper. In Appendix 1 we present results of the treatment effect estimates from the Across and Within methods in the simulations of Section 3 when matching is performed with replacement. In Appendix 2 we investigate different applications of propensity scores to es...

Variance estimation after imputation is an important practical problem in survey sampling. When deterministic imputation or stochastic imputation is used, we show that the variance of the imputed estimator can be consistently estimated by a unifying linearize and reverse approach. We provide some applications of the approach to regression imputation, fractional categorical imputation, multiple ...

BACKGROUND. In many studies with longitudinal data, time-dependent covariates can only be measured intermittently (not at all observation times), and this presents difficulties for standard statistical analyses. This situation is common in medical studies, and methods that deal with this challenge would be useful. METHODS. In this study, we performed the seemingly unrelated regression (SUR) bas...

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...

MOTIVATION Approaches to identifying new risk loci, training risk prediction models, imputing untyped variants and fine-mapping causal variants from summary statistics of genome-wide association studies are playing an increasingly important role in the human genetics community. Current summary statistics-based methods rely on global 'best guess' reference panels to model the genetic correlation...

Random Forests are commonly applied for data prediction and interpretation. The latter purpose is supported by variable importance measures that rate the relevance of predictors. Yet existing measures can not be computed when data contains missing values. Possible solutions are given by imputation methods, complete case analysis and a newly suggested importance measure. However, it is unknown t...

This paper considers the estimation of coe cients in a linear regression model with missing observations in the independent variables and intro duces a modi cation of the standard rst order regression method for imputation of missing values The modi cation provides stochastic values for imputation Asymptotic properties of the estimators for the regression coe cients arising from the proposed mo...

We describe a basic Geo-coding service encompassing a geo-parsing tool and integrated digital gazetteer service. The development of a geo-parser comes from the need to explicitly georeference large resource collections such as the Statistical Accounts of Scotland which currently only contain implicit georeferences in the form of placennames thus making such collections inherently geographically...