Background: Maternal and neonatal mortality is high in South Asia. Recent studies have identifed factors such as adolescent female fertility, healthcare spending is reducing maternal and neonatal mortality. The objective of this study is to examine the efect of adolescent female fertility and...

This paper investigates how data requirements often encountered in archival accounting research can produce a data-restricted sample that is a non-random selection of observations from the reference sample to which the researcher wishes to generalize results. We illustrate the effects of...

Outcomes in economic evaluations, such as health utilities and costs, are products of multiple variables, often requiring complete item responses to questionnaires. Therefore, missing data are very common in cost-effectiveness analyses. Multiple imputations (MI) are predominately recommended and...

The basic concept of multiple imputation is straightforward and easy to understand, but the application to real data imposes many implementation problems. To define useful imputation models for a dataset that consists of categorical and of continuous variables with distributions that are...

This dissertation focuses on two topics in semiparametric statistical methods and their applications in medical science: (1) prediction of patients? lifetimes based on their risk profiles; (2) estimation of dynamic exposure effects on survival outcomes. In Chapter II, we develop multiple...

In this paper, we present a case study of the imputation in a complex household survey - the first wave of the German Panel on Household Finances (PHF). A household wealth survey has to be built on a questionnaire with rather complex logical structure mainly because the probes of many wealth...

Questions on income in surveys are prone to two sources of errors that can cause bias if not addressed adequately at the analysis stage. On the one hand, income is considered sensitive information and response rates on income questions generally tend to be lower than response rates for other...

Multiple imputation is a method specifically designed for variance estimation in the presence of missing data. Rubin's combination formula requires that the imputation method is "proper" which essentially means that the imputations are random draws from a posterior distribution in a Bayesian...

Summary We introduce a selection model-based multilevel imputation approach to be used within the fully conditional specification framework for multiple imputation. Concretely, we apply a censored bivariate probit model to describe binary variables assumed to be missing not at random. The first...

We propose a flexible hedonic methodology for computing house price indexes that uses multiple imputation (MI) to account for missing data (a huge problem in housing data sets). Ours is the first study to use MI in this context. We also allow for spatial correlation, include interaction terms...