A Bayesian Approach to Parameter Estimation in the Presence of Spatial Missing Data

The missing data problem has been widely addressed in the literature. The traditional methods for handling missing data may be not suited to spatial data, which can exhibit distinctive structures of dependence and/or heterogeneity. As a possible solution to the spatial missing data problem, this paper proposes an approach that combines the Bayesian Interpolation method [Benedetti, R. & Palma, D. (1994) Markov random field-based image subsampling method, Journal of Applied Statistics, 21(5), 495–509] with a multiple imputation procedure. The method is developed in a univariate and a multivariate framework, and its performance is evaluated through an empirical illustration based on data related to labour productivity in European regions.     

Methods for the Analysis of Explanatory Linear Regression Models with Missing Data Not at Random

Since the work of Little and Rubin (1987) not substantial advances in the analysisof explanatory regression models for incomplete data with missing not at randomhave been achieved, mainly due to the difficulty of verifying the randomness ofthe unknown data. In practice, the analysis of nonrandom missing data is donewith techniques designed for datasets with random or completely random missingdata, as complete case analysis, mean imputation, regression imputation, maximumlikelihood or multiple imputation. However, the data conditions required to minimizethe bias derived from an incorrect analysis have not been fully determined. In thepresent work, several Monte Carlo simulations have been carried out to establishthe best strategy of analysis for random missing data applicable in datasets withnonrandom missing data. The factors involved in simulations are sample size,percentage of missing data, predictive power of the imputation model and existenceof interaction between predictors. The results show that the smallest bias is obtainedwith maximum likelihood and multiple imputation techniques, although with lowpercentages of missing data, absence of interaction and high predictive power ofthe imputation model (frequent data structures in research on child and adolescentpsychopathology) acceptable results are obtained with the simplest regression imputation.     

Utilising Recent Advancements in Techniques for the Analysis of Incomplete Multivariate Data to Improve the Data Quality Management of Current Academic Research

This paper discusses the importance of managing data quality in academic research in its relation to satisfying the customer. This focus is on the data completeness objectivedimension of data quality in relation to recent advancements which have been made in the development of methods for analysing incomplete multivariate data. An overview and comparison of the traditional techniques with the recent advancements are provided. Multiple imputation is also discussed as a method of analysing incomplete multivariate data, which can potentially reduce some of the biases which can occur from using some of the traditional techniques. Despite these recent advancements in the analysis of incomplete multivariate data, evidence is presented which shows that researchers are not using these techniques to manage the data quality of their current research across a variety of academic disciplines. An analysis is then provided as to why these techniques have not been adopted along with suggestions to improve the frequency of their use in the future. Source-Reference. The ideas for this paper originated from research work on David J. Fogarty's Ph.D. dissertation. The subject area is the use of advanced techniques for the imputation of incomplete multivariate data on corporate data warehouses.