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...

Missing data present an important challenge when dealing with high dimensional data arranged in the form of an array. In this paper, we propose methods for estimation of the parameters of array variate normal probability model from partially observed multi-way data. The methods developed here are useful for missing data imputation, estimation of mean and covariance parameters for multi-way data...

The treatment of missing data in the social sciences has changed tremendously during the last decade. Modern missing data techniques such as multiple imputation and full-information maximum likelihood are used much more frequently. These methods assume that data are missing at random. One very common approach to increase the likelihood that missing at random is achieved, consists of including m...

One of the most important issues that confront statisticians in longitudinal studies is dropouts. A variety of reasons may lead to withdrawal from a study and produce two different missingness mechanisms, namely, missing at random and non-ignorable dropouts. Nevertheless, none of these mechanisms is tenable in most studies. In addition, it may be that not all of dropouts are nonignorable. Many ...

Dealing with missing data via parametric multiple imputation methods usually implies stating several strong assumptions about both the distribution of the data and about underlying regression relationships. If such parametric assumptions do not hold, the multiply imputed data are not appropriate and might produce inconsistent estimators and thus misleading results. In this paper, a fully nonpar...

Data fusion techniques typically aim to achieve a complete data file from different sources which do not contain the same units. Traditionally, data fusion, in the US also addressed by the term statistical matching, is done on the basis of variables common to all files. It is well known that those approaches establish conditional independence of the (specific) variables not jointly observed giv...

This paper presents the development of Ocean Color Monte Carlo simulation by means of high-performance computing techniques in the context of the Geo-Info project. The paper first outlines the Geo-Info project whose primary aim is to provide domain experts (e.g., oceanographers and geologists) with a set of software toolkits specifically designed for classes of problems in selected target appli...

This paper illustrates the macroscopic modeling and simulation of Interstate 80 Eastbound Freeway in the Bay Area. Traffic flow and occupancy data from loop detectors are used for calibrating the model and specifying the inputs to the simulation. The freeway is calibrated based on the Link-Node Cell Transmission Model and missing ramp flow data are estimated using an iterative learning-based im...

Finite sample properties of multiple imputation estimators under the linear regression model are studied. The exact bias of the multiple imputation variance estimator is presented. A method of reducing the bias is presented and simulation is used to make comparisons. We also show that the suggested method can be used for a general class of linear estimators. 1. Introduction. Multiple imputation...

Obtaining predictions from regression models fit to multiply imputed data can be challenging because treatments of multiple imputation seldom give clear guidance on how predictions can be calculated, and because available software often does not have built-in routines for performing the necessary calculations. This research note reviews how predictions can be obtained using Rubin’s rules, that ...