Statistical matching consists in estimating the joint characteristics of two variables observed
in two distinct and independent sample surveys, respectively. In a parametric setup, ranges …

The goal of statistical matching is the estimation of a joint distribution having observed only
samples from its marginals. The lack of joint observations on the variables of interest is the …

In the era of data revolution, availability and presence of data is a huge wealth that has to be
utilized. Instead of making new surveys, benefit can be made from data that already exists …

Among the goals of statistical matching, a very important one is the estimation of the joint
distribution of variables not jointly observed in a sample survey but separately available from …

The problem of inference about the joint distribution of two categorical variables based on
knowledge or observations of their marginal distributions, to be referred to as categorical …

Statistical matching is progressively emerging as a straightforward approach to data
integration. This method of increasing importance and interest is useful to address the …

Data for statistical analysis is often available from different samples, with each sample
containing measurements on only some of the variables of interest. Statistical matching …

Abstract Big Data and the 'Internet of Things' are transforming the processes of data
collection, storage and use. The relationship between data collected first hand (primary …

The statistical matching problem involves the integration of multiple datasets where some
variables are not observed jointly. This missing data pattern leaves most statistical models …

The goal of statistical matching, at a macro level, is the estimation of the joint distribution of
variables separately observed in independent samples. The lack of joint information on the …