Statistical matching is studied inside a coherent setting, by focusing on the problem of removing inconsistencies. When structural zeros among involved variables are present, incoherencies on the parameter estimations can arise. The aim is to compare different methods to remove such incoherences based on specific pseudo-distances. The comparison is given through an exemplifying example of 100 simulations from a known population with three categorical variables, that carries out to the light peculiarities of the statistical matching problem. © Springer International Publishing Switzerland 2013
Among the goals of statistical matching, a very important one is the estimation of the joint distrib...
Researchers who generate data often optimize efficiency and robustness by choosing stratified over s...
In this paper, the difference between the data generating process and the imputation procedures used...
We deal with the statistical matching problem and in particular we study the problem related to the ...
AbstractSeveral economic applications require to consider different data sources and to integrate th...
The goal of statistical matching is the estimation of a joint distribution having observed only samp...
Statistical matching attempts at producing a unique, synthetic data file, where variables observed i...
We develop the first statistical matching micro approach reflecting the natural uncer- tainty arisi...
Although published works rarely include causal estimates from more than a few model specifications, ...
An important feature of statistical matching is the estimation of the underlying joint distribution ...
The statistical matching problem involves the integration of multiple datasets where some variables ...
AbstractThe statistical matching problem involves the integration of multiple datasets where some va...
The goal of statistical matching, at a “macro” level, is the estimation of a joint distribution hav...
Among the goals of statistical matching, a very important one is the estimation of the joint distrib...
Researchers who generate data often optimize efficiency and robustness by choosing stratified over s...
In this paper, the difference between the data generating process and the imputation procedures used...
We deal with the statistical matching problem and in particular we study the problem related to the ...
AbstractSeveral economic applications require to consider different data sources and to integrate th...
The goal of statistical matching is the estimation of a joint distribution having observed only samp...
Statistical matching attempts at producing a unique, synthetic data file, where variables observed i...
We develop the first statistical matching micro approach reflecting the natural uncer- tainty arisi...
Although published works rarely include causal estimates from more than a few model specifications, ...
An important feature of statistical matching is the estimation of the underlying joint distribution ...
The statistical matching problem involves the integration of multiple datasets where some variables ...
AbstractThe statistical matching problem involves the integration of multiple datasets where some va...
The goal of statistical matching, at a “macro” level, is the estimation of a joint distribution hav...
Among the goals of statistical matching, a very important one is the estimation of the joint distrib...
Researchers who generate data often optimize efficiency and robustness by choosing stratified over s...
In this paper, the difference between the data generating process and the imputation procedures used...