This paper stresses the links that exist between concepts that are used in the theory of model reduction and concepts that arise in the missing data literature. This connection motivates the extension of the missing at random (MAR) and the missing completely at random (MCAR) concepts from a static setting, as introduced by Rubin (1976), to the case of dynamic panel data models. Using this extension of the MAR and MCAR definitions, we emphasize the limits of some tests and procedures, proposed by Little (1988), Diggle (1989), Park and Davis (1993), Taris (1996) and others, to verify the ignorability of the missing data mechanism.dynamic panel model, attrition, non-response, missing at random, missing completely at random, statistical model r...
The analysis of incomplete contingency tables is a practical and an interesting problem. In this pap...
Abstract: When data are missing due to at most one cause from some time to next time, we can make sa...
Models for incomplete longitudinal data under missingness not at random have gained some popularity....
Abstract: This paper discusses identification, estimation and testing in panel data models with att...
This paper provides further insight into the key concept of missing at random (MAR) in incomplete da...
Over the last decade a variety of models to analyse incomplete multivariate and longitudinal data ha...
Longitudinal data are collected over several time periods for the same units and therefore allow for...
We review some issues related to the implications of different missing data mechanisms on statistica...
We analyse the finite sample properties of maximum likelihood estimators for dynamic panel data mode...
When data are incomplete, models are often catalogued according to one of the three modelling framew...
In this paper, we carry out an in-depth theoretical investigation for inference with missing respons...
The otherwise straightforward analysis of randomized experiments is often complicated by the presenc...
Rubin (1976, and elsewhere) claimed that there are three kinds of “missingness”: missing completely ...
Missing data usually present special problems for statistical analyses, especially when the data are...
Multiple Imputation, Maximum Likelihood and Fully Bayesian methods are the three most commonly used ...
The analysis of incomplete contingency tables is a practical and an interesting problem. In this pap...
Abstract: When data are missing due to at most one cause from some time to next time, we can make sa...
Models for incomplete longitudinal data under missingness not at random have gained some popularity....
Abstract: This paper discusses identification, estimation and testing in panel data models with att...
This paper provides further insight into the key concept of missing at random (MAR) in incomplete da...
Over the last decade a variety of models to analyse incomplete multivariate and longitudinal data ha...
Longitudinal data are collected over several time periods for the same units and therefore allow for...
We review some issues related to the implications of different missing data mechanisms on statistica...
We analyse the finite sample properties of maximum likelihood estimators for dynamic panel data mode...
When data are incomplete, models are often catalogued according to one of the three modelling framew...
In this paper, we carry out an in-depth theoretical investigation for inference with missing respons...
The otherwise straightforward analysis of randomized experiments is often complicated by the presenc...
Rubin (1976, and elsewhere) claimed that there are three kinds of “missingness”: missing completely ...
Missing data usually present special problems for statistical analyses, especially when the data are...
Multiple Imputation, Maximum Likelihood and Fully Bayesian methods are the three most commonly used ...
The analysis of incomplete contingency tables is a practical and an interesting problem. In this pap...
Abstract: When data are missing due to at most one cause from some time to next time, we can make sa...
Models for incomplete longitudinal data under missingness not at random have gained some popularity....