We show how to determine the parameter redundancy status of a model with covariates from that of the same model without covariates, thereby simplifying the calculation considerably. A matrix decomposition is necessary to ensure that the symbolic computation computer programmes return correct results. The paper is illustrated by mark-recovery and latent-class models, with associated Maple code. Copyright 2010, Oxford University Press.
A non-linear version of redundancy analysis is introduced. The technique is called REDUNDALS. It is ...
Tools are established for evaluating the deficiency of parameter-redundant models.Examples are provi...
Log-linear models are typically fitted to contingency table data to describe and identify the relati...
A model, involving a particular set of parameters, is said to be parameter redundant when the likeli...
The focus of the present work are Structural Equation Models in the Redundancy Analysis framework (S...
Necessary and sufficient conditions are established for the parameter redundancy of a wide class of ...
To be able to fit a parametric model successfully using maximum likelihood, all the parameters need ...
Redundancy is a system property that generally refers to duplication of state information or system ...
International audienceThere are a lot of statistical problems connected with the redundancy among mu...
This thesis is concerned with parameter redundancy in statistical ecology models. If it is not possi...
With the intent of dispelling the prevailing negative connotations associated with redundancy, we ar...
In this paper we develop a comprehensive approach to determining the paramet-ric structure of models...
In this paper we develop a comprehensive approach to determining the parametric structure of models....
A simple condition, which is expressed directly in terms of the ARMA model pa-rameters, is given for...
Received zzz, revised zzz, accepted zzz We provide a definitive guide to parameter redundancy in mar...
A non-linear version of redundancy analysis is introduced. The technique is called REDUNDALS. It is ...
Tools are established for evaluating the deficiency of parameter-redundant models.Examples are provi...
Log-linear models are typically fitted to contingency table data to describe and identify the relati...
A model, involving a particular set of parameters, is said to be parameter redundant when the likeli...
The focus of the present work are Structural Equation Models in the Redundancy Analysis framework (S...
Necessary and sufficient conditions are established for the parameter redundancy of a wide class of ...
To be able to fit a parametric model successfully using maximum likelihood, all the parameters need ...
Redundancy is a system property that generally refers to duplication of state information or system ...
International audienceThere are a lot of statistical problems connected with the redundancy among mu...
This thesis is concerned with parameter redundancy in statistical ecology models. If it is not possi...
With the intent of dispelling the prevailing negative connotations associated with redundancy, we ar...
In this paper we develop a comprehensive approach to determining the paramet-ric structure of models...
In this paper we develop a comprehensive approach to determining the parametric structure of models....
A simple condition, which is expressed directly in terms of the ARMA model pa-rameters, is given for...
Received zzz, revised zzz, accepted zzz We provide a definitive guide to parameter redundancy in mar...
A non-linear version of redundancy analysis is introduced. The technique is called REDUNDALS. It is ...
Tools are established for evaluating the deficiency of parameter-redundant models.Examples are provi...
Log-linear models are typically fitted to contingency table data to describe and identify the relati...
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