Item does not contain fulltextstablespec is an implementation of stable specification search for cross-sectional data (S3C) and for longitudinal data (S3L). S3C/L is an exploratory and heuristic approach for specification search in Structural Equation Modeling. The basic idea is to subsample the original data and then search for optimal models on each subset. Optimality is defined through two objectives: model fit and parsimony. As these objectives are conflicting, we apply a multi-objective optimization methods, specifically NSGA-II, to obtain optimal models for the whole range of model complexities. From these optimal models, we consider only the relevant model specifications (structures), i.e., those that are both stable (occur frequentl...
(A) Three basic models were used for implementation of the BSR. (B) The estimated basin stability wa...
Structural Equation Modeling (SEM), as a statistical modeling technique, is one of the most comprehe...
Sparse estimation via penalized likelihood (PL) is now a popular approach to learn the associations ...
Contains fulltext : 168717pre.pdf (Author’s version preprint ) (Open Access) ...
Abstract—Structural equation modelling (SEM) is a statistical technique for testing and estimating c...
Contains fulltext : 199258pre.pdf (preprint version ) (Open Access) ...
Contains fulltext : 207744pre.pdf (Author’s version preprint ) (Open Access) ...
Sample data obtained via cluster sampling rather than simple random sampling requires the use of spe...
We develop estimation for potentially high-dimensional additive structural equation models. A key co...
The statistical community has brought logical rigor and mathematical precision to the problem of usi...
Previous studies suggest that results from specification searches, as typically employed in structur...
Search-Based Software Engineering (SBSE) is about solving software development problems by formulati...
In causal inference, all methods of model learning rely on testable implications, namely, properties...
We present two algorithms for inducing structural equation models from data. Assuming no latent vari...
International audienceAfter showing that plain covariance or correlation-based criteria are generall...
(A) Three basic models were used for implementation of the BSR. (B) The estimated basin stability wa...
Structural Equation Modeling (SEM), as a statistical modeling technique, is one of the most comprehe...
Sparse estimation via penalized likelihood (PL) is now a popular approach to learn the associations ...
Contains fulltext : 168717pre.pdf (Author’s version preprint ) (Open Access) ...
Abstract—Structural equation modelling (SEM) is a statistical technique for testing and estimating c...
Contains fulltext : 199258pre.pdf (preprint version ) (Open Access) ...
Contains fulltext : 207744pre.pdf (Author’s version preprint ) (Open Access) ...
Sample data obtained via cluster sampling rather than simple random sampling requires the use of spe...
We develop estimation for potentially high-dimensional additive structural equation models. A key co...
The statistical community has brought logical rigor and mathematical precision to the problem of usi...
Previous studies suggest that results from specification searches, as typically employed in structur...
Search-Based Software Engineering (SBSE) is about solving software development problems by formulati...
In causal inference, all methods of model learning rely on testable implications, namely, properties...
We present two algorithms for inducing structural equation models from data. Assuming no latent vari...
International audienceAfter showing that plain covariance or correlation-based criteria are generall...
(A) Three basic models were used for implementation of the BSR. (B) The estimated basin stability wa...
Structural Equation Modeling (SEM), as a statistical modeling technique, is one of the most comprehe...
Sparse estimation via penalized likelihood (PL) is now a popular approach to learn the associations ...