In maximum entropy sampling (MES), a design is chosen by maximizing the joint Shannon entropy of parameters and observations. However, when the conditional parametric model of the response contains a large number of covariates, the posterior calculations in MES can be challenging or infeasible. In this work, we consider the use of composite likelihood modelling to break down the complexity of the full likelihood and code the original optimization problem into a set of simple partial likelihood problems. We study the optimality behaviour of the composite likelihood sampling approach as the number of design variables grows using both asymptotic analysis and numerical simulations.

Ferrari, D., Borrotti, M. (2013). Maximum Entropy Design in High Dimensions by Composite Likelihood Modelling. In mODa 10 – Advances in Model-Oriented Design and Analysis (pp.73-80) [10.1007/978-3-319-00218-7_9].

Maximum Entropy Design in High Dimensions by Composite Likelihood Modelling

Borrotti, M
2013

Abstract

In maximum entropy sampling (MES), a design is chosen by maximizing the joint Shannon entropy of parameters and observations. However, when the conditional parametric model of the response contains a large number of covariates, the posterior calculations in MES can be challenging or infeasible. In this work, we consider the use of composite likelihood modelling to break down the complexity of the full likelihood and code the original optimization problem into a set of simple partial likelihood problems. We study the optimality behaviour of the composite likelihood sampling approach as the number of design variables grows using both asymptotic analysis and numerical simulations.
paper
Design Space; Shannon Entropy; Design Matrice; Partial Likelihood; Normal Regression
English
Workshop on Model-Oriented Data Analysis and Optimum Design (mODa10)
2013
mODa 10 – Advances in Model-Oriented Design and Analysis
978-3-319-00217-0
2013
73
80
https://link.springer.com/chapter/10.1007/978-3-319-00218-7_9#aboutcontent
none
Ferrari, D., Borrotti, M. (2013). Maximum Entropy Design in High Dimensions by Composite Likelihood Modelling. In mODa 10 – Advances in Model-Oriented Design and Analysis (pp.73-80) [10.1007/978-3-319-00218-7_9].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/214766
Citazioni
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
Social impact