Multiplicative Algorithms for Density Combination and Deconvolution

Christine De Mol ()
Additional contact information
Christine De Mol: Université libre de Bruxelles, Department of Mathematics and ECARES

A chapter in Recent Advances in Econometrics and Statistics, 2024, pp 493-510 from Springer

Abstract: Abstract In a previous paper by Conflitti et al. (Int J Forecasting 31:1096–1103, 2015), we devised a simple multiplicative iterative algorithm to compute the weights of an optimal combination of density forecasts. However, the question of convergence of this algorithm was not addressed. In the present work, we provide a detailed convergence proof, both for the cost function and for the sequence of iterates. We then show that a similar algorithm can be used for a classic problem in statistics, namely, the estimation of a mixture of known probability densities. The algorithm can be generalized to treat the case where the data are affected by a known noise, i.e., a density deconvolution problem. Finally, we address the situation of an unknown noise, assuming that it is itself a mixture of known densities, and we propose an adapted alternating blind deconvolution algorithm, the convergence properties of which are also analyzed.

Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-61853-6_25

Ordering information: This item can be ordered from
http://www.springer.com/9783031618536

DOI: 10.1007/978-3-031-61853-6_25

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().