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Estimation of Nonparametric Density from Sparse Summary Information: Densities of Undiscovered Oil

10 Pages Posted: 10 May 2013  

Paul H. C. Eilers

Erasmus MC

Vlasios Voudouris

ABM Analytics Ltd

Robert Rigby

London Metropolitan University

Dimitrios Stasinopoulos

London Metropolitan University

Date Written: May 8, 2013

Abstract

Information on observable economic and financial variables is sometimes limited to summary form. Therefore, in many practical situations, it is desirable to restrict the flexibility of nonparametric density estimators to accommodate information about the summary data as well as any prior information about the nature of the problem. Our nonparametric estimator is easy to implement and has an explicit algebraic structure. The motivation for this letter is the generation of non-parametric densities from sparse summary data rather than from individual observations.

Keywords: nonparametric density, smoothing penalties, constraint-based

Suggested Citation

Eilers, Paul H. C. and Voudouris, Vlasios and Rigby, Robert and Stasinopoulos, Dimitrios, Estimation of Nonparametric Density from Sparse Summary Information: Densities of Undiscovered Oil (May 8, 2013). USAEE Working Paper No. 13-124. Available at SSRN: https://ssrn.com/abstract=2262584 or http://dx.doi.org/10.2139/ssrn.2262584

Paul H. C. Eilers

Erasmus MC ( email )

3000 DR Rotterdam
Netherlands

Vlasios Voudouris (Contact Author)

ABM Analytics Ltd ( email )

Suite 17 125
145-157 St John Street
London, EC1V 4PW
United Kingdom

HOME PAGE: http://www.abm-analytics.com/people.php

Robert Rigby

London Metropolitan University ( email )

166-220 Holloway Road
London EC3N 2EY, N7 8HN
United Kingdom

Dimitrios Stasinopoulos

London Metropolitan University ( email )

166-220 Holloway Road
London EC3N 2EY, N7 8HN
United Kingdom

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