Statistics & Risk Modeling
with Applications in Finance and Insurance
Editor-in-Chief: Stelzer, Robert
4 Issues per year
Cite Score 2016: 0.33
SCImago Journal Rank (SJR) 2016: 0.346
Source Normalized Impact per Paper (SNIP) 2016: 0.167
Mathematical Citation Quotient (MCQ) 2016: 0.32
Mean and covariance matrix adaptive estimation for a weakly stationary process. Application in stochastic optimization
We introduce an adaptive algorithm to estimate the uncertain parameter of a stochastic optimization problem. The procedure estimates the one-step-ahead means, variances and covariances of a random process in a distribution-free and multidimensional framework when these means, variances and covariances are slowly varying on a given past interval. The quality of the approximate problem obtained when employing our estimation of the uncertain parameter is controlled in function of the number of components of the process and of the length of the largest past interval where the means, variances and covariances slowly vary. The procedure is finally applied to a portfolio selection model.
Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.