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Showing 1 to 4 of 4 for “"covariance shrinkage"”.

  1. Robust portfolio construction: using resampled efficiency in combination with covariance shrinkage

    … focusses on estimation error in the sample covariance (one of portfolio optimisation inputs). In particular shrinkage techniques applied to the sample covariance matrix are considered and the merits thereof are assessed. The second technique considered in the thesis focusses on the portfolio …

    cape-town Repository record for Robust portfolio construction: using resampled efficiency in combination with covariance shrinkage (opens in a new tab)

  2. Bayesian Estimation of Material Properties in Case of Correlated and Insufficient Data

    … in multivariate error modeling and high sample covariance matrix instability. To recover from the lack of information about the true covariance we analyze two different methodologies: first the hierarchical covariance modeling is investigated, then a method based on covariance shrinkage is …

    tdl Repository record for Bayesian Estimation of Material Properties in Case of Correlated and Insufficient Data (opens in a new tab)

  3. Enhanced minimum variance optimisation: a pragmatic approach

    … into the multicriteria problem, together with covariance shrinkage – improve the performance of the MVP. The factor tilts examined include Active Distance, Concentration and Volume. Additionally, the constant correlation model is employed in the estimation of the shrinkage intensity, structured …

    cape-town Repository record for Enhanced minimum variance optimisation: a pragmatic approach (opens in a new tab)

  4. Combining Data-driven and Theory-guided Models in Ensemble Data Assimilation

    There once was a dream that data-driven models would replace their theory-guided counterparts. We have awoken from this dream. We now know that data cannot replace theory. Data-driven models still have their advantages, mainly in computational efficiency but also providing us with some special …

    vt Repository record for Combining Data-driven and Theory-guided Models in Ensemble Data Assimilation (opens in a new tab)