Anthony Christidis

Anthony Christidis

8 packages on CRAN

nnGarrote

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Functions to compute the non-negative garrote estimator as proposed by Breiman (1995) <https://www.jstor.org/stable/1269730> with the penalized initial estimators extension as proposed by Yuan and Lin (2007) <https://www.jstor.org/stable/4623260>.

RPEGLMEN

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Implements the fast iterative shrinkage-thresholding algorithm (FISTA) algorithm to fit a Gamma distribution with an elastic net penalty as described in Chen, Arakvin and Martin (2018) <arxiv:1804.07780>. An implementation for the case of the exponential distribution is also available, with details available in Chen and Martin (2018) <https://papers.ssrn.com/abstract_id=3085672>.

RPEIF

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Computes the influence functions time series of the returns for the risk and performance measures as mentioned in Zhang and Martin (2017) <https://ssrn.com/abstract=2747179> as well as Chen and Martin (2018) <https://ssrn.com/abstract=3085672>. Also evaluates estimators influence functions at a set of parameter values and plots them to display the shapes of the influence functions.

RPESE

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Estimates of standard errors of popular risk and performance measures for asset or portfolio returns using methods as described in Chen and Martin (2019) <https://ssrn.com/abstract=3085672>.

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Transforms or simulates data with a target empirical covariance matrix supplied by the user. The method to obtain the data with the target empirical covariance matrix is described in Section 5.1 of Christidis, Van Aelst and Zamar (2019) <arXiv:1812.05678>.

SplitReg

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Functions for computing split regularized estimators defined in Christidis, Lakshmanan, Smucler and Zamar (2019) <arXiv:1712.03561>. The approach fits linear regression models that split the set of covariates into groups. The optimal split of the variables into groups and the regularized estimation of the regression coefficients are performed by minimizing an objective function that encourages sparsity within each group and diversity among them. The estimated coefficients are then pooled together to form the final fit.

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Functions to generate or sample from all possible splits of features or variables into a number of specified groups. Also computes the best split selection estimator (for low-dimensional data) as defined in Christidis, Van Aelst and Zamar (2019) <arXiv:1812.05678>.

ensembleEN

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Functions for computing the ensembles of regularized linear regression estimators defined in Christidis, Lakshmanan, Smucler and Zamar (2017) <arXiv:1712.03561>. The procedure works on top of a given penalized linear regression estimator, the Elastic Net in this implementation, by fitting it to possibly overlapping subsets of features, while at the same time encouraging diversity among the subsets, to reduce the correlations between the predictions that result from each fitted model. The predictions from the models are then aggregated.