public class LedoitWolf2004 extends Object
Modifier and Type | Class and Description |
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static class |
LedoitWolf2004.Result
The estimator and some intermediate values computed by the algorithm.
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Constructor and Description |
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LedoitWolf2004()
Creates the algorithm instance, using an unbiased sample covariance
matrix by default.
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LedoitWolf2004(boolean unbiased)
Creates the algorithm instance, with the option to use an unbiased or
biased sample covariance matrix.
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Modifier and Type | Method and Description |
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LedoitWolf2004.Result |
compute(Matrix Y)
Estimates the covariance matrix for a given matrix Y (each column
in Y is a time-series), with the optimal shrinkage parameter
computed by the algorithm.
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LedoitWolf2004.Result |
compute(Matrix Y,
double shrinkage)
Estimates the covariance matrix for a given matrix Y (each column
in Y is a time-series), with the given shrinkage parameter.
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public LedoitWolf2004()
public LedoitWolf2004(boolean unbiased)
unbiased
- true
to use unbiased covariance matrixpublic LedoitWolf2004.Result compute(Matrix Y)
Y
- the input matrixpublic LedoitWolf2004.Result compute(Matrix Y, double shrinkage)
Y
- the input matrixshrinkage
- the shrinkage parameterCopyright © 2010-2020 NM FinTech Ltd.. All Rights Reserved.