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| R2011b Documentation → Financial Toolbox | |
Learn more about Financial Toolbox |
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| Contents | Index |
This table summarizes what's new in Version 2.5 (R14SP3):
| New Features and Changes | Version Compatibility Considerations | Fixed Bugs and Known Problems |
|---|---|---|
| Yes Details below | No |
Version 2.5 introduces a set of financial statistical computation routines that compute values, such as mean and covariance, when there are missing data elements within a larger data set. These routines implement the Expectation Conditional Maximization (ECM) algorithm with various options that depend on the percentage of missing at random (MAR) data within the data set. The table below lists the functions that implement the ECM algorithm in Financial Toolbox software.
The following ECM functions have been added at this release.
| ecmnfish | Fisher information matrix |
| ecmnhess | Hessian of negative log-likelihood function |
| ecmninit | Initial mean and covariance |
| ecmnmle | Mean and covariance of incomplete multivariate normal data |
| ecmnobj | Negative log-likelihood function |
| ecmnstd | Standard errors for mean and covariance of incomplete data |
 | Version 3.0 (R2006a) Financial Toolbox Software | Compatibility Summary for Financial Toolbox Software |  |
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