Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/post_equi_mcmc.R

This function will 'average' Bayes rules given random rotations of the data array. This 'averaged' estimator has lower risk than the uniformly minimum risk equivariant estimator under a product group of lower triangular matrices. Truncated multiway Takemura's estimator is not equivariant with respect to this product group of lower triangular matrices, but it is an equivariant randomized estimator with respect to a product group of orthogonal matrices.

1 2 | ```
multiway_takemura(X, ortho_max = 2, mcmc_itermax = 1000,
start_identity = FALSE, print_mcmc = FALSE, mode_rep = NULL)
``` |

`X` |
An array. This is the data array. |

`ortho_max` |
An integer. The number of 'averagings' to perform. |

`mcmc_itermax` |
An integer. The number of iterations each MCMC should
perform using |

`start_identity` |
Should each MCMC start their covariance matrices at the identity (TRUE) or at the sample covariance matrices (FALSE)? |

`print_mcmc` |
Should the output of the MCMC be printed to the screen (TRUE) or not (FALSE)? |

`mode_rep` |
A vector of integers. Which mode(s) are considered iid observations? Default is none. |

This function will (1) randomly rotate `X`

along every mode, then (2) it
will calculate the uniformly minimum risk equivariant estimator using
`equi_mcmc`

, then (3) it will 'average' these estimates.

`B`

A list of the truncated multiway Takemura's estimators for
each component covariance matrix. Not their Cholesky square roots.

`b`

Truncated multiway Takemura's estimator for the total variation
parameter. The 'variance' form, not the 'standard devation' form.

David Gerard.

Gerard, D., & Hoff, P. (2015).
Equivariant
minimax dominators of the MLE in the array normal model. *Journal of
Multivariate Analysis*, 137, 32-49.

1 2 3 4 5 6 7 8 |

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