Description Usage Arguments Value Author(s) References Examples

This function computes an approximate expectation for a (second-order stationary) process's autocovariance function from the first two moments of its log-spectrum, as encoded in an expectation vector and variance matrix for the coefficients of a basis representation. It then uses this autocovariance to interpolate values of a process and to calculate variances for them.

The function is really here to facilitate the reproduction of an example
from Nason, Powell, Elliott and Smith (2016).
It may be studied as an example, but is not recommended for general use.
Instead, custom Kriging-type estimates ought to be produced by manipulating by
hand variance matrices populated with autocovariance function values,
which can be computed with the function `logspec2cov`

.

1 2 |

`Dhigh` |
Vector. The high frequency data. |

`hightimes` |
Vector. Integer time points at which the high frequency observations are made. |

`Dlow` |
Vector. The low frequency data. |

`lowtimes` |
Vector. Integer time points at which the low frequency observations are made. |

`predtimes` |
Vector. Integer time points at which hindcasts are required. |

`filter` |
Vector. A known vector of filter coefficients arising from the
observation process prior to any subsampling.
The default is |

`ebeta` |
Vector. Expectations for basis coefficients of the log spectrum. |

`vbeta` |
Vector. The variance for the basis coefficients of the log spectrum. |

`SARIMA` |
List. A list encoding the SARIMA model that acts as an intercept,
or base line, for the non-parametric estimate of the log-spectrum.
The default is white noise with variance one.
The log-spectrum basis coefficients parameterize a deviation away
from the SARIMA model's log-spectrum.
The contents of the SARIMA list are formatted in line with the
format used by the package |

`hindcast` |
A vector of hindcast expectations |

`var.hindcast` |
A covariance matrix for the hindcast values. |

Ben Powell

Nason, G.P., Powell, B., Elliott, D. and Smith, P. (2016) Should We Sample a Time Series More Frequently? Decision Support via Multirate Spectrum Estimation. Journal of the Royal Statistical Society, Series A., 179, (to appear).

1 2 3 | ```
#
# See example in \code{\link{travel}} help file
#
``` |

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