Description Usage Arguments Details Value References

This function computes the Best Predictive Estimators (BPE) of the unknown parameters for Fay-Herriot model. It computes BPE of the regression coefficients of the fixed effect (beta). The variance of the random effect can be specified by the user in which case that will be used to calculate the BPE of beta. Otherwise the function will calculate BPE of the variance component of the random effect and use that to calculate the BPE of beta.

1 2 |

`formula` |
an object of class formula (or one that can be coerced to that class): a symbolic description of the model to be fitted. The variables included in formula must have a length equal to the number of small areas. More about the model specification are given under Details. |

`data` |
optional data frame containing the variable names in |

`errorvar` |
vector containing the variances of the random errors for all small areas. |

`randvar` |
variance of the random effect. If not supplied, it is estimated by BPE. |

`maxiter` |
maximum number of iterations used in estimating randvar. |

`precision` |
covergence tolerance limit for estimating randvar. |

If `randvar`

is not provided, it is first estimated by its BPE.

`formula`

is specified in the form `response ~ predictors`

where the predictors are separated by `+`

. `formula`

has an implied intercept term. To remove the intercept term, use either `y ~ x - 1`

or `y ~ 0 + x`

.

The function will return a list with the following objects.

`A.BPE` |
BPE of variance of the random effect (if not specified by the user). |

`beta.BPE` |
BPE of the regression coefficient of the fixed effects. |

Jiang J, Nguyen T, and Rao J. S. (2011), "Best Predictive Small Area Estimation", Journal of the American Statistical Association.

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