# Small Area Estimation for Rao and Yu model

### Description

A complete set of functions to calculate several eblups estimators and its mean square errors. All estimators are based in area-level linear mixed model introduced by Rao and Yu in 1994 (see documentation). Saery package are developed to fit the model with REML method.

### Details

Package: | saery |

Type: | Package |

Version: | 1.0 |

Date: | 2014-09-10 |

License: | GPL-2 |

The main functions of the saery package are `fit.saery`

and `eblup.saery`

.
The function `fit.saery`

is used to fit the correct model for three options. `eblup.saery`

calculates the eblup and mse for the model.

### Author(s)

Maria Dolores Esteban Lefler, Domingo Morales Gonzalez, Agustin Perez Martin

Maintainer: Agustin Perez Martin <agustin.perez@umh.es>

### References

Rao, J.N.K., Yu, M., 1994. Small area estimation by combining time series and cross sectional data. Canadian Journal of Statistics 22, 511-528.

Esteban, M.D., Morales, D., Perez, A., Santamaria, L., 2012. Small area estimation of poverty proportions under area-level time models. Computational Statistics and Data Analysis, 56 (10), pp. 2840-2855.

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 | ```
sigma2edi <- datos[,6]
X <- as.matrix(datos[,5])
ydi <- datos[,3]
D <- length(unique(datos[,1]))
md <- rep(length(unique(datos[,2])), D)
output.fit.ar1 <- fit.saery(X, ydi, D, md, sigma2edi, "AR", 0.9)
output.fit.ar1
#For computational reasons B is too low. We recomend to increase up to 100
eblup.output.ar1 <- eblup.saery(X, ydi, D, md, sigma2edi, "a", plot = TRUE, B = 2)
eblup.output.ar1
``` |