ci_rbod {Compind} | R Documentation |

## Robust Benefit of the Doubt approach (RBoD)

### Description

Robust Benefit of the Doubt approach (RBoD) is the robust version of the BoD method. It is based on the concept of the expected minimum input function of order-*m* so "*in place of looking for the lower boundary of the support of F, as was typically the case for the full-frontier (DEA or FDH), the order-m efficiency score can be viewed as the expectation of the maximal score, when compared to m units randomly drawn from the population of units presenting a greater level of simple indicators*", Daraio and Simar (2005).

### Usage

`ci_rbod(x,indic_col,M,B)`

### Arguments

`x` |
A data.frame containing score of the simple indicators. |

`indic_col` |
Simple indicators column number. |

`M` |
The number of elements in each of the bootstrapped samples. |

`B` |
The number of bootstrap replicates. |

### Value

An object of class "CI". This is a list containing the following elements:

`ci_rbod_est` |
Composite indicator estimated values. |

`ci_method` |
Method used; for this function ci_method="rbod". |

### Author(s)

Vidoli F.

### References

Daraio, C., Simar, L. "*Introducing environmental variables in nonparametric frontier models: a probabilistic approach*", Journal of productivity analysis, 2005, 24(1), 93 - 121.

Vidoli F., Mazziotta C., "*Robust weighted composite indicators by means of frontier methods with an application to European infrastructure endowment*", Statistica Applicata, Italian Journal of Applied Statistics, 2013.

### See Also

### Examples

```
i1 <- seq(0.3, 0.5, len = 100) - rnorm (100, 0.2, 0.03)
i2 <- seq(0.3, 1, len = 100) - rnorm (100, 0.2, 0.03)
Indic = data.frame(i1, i2)
CI = ci_rbod(Indic,B=10)
data(EU_NUTS1)
data_norm = normalise_ci(EU_NUTS1,c(2:3),polarity = c("POS","POS"), method=2)
CI = ci_rbod(data_norm$ci_norm,c(1:2),M=10,B=20)
```

*Compind*version 3.1 Index]