ci_rbod function

Robust Benefit of the Doubt approach (RBoD)

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).

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.

Returns

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".

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.

Author(s)

Vidoli F.

See Also

ci_bod

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)