RobustWeights: Robustification of the sampling weights

In GB2: Generalized Beta Distribution of the Second Kind: Properties, Likelihood, Estimation

Robustification of the sampling weights

Description

Calculation of a Huber-type correction factor by which the vector of weights is multiplied.

Usage

robwts(x, w=rep(1,length(x)), c=0.01, alpha=0.001)

Arguments

x	numeric; vector of data values.
w	numeric; vector of weights. Must have the same length as x. By default w is a vector of 1.
c	numeric; a constant which can take different values, e.g. 0.01,0.02. By default c=0.1.
alpha	numeric; a probability in the interval (0,1). By default alpha=0.001.

Details

If x denotes the observed value and x_{α} the α-th qiantile of the Fisk distribution, then we define our scale as:

d = \displaystyle \frac{x_{1-α}}{b} - \frac{x_{α}}{b}

. Next, the correction factor is calculated as follows:

corr = \max≤ft\{c, \min≤ft(1,\displaystyle \frac{d}{|b/x-1|},\frac{d}{|x/b-1|}\right)\right\}

Value

robwts returns a list of two elements: the vector of correction factors by which the weights are multiplied and the vector of corrected (robustified) weights.

Author(s)

Monique Graf

References

Graf, M., Nedyalkova, D., Muennich, R., Seger, J. and Zins, S. (2011) AMELI Deliverable 2.1: Parametric Estimation of Income Distributions and Indicators of Poverty and Social Exclusion. Technical report, AMELI-Project.

GB2 documentation built on June 22, 2022, 9:07 a.m.