# imp.weights

0th

Percentile

##### Importance Sampling Weights

This function calculates the importance sampling weight required to correct for simulation from a distribution with probabilities p when estimates are required assuming that simulation was from an alternative distribution with probabilities q.

Keywords
nonparametric
##### Usage
imp.weights(boot.out, def = TRUE, q = NULL)
##### Arguments
boot.out

A object of class "boot" generated by boot or tilt.boot. Typically the bootstrap simulations would have been done using importance resampling and we wish to do our calculations under the assumption of sampling with equal probabilities.

def

A logical variable indicating whether the defensive mixture distribution weights should be calculated. This makes sense only in the case where the replicates in boot.out were simulated under a number of different distributions. If this is the case then the defensive mixture weights use a mixture of the distributions used in the bootstrap. The alternative is to calculate the weights for each replicate using knowledge of the distribution from which the bootstrap resample was generated.

q

##### Note

See the example in the help for imp.moments for an example of using imp.weights.

##### References

Davison, A. C. and Hinkley, D. V. (1997) Bootstrap Methods and Their Application. Cambridge University Press.

Hesterberg, T. (1995) Weighted average importance sampling and defensive mixture distributions. Technometrics, 37, 185--194.

Johns, M.V. (1988) Importance sampling for bootstrap confidence intervals. Journal of the American Statistical Association, 83, 709--714.

boot, exp.tilt, imp.moments, smooth.f, tilt.boot