The ‘2’ in the name of the function suggests a second implementation
where exact stochastic dominance, decileVote and momentVote are used.
Begin with the output of bootPairs function, a (n999 by p-1) matrix when
there are p columns of data,
bootSummary produces a (6 by p-1) mtx
of summary of bootstrap ouput (Min, 1st Qu,Median, Mean, 3rd Qi.,Max)
output from bootPair2 with p-1 columns and n999 rows in input here
logical (default per100=TRUE) to change the range of 'sum' to [-100, 100] values which are easier to interpret
summ a summary matrix (n999 by p-1) having usual parameters
using the output of
Each containing resampled ‘sum’ values summarizing the weighted sums
associated with all three criteria from the
applied to each bootstrap sample separately.
Prof. H. D. Vinod, Economics Dept., Fordham University, NY
Vinod, H. D. 'Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, https://doi.org/gffn86
Vinod, H. D. and Lopez-de-Lacalle, J. (2009). 'Maximum entropy bootstrap for time series: The meboot R package.' Journal of Statistical Software, Vol. 29(5), pp. 1-19.
Vinod, H. D. Causal Paths and Exogeneity Tests in Generalcorr Package for Air Pollution and Monetary Policy (June 6, 2017). Available at SSRN: https://www.ssrn.com/abstract=2982128
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## Not run: options(np.messages = FALSE) set.seed(34);x=sample(1:10);y=sample(2:11) bb=bootPair2(cbind(x,y),n999=29) bootSummary2(bb) #gives summary stats for n999 bootstrap sum computations bb=bootPair2(airquality,n999=999);options(np.messages=FALSE) bootSummary2(bb)#signs for n999 bootstrap sum computations data('EuroCrime') attach(EuroCrime) bb=bootPair2(cbind(crim,off),n999=29) #col.1= crim causes off #hence positive signs are more intuitively meaningful. #note that n999=29 is too small for real problems, chosen for quickness here. bootSummary2(bb)#signs for n999 bootstrap sum computations ## End(Not run)
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