MATLAB: How to use bootstrap technique among multiple data sets by choosing data sets randomly

Question

I have two time series of 2,15,000 samples each i.e. one is E and another one H;

I made total samples into 10 sections i.e. 21,500 samples each section for E time series and 10 sections i.e. 21,500 samples each section for H time series

I want to calculate Z=[E/H];

E and H data are stored in one matrix for one section i.e. total 10 matrices for 10 sections.

Now One section (matrix) is chosen randomly among 10 with equal probability i.e. 1/10, and the process is repeated 10 times. This yields 10 sections( or matrices). It should be noted that among them, some sections may be the same section, since each section is chosen independently with equal probability from the entire sampling space. The corresponding E and H data terms of the chosen 10 matrices, are then used in equation Z=[E/H] to obtain one estimate of the impedance Z1.

The whole procedure is repeated 500 times such that 500 estimates of Z1,Z2….Z500 are obtained. The mean values obtained by the bootstrapping are considered as the Z estimates and confidence intervals of 95% are derived from standard deviations.

How to do this. Please anybody help me to solve this.?

Answer 1

"... i want to use all the 10 sections data i.e. EH1 to EH10 in bootsrtp function for 500 iterations to get Zfinal."

Why split them up in the first place? The best solution is to not split up the data.

But if you want the split data to be combined,

EHall = [EH1; EH2; EH3; . . .; EH10]; 
TFF=bootstrp(500,@(tf) impten(tf),EHall);