I have a dataset of data composed by x,y where x is flood waterdepth and y is economic damage of buildings in a csv format.
- First, the dataset will be resampled using a bootstrapping approach;
- For the new resample, the most appropriate value of the slope of a trend line that strat from 0,0 will be selected by the chi-square test of goodness of fit;
- The two previous steps were repeated 1,000 times, and 1,000 sets of parameter (i.e., slope of the curve) were generated as the result;
- Finally, by the above iteration, the averages of the 1,000 calibrated parameter converged to a fixed value considered as the most likely scenario. The most likely parameters produce the smallest cumulative error compared to the actual damage data.
- Also, from the 1,000 sets of parameters generated above, the function that maximises the depth-damage relationship will be taken as a maximum damage curve, and the observation that created the minimum depth-damage relationship will be considered for the minimum depth-damage function
I am an Engineering student. Taken Statistics courses. Have a lot of experiences in Python and Matlab. Experience in research work related to data science/Machine learning/optimization in Matlab
€20 EUR em 2 dias
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