Summary
In this chapter, we learned how to resample a dataset. We analyzed several techniques that approach the problem differently. First, we analyzed the basic concepts of sampling and learned about the reasons that push us to use a sample extracted from a population. We then examined the pros and cons of this choice. We also analyzed how a resampling algorithm works.
We then tackled the first resampling method: the Jackknife method. We first defined the concepts behind the method and then moved on to the procedure, which allows us to obtain samples from the original population. To put the concepts we learned into practice, we applied Jackknife resampling to a practical case.
We then explored the bootstrap method, which builds unobserved but statistically, like the observed samples. This is accomplished by resampling the observed series through an extraction procedure where we reinsert the observations. After defining the method, we worked through an example to highlight the...
