Explanation:
Bootstrapping is a non-parametric statistical technique that involves resampling data with replacement from an original sample to estimate the distribution of a statistic.
Option A is incorrect because while basic bootstrapping assumes i.i.d. data, variations like block bootstrapping exist for non-i.i.d. data (such as time series). Option B is incorrect because resampling in bootstrapping is done with replacement, which allows the sample size of each bootstrap to be the same as the original dataset. Option C is incorrect because bootstrapping is a non-parametric method and does not require the data to follow a normal distribution.
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Q.31 A financial analyst at a technology company is evaluating the risk profile of a new investment product using historical data. The analyst is considering applying a resampling technique to estimate the distribution of potential returns. The analyst debates whether to use Monte Carlo simulation or bootstrapping for the analysis. Which of the following statements about bootstrapping is correct?
A
Bootstrapping requires that the data be independent and identically distributed (i.i.d).
B
In bootstrapping, data must be resampled without replacement to preserve statistical integrity.
C
Bootstrapping can only be applied to data that is normally distributed.
D
Bootstrapping involves resampling with replacement from the original dataset.
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