High dimensional inference has been a crucial topic in statistical learning, especially with the rise of data that has a lot of features yet is difficult to gather. We analyze regressions on data in large dimensions p that are much greater than the samples available, n. The usual method in constructing confidence intervals used for OLS is not useful when p » n since asymptotic normality fails. We survey literature that comes up with new method of constructing these intervals.
