Bootstrapping data arrays of arbitrary order

  title={Bootstrapping data arrays of arbitrary order},
  author={B. Owen Stanford University Dean Eckles},
In this paper we study a bootstrap strategy for estimating the variance of a mean taken over large multifactor crossed random effects data sets. We apply bootstrap reweighting independently to the levels of each factor, giving each observation the product of its factor weights. No exact bootstrap exists for this problem (McCullagh, 2000). We show that the proposed bootstrap is mildly conservative, under sufficient conditions that allow very unbalanced and heteroscedastic inputs. Earlier results… CONTINUE READING
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