Location-adjusted Wald statistics

Ioannis Kosmidis (University of Warwick)

Frank Adams 2,

Inference about a scalar parameter of interest is a core statistical task that has attracted immense research in statistics. The Wald statistic is a prime candidate for the task, on the grounds of the asymptotic validity of the standard normal approximation to its finite-sample distribution, simplicity and low computational cost. It is well known, though, that this normal approximation can be inadequate, especially when the sample size is small or moderate relative to the number of parameters.  We propose a novel, algebraic adjustment to the Wald statistic that can deliver significant improvements in inferential performance with only small implementation and computational overhead, predominantly due to additional matrix multiplications.

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