Dr Ioannis Kosmidis - University of Warwick |

Fri 23 Nov 2018, 15:05 - 16:00 |

JCMB 5323 |

If you have a question about this talk, please contact: Ruben Amoros Salvador (ramoros)

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.

Co-author:

Claudia Di Caterina,

University of Padova