Research Seminar Series in Statistics and Mathematics

Wirtschaftsuniversität Wien, Departments 4 D4.4.00809:00 - 10:30

Art Vortrag/Diskussion
Vortragende/rIoannis Kosmidis (Department of Statistics, University of Warwick)
Veranstalter Institut für Statistik und Mathematik
Kontakt katrin.artner@wu.ac.at

Ioannis Kosmidis (Department of Statistics, University of Warwick) about “Location-adjusted Wald statistics”

The Institute for Statistics and Mathematics (Department of Finance, Accounting and Statistics) cordially invites everyone interested to attend the talks in our Research Seminar Series, where internationally renowned scholars from leading universities present and discuss their (working) papers.

The list of talks for the summer term 2018 is available via the following link:
Summer Term 2018


Inference on a scalar parameter of interest is commonly constructed using a Wald statistic, on the grounds of the validity of the standard normal approximation to its finite-sample distribution and computational convenience. A prominent example are the individual Wald tests for regression parameters that are reported by default in regression output in the majority of statistical computing environments. The normal approximation can, though, be inadequate, especially when the sample size is small or moderate relative to the number of parameters. In this talk, the Wald statistic is viewed as an estimate of a transformation of the model parameters and is appropriately adjusted so that its null expectation is asymptotically closer to zero. The bias adjustment depends on the expected information matrix, the first-order term in the bias expansion of the maximum likelihood estimator, and the derivatives of the transformation, all of which are either readily available or easily obtainable in standard software for a wealth of well-used models. The finite-sample performance of the location-adjusted Wald statistic is examined analytically in simple models and via simulation in a series of more realistic modelling frameworks, including generalized linear models, meta-regression and beta regression. The location-adjusted Wald statistic is found able to deliver significant improvements in inferential performance over the standard Wald statistic, without sacrificing any of its computational simplicity.

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