Location-adjusted Wald statistics for scalar parameters

@article{Caterina2019LocationadjustedWS,
  title={Location-adjusted Wald statistics for scalar parameters},
  author={Claudia Di Caterina and Ioannis Kosmidis},
  journal={Comput. Stat. Data Anal.},
  year={2019},
  volume={138},
  pages={126-142}
}
1 Citations

Figures and Tables from this paper

Mean and median bias reduction: A concise review and application to adjacent-categories logit models
The estimation of categorical response models using bias-reducing adjusted score equations has seen extensive theoretical research and applied use. The resulting estimates have been found to have

References

SHOWING 1-10 OF 75 REFERENCES
Reducing the Impact of Bias in Likelihood Inference for Prominent Model Settings
TLDR
This thesis proposes a convenient way to refine Wald-type inference in regression settings through asymptotic bias correction of the $z$-statistic, and suggests a strategy to extend the current range of applications of the modified profile likelihood.
A Reminder of the Fallibility of the Wald Statistic
Abstract Computer programs often produce a parameter estimate and estimated variance (). Thus it is easy to compute a Wald statistic (- θ0){()}−1/2 to test the null hypothesis θ = θ0. Hauck and
Improved estimation in cumulative link models
For the estimation of cumulative link models for ordinal data, the bias reducing adjusted score equations of Firth in 1993 are obtained, whose solution ensures an estimator with smaller asymptotic
Parametric bootstrapping with nuisance parameters
Modified profile likelihoods in models with stratum nuisance parameters
It is well known, at least through many examples, that when there are many nuisance parameters modified profile likelihoods often perform much better than the profile likelihood. Ordinary asymptotics
Principles of statistical inference : from a neo-Fisherian perspective
In this book, an integrated introduction to statistical inference is provided from a frequentist likelihood-based viewpoint. Classical results are presented together with recent developments, largely
Jeffreys-prior penalty, finiteness and shrinkage in binomial-response generalized linear models
Penalization of the likelihood by Jeffreys’ invariant prior, or a positive power thereof, is shown to produce finite-valued maximum penalized likelihood estimates in a broad class of binomial
A generic algorithm for reducing bias in parametric estimation
TLDR
A general iterative algorithm is developed for the computation of reduced-bias parameter estimates in regular statistical models through adjustments to the score function, which can usefully be viewed as a series of iterative bias corrections, thus facilitating the adjusted score approach to bias reduction.
...
...