A
Alan H. Welsh
Researcher at Australian National University
Publications - 184
Citations - 7752
Alan H. Welsh is an academic researcher from Australian National University. The author has contributed to research in topics: Estimator & Regression analysis. The author has an hindex of 40, co-authored 182 publications receiving 7163 citations. Previous affiliations of Alan H. Welsh include Universidade Nova de Lisboa & United States Department of the Army.
Papers
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Processing interactions and lexical access during word recognition in continuous speech
TL;DR: An active direct access model is proposed, in which top-down processing constraints interact directly with bottom-up information to produce the primary lexical interpretation of the acoustic-phonetic input.
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Modelling the abundance of rare species: statistical models for counts with extra zeros
TL;DR: In this paper, the authors consider several statistical models for the analysis of the abundance of a rare species and show how to obtain standard errors for the parameter estimates, and also estimate the mean abundance of animals at a site.
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Adaptive Estimates of Parameters of Regular Variation
Peter Hall,Alan H. Welsh +1 more
TL;DR: In this paper, the problem of estimating shape and scale parameters for a distribution with regularly varying tails is related to that of nonparametrically estimating a density at a fixed point, in that optimal construction of the estimators depends substantially upon unknown features of the distribution.
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Generalized additive modelling and zero inflated count data
Simon C. Barry,Alan H. Welsh +1 more
TL;DR: In this article, the authors describe a flexible method for modelling zero inflated count data, which are typically found when trying to model and predict species distributions, and extend previous work to incorporate the use of generalized additive models (GAM) in the modelling steps.
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Bootstrapping clustered data
Chris Field,Alan H. Welsh +1 more
TL;DR: In this article, the consistency of variance estimates for a bootstrap method depends on the choice of model with the residual bootstrap giving consistency under the transformation model whereas the cluster bootstrap gives consistent estimates under both the transformation and the random effect model.