In all likelihood: Statistical modelling and inference using likelihood

By Pamela Morris. Mathematical Gazette, 2002

1,291 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/56c403801d597ede644f7d813dd3defc35415f2a

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Citations in the corpus

Swimming behaviour and migration of a swordfish recorded by an archival tag (2003, background): “This is considered to be a kind of estimated likelihood or pseudo-likelihood approach (Gong and Samaniego 1981; Pawitan 2001).”
An improved model to predict the effects of changing biodiversity levels on ecosystem function (2013, methodology): “mated coefficient (Pawitan 2001).”
Alternatives to statistical hypothesis testing in ecology: a guide to self teaching. (2006, methodology): “Likelihood theory can easily incorporate multiple types of observations like these (Aguiar and Sala 1999, Pawitan 2001; Appendix D).”
Bayesian inference and model comparison for metallic fatigue data (2015, methodology): “The fatigue limit parameter assessment under both models can be done by computing numerically tailored functions from their joint likelihoods, usually called profile likelihoods [10].”
Statistical inference for stochastic simulation models--theory and application. (2011, background): “A sufficient summary statistic that cannot be further simplified is called minimally sufficient (Pawitan 2001).”
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 (2014, positive): “This adjustment, first used in the context of dispersion estimation for SAGE data [48] and then for HTS data [3] in edgeR, corrects for the negative bias of dispersion estimates from using the MLEs for the fitted values μ̂ij (analogous to Bessel’s correction in the usual sample variance formula; for”
Mixed and Mixture Regression Models for Continuous Bounded Responses Using the Beta Distribution (2012, neutral): “One particular warning that applies to users of beta regression is that all integration constants need to be included in the computation of these measures when they are used to compare across distributions (Pawitan, 2001).”
Visual Representations: Defining Properties and Deep Approximations (2014, background): “ood function is invariant4 to invertible transformations of the parameter, g2G(the inverse needs not be unique): If = g(), then L() = sup jg()= L() : = L(g 1()). The following (Theorem 3.2 of [45]) can be extended to an innite-dimensional parameter (Theorem 6.1 of [3]). Theorem 1 (The likelihood fun”