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Found 7 papers, 5 papers with code
Scalable Marked Point Processes for Exchangeable and Non-Exchangeable Event Sequences
We adopt the interpretability offered by a parametric, Hawkes-process-inspired conditional probability mass function for the marks and apply variational inference techniques to derive a general and scalable inferential framework for marked point processes.
Empirical bias-reducing adjustments to estimating functions
That penalized objective relates closely to information criteria for model selection, and can be further enhanced with plug-in penalties to deliver reduced-bias $M$-estimates with extra properties, like finiteness in models for categorical data.
Methodology Statistics Theory Statistics Theory 62F10, 62F12, 62J12
Jeffreys-prior penalty, finiteness and shrinkage in binomial-response generalized linear models
This paper studies the finiteness properties of the reduced-bias estimator for logistic regression that results from penalization of the likelihood by Jeffreys' invariant prior; and it provides geometric insights on the shrinkage towards equiprobability that the penalty induces.
Statistics Theory Methodology Statistics Theory 62J12, 62F10, 62F12, 62F03
Modelling rankings in R: the PlackettLuce package
This means that the worth of each item can always be estimated by maximum likelihood, with finite standard error.
Computation
Modeling outcomes of soccer matches
The parameters of the Bradley-Terry extensions are estimated by maximizing the log-likelihood, or an appropriately penalized version of it, while the posterior densities of the parameters of the hierarchical Poisson log-linear model are approximated using integrated nested Laplace approximations.
Mean and median bias reduction in generalized linear models
This paper presents an integrated framework for estimation and inference from generalized linear models using adjusted score equations that result in mean and median bias reduction.
Methodology 62J12, 62F03, 62F12
Two-way sparsity for time-varying networks, with applications in genomics
Separation of these two types of sparsity is achieved with the introduction of a novel prior structure, which draws on ideas from the Bayesian lasso and from copula modelling.
Methodology
