Likelihood Methods in Statistics

By Thomas A. Severini. 2001

515 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/398bb3f406b72c6f68aea36e9ca426d2f7545bd9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Some asymptotic results on generalized penalized spline smoothing (2009, background): “This, however, is exactly the condition in which the Laplace approximation works (see Severini (2000)).”
Improved likelihood inference for discrete data (2006, methodology): “The formulae that were given in Fraser (2003) can be used to convert {l.θ/, ϕ.θ/} to a marginal log-likelihood for ψ, whether ψ is scalar or vector.”
Goodness-of-fit tests in mixed models (2009, background): “Severini (2000) , Section 2.3, which also contains an expression for the multi-dimensional case),”
Robust priors in nonlinear panel data models (2007, methodology): “In particular, it is maximized at θ0 and satisfies Bartlett identities (Severini, 2000).”
Asymptotics and the theory of inference (2003, background): “A discussion of the p * approximation when y has a lattice distribution is given in Severini (2000).”
Extending the rank likelihood for semiparametric copula estimation (2006, methodology): “In this situation, estimation of θ can be based on the marginal likelihood p(t (y)|θ), eliminating the need to estimate the nuisance parameter ψ [see, e.g., Section 8.3 of Severini (2000)].”
AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons (2011, background): “maximized log-likelihood (a fundamental quantity in mathematical statistics, written as log L ðÞ ). The concept of likelihood is fundamental to statistical theory (see Edwards, 1992, Azzalini 1996, Severini 2000, and Pawitan 2001).”
On quantum statistical inference (2003, background): “Some key references are Leonhardt (1997) and D’Ariano (1997a, b, 2001).”
Monte Carlo State-Space Likelihoods by Weighted Posterior Kernel Density Estimation (2004, methodology): “5), uses the multivariate Edgeworth approximation (e.g., Severini 2000, sec.”