Aspects of likelihood inference

Abstract

I review the classical theory of likelihood based inference and consider how it is being extended and developed for use in complex models and sampling schemes.

Published in at http://dx.doi.org/10.3150/12-BEJSP03 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)

References in corpus (4)

• Statistical Modeling of Spatial Extremes
• Extending the scope of empirical likelihood
• Nonconcave penalized composite conditional likelihood estimation of sparse Ising models
• Statistical inference for max-stable processes in space and time

Cited by in corpus (2)

• Regularization of the ensemble Kalman filter using a non-parametric, non-stationary spatial model
• On divergences tests for composite hypotheses under composite likelihood