Likelihood inference in complex settings
نویسنده
چکیده
Inference based on the likelihood function owes much to theory developed some decades ago. What is the current role of likelihood in developing strategies for the analysis of very large data sets, often with very high dimension, and complex dependencies? This paper considers some aspects of this question with emphasis on problems in stochastic modelling, estimating equations, and surveymethodology. The Canadian Journal of Statistics 40: 1–14; 2012 © 2012 Statistical Society of Canada Résumé: Inference based on the likelihood function owes much to theory developed some decades ago. What is the current role of likelihood in developing strategies for the analysis of very large data sets, often with very high dimension, and complex dependencies? This paper considers some aspects of this question with emphasis on problems in stochastic modelling, estimating equations, and surveymethodology. La revue canadienne de statistique 40: 1–14; 2012 © 2012 Société statistique du Canada
منابع مشابه
Likelihood inference
The essential role of the likelihood function in both Bayesian and non-Bayesian inference is described. Several topics related to the extension of likelihood-based methodology to more complex settings are reviewed, including modifications to profile likelihood, composite and pseudo-likelihoods, quasi-likelihood, semiparametric and non-parametric likelihoods, and empirical likelihood . 2010 Jo...
متن کاملAccurate Inference for the Mean of the Poisson-Exponential Distribution
Although the random sum distribution has been well-studied in probability theory, inference for the mean of such distribution is very limited in the literature. In this paper, two approaches are proposed to obtain inference for the mean of the Poisson-Exponential distribution. Both proposed approaches require the log-likelihood function of the Poisson-Exponential distribution, but the exact for...
متن کاملComputation of the Empirical Likelihood Ratio from Censored Data
The empirical likelihood ratio method is a general nonparametric inference procedure that has many desirable properties. Recently, the procedure has been generalized to several settings including testing of weighted means with right censored data. However, the computation of the empirical likelihood ratio with censored data and other complex settings is often non-trivial. We propose to use a se...
متن کاملModified signed log-likelihood test for the coefficient of variation of an inverse Gaussian population
In this paper, we consider the problem of two sided hypothesis testing for the parameter of coefficient of variation of an inverse Gaussian population. An approach used here is the modified signed log-likelihood ratio (MSLR) method which is the modification of traditional signed log-likelihood ratio test. Previous works show that this proposed method has third-order accuracy whereas the traditi...
متن کاملPseudo-Likelihood Inference Underestimates Model Uncertainty: Evidence from Bayesian Nearest Neighbours
When using the K-nearest neighbours (KNN) method, one often ignores the uncertainty in the choice of K. To account for such uncertainty, Bayesian KNN (BKNN) has been proposed and studied (Holmes and Adams 2002 Cucala et al. 2009). We present some evidence to show that the pseudo-likelihood approach for BKNN, even after being corrected by Cucala et al. (2009), still significantly underest...
متن کامل