Nature

Bayesian Estimation and Maximum Likelihood Methods

Bayesian estimation and maximum likelihood methods represent two central paradigms in modern statistical inference. Bayesian estimation incorporates prior beliefs through Bayes’ theorem, updating ...
Risk

Estimating operational risk capital: the challenges of truncation, the hazards of maximum likelihood estimation, and the promise of robust statistics

In operational risk measurement, the estimation of severity distribution parameters is the main driver of capital estimates, yet this remains a nontrivial challenge for many reasons. Maximum ...
Simon Fraser University

MLE for User-Defined Likelihood Functions

There are three SAS procedures that enable you to do maximum likelihood estimation of parameters in an arbitrary model with a likelihood function that you define: PROC MODEL, PROC NLP, and PROC IML.
Simon Fraser University

Example 5.6: Maximum Likelihood Weibull Estimation

The following data are taken from Lawless (1982, p.193) and represent the number of days it took rats painted with a carcinogen to develop carcinoma. The last 2 observations are censored data from a ...
Risk

Operational risk models and asymptotic normality of maximum likelihood estimation

Operational risk models commonly employ maximum likelihood estimation (MLE) to fit loss data to heavy-tailed distributions. Yet several desirable properties of MLE (eg, asymptotic normality) are ...