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.
JSTOR Daily

Maximum Likelihood Estimation of a Polynomial Regression Function with Grouped Data

Journal of the Royal Statistical Society. Series C (Applied Statistics) When the data in a polynomial regression problem come in grouped form, finding the maximum likelihood estimates of the ...
Simon Fraser University

Parameter Estimation

Maximum likelihood estimation of the parameters of a statistical model involves maximizing the likelihood or, equivalently, the log likelihood with respect to the parameters. The parameter values at ...
JSTOR Daily

Nonparametric Maximum Likelihood Estimation for Shifted Curves

Journal of the Royal Statistical Society. Series B (Statistical Methodology), Vol. 63, No. 2 (2001), pp. 243-259 (17 pages) The analysis of a sample of curves can be done by self-modelling regression ...
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 ...
Nature

Weibull Distribution Parameter Estimation Techniques

The Weibull distribution remains an indispensable tool in reliability engineering and lifetime analysis, offering flexibility for modelling diverse failure behaviours. Modern parameter estimation ...