Quasi-likelihood and its application: A general approach to optimal parameter estimation.

One important aspect of bringing a model online is parameter estimation.

Algebra has been useful for experimental design, parameter estimation, and hypothesis testing.

Like N-gram models, smoothing techniques are necessary in parameter estimation.

The fitting can include both variable selection and parameter estimation.

For model convergence, and therefore parameter estimation, it is often necessary that the data have little or no collinearity.

Small sample sizes occur in practice and present unusually difficult problems for parameter estimation.

A large number of procedures have been developed for parameter estimation and inference in linear regression.

The foundation of adaptive control is parameter estimation.

A textbook example is parameter estimation of a probability distribution function.