Classical and Bayesian Methods in Estimation of a Scale Parameter of an Erlang Distribution

E.S. Oguntade and D.M. Oladimeji

This study examined the Bayesian and Classical approaches in the estimation of a scale a parameter of an Erlang distribution under different loss functions (both symmetric and asymmetric loss functions) and prior probabilities. The shape parameter was assumed known while the scale parameter was assumed to follow Jeffrey’s, quasi and uniform priors. A square error loss, entropy loss and linear exponential loss functions were considered. Under different combinations and scenarios of these priors and loss functions, the estimators of the scale parameter of an Erlang distribution were derived using R software. The estimators were compared and the best estimator was chosen based on having the least value of the mean square error of the estimates.

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