Estimating the Parameters of Log-Normal Distribution from Censored Samples

Let g(x) be the ratio of the ordinate and the probability integral of a standardized normal distribution. The local approximation g(x) ≃α + βx is used to derive estimators μc, σc and τc of the parameters of a log-normal distribution, from a type II censored sample. The asymptotic variances and covariances of these estimators are obtained. The estimators are shown to be asymptotically as efficient as the maximum likelihood estimators. Numerical example is presented to give an idea of the computation involved in calculating them. It may be noted that these estimators can be computed by means of a systematic routine whereas the maximum likelihood estimators cannot.