In a previous work [7], the mean and standard deviation were estimated by arranging all the sample elements in ascending order and taking the best linear combination of them. We will use here the same principle to estimate the mean and standard deviation of certain populations from singly and doubly censored samples. Censored samples may be considered as truncated samples having a known number of unmeasured (missing) observations, i.e., those in which the total number of sample elements is known, but measurements on some of which are lacking. In life testing, fatigue testing, and in other tests of a destructive nature, we have $n$ items drawn at random from some population which when subjected to a test, fail in order of time. To save time and/or items, it is often required to stop the experiment (to censor the sample) after recording the first $r (