A Few Properties of Sample Variance

A basic result is that the sample variance for i.i.d. observations is an unbiased estimator of the variance of the underlying distribution (see for instance Casella and Berger (2002)). Another result is that the sample variance 's variance is minimum compared to any other unbiased estimators (see Halmos (1946)). But what happens if the observations are neither independent nor identically distributed. What can we say? Can we in particular compute explicitly the first two moments of the sample mean and hence generalize formulae provided in Tukey (1957a), Tukey (1957b) for the first two moments of the sample variance? We also know that the sample mean and variance are independent if they are computed on an i.i.d. normal distribution. This is one of the underlying assumption to derive the Student distribution Student alias W. S. Gosset (1908). But does this result hold for any other underlying distribution? Can we still have independent sample mean and variance if the distribution is not normal? This paper precisely answers these questions and extends previous work of Cho, Cho, and Eltinge (2004). We are able to derive a general formula for the first two moments and variance of the sample variance under no specific assumptions. We also provide a faster proof of a seminal result of Lukacs (1942) by using the log characteristic function of the unbiased sample variance estimator.