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It is fairly readily shown that the maximum-likelihood estimate of the mean vector μ is the "sample mean" vector: ¯ = + +. See the section on estimation in the article on the normal distribution for details; the process here is similar.
The sample covariance matrix has in the denominator rather than due to a variant of Bessel's correction: In short, the sample covariance relies on the difference between each observation and the sample mean, but the sample mean is slightly correlated with each observation since it is defined in terms of all observations.
In such settings, the sample mean is considered to meet the desirable criterion for a "good" estimator in being unbiased; that is, the expected value of the estimate is equal to the true value of the underlying parameter.
But we may have some guess as to what the mean vector is. This can be considered a disadvantage of the estimator: the choice is not objective as it may depend on the beliefs of the researcher. Nonetheless, James and Stein's result is that any finite guess ν improves the expected MSE over the maximum-likelihood estimator, which is tantamount to ...
That is, for any constant vector , the random variable = has a univariate normal distribution, where a univariate normal distribution with zero variance is a point mass on its mean. There is a k-vector and a symmetric, positive semidefinite matrix , such that the characteristic function of is () = ().
Important examples include the sample variance and sample standard deviation. Without Bessel's correction (that is, when using the sample size instead of the degrees of freedom), these are both negatively biased but consistent estimators. With the correction, the corrected sample variance is unbiased, while the corrected sample standard ...
As an example, consider a random sample of size 6. In that case, the sample median is usually defined as the midpoint of the interval delimited by the 3rd and 4th order statistics. However, we know from the preceding discussion that the probability that this interval actually contains the population median is [ clarification needed ]
For example, some authors [6] define φ X (t) = E[e −2πitX], which is essentially a change of parameter. Other notation may be encountered in the literature: p ^ {\displaystyle \scriptstyle {\hat {p}}} as the characteristic function for a probability measure p , or f ^ {\displaystyle \scriptstyle {\hat {f}}} as the characteristic function ...