Title :
Estimating the standard deviation from extreme Gaussian values
Author :
Stark, Henry ; Brankov, Jovan G.
Author_Institution :
Dept. of Electr. & Comput. Eng., Illinois Inst. of Technol., Chicago, IL, USA
fDate :
3/1/2004 12:00:00 AM
Abstract :
We derive an estimator for the standard deviation of a Gaussian random variable that uses the maximum of two observations on the random variables. The process is repeated until the variance of the estimator or the degree of confidence reaches a predetermined level. The estimator is unbiased and consistent, and its variance is only marginally larger than the standard square root of the sum of the squares estimator. Moreover the computation of an estimate requires only a sequence of comparisons of two numbers followed by an addition.
Keywords :
Gaussian distribution; maximum likelihood estimation; normal distribution; Gaussian distribution; degree of confidence; estimator variance; extreme Gaussian random variable; extreme value distribution; normal distribution; square estimator; standard deviation estimation; Distribution functions; Equations; Gaussian distribution; Maximum likelihood estimation; Probability density function; Random variables; Reactive power; Yield estimation;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2003.821728