TY - JOUR
N2 - The correlation of data contained in a series of signal sample values makes the estimation of the statistical characteristics describing such a random sample difficult. The positive correlation of data increases the arithmetic mean variance in relation to the series of uncorrelated results. If the normalized autocorrelation function of the positively correlated observations and their variance are known, then the effect of the correlation can be taken into consideration in the estimation process computationally. A significant hindrance to the assessment of the estimation process appears when the autocorrelation function is unknown. This study describes an application of the conditional averaging of the positively correlated data with the Gaussian distribution for the assessment of the correlation of an observation series, and the determination of the standard uncertainty of the arithmetic mean. The method presented here can be particularly useful for high values of correlation (when the value of the normalized autocorrelation function is higher than 0.5), and for the number of data higher than 50. In the paper the results of theoretical research are presented, as well as those of the selected experiments of the processing and analysis of physical signals.
L1 - http://sd.czasopisma.pan.pl/Content/90042/PDF/Journal10178-VolumeXIXIssue4_16.pdf
L2 - http://sd.czasopisma.pan.pl/Content/90042
PY - 2012
IS - No 4
EP - 787-796
KW - uncertainty of the mean value
KW - autocorrelated data
KW - conditional averaging
KW - random signal.
A1 - Kowalczyk, Adam
A1 - Szlachta, Anna
A1 - Hanus, Robert
PB - Polish Academy of Sciences Committee on Metrology and Scientific Instrumentation
SP - 787-796
T1 - Standard Uncertainty Determination of the Mean for Correlated Data Using Conditional Averaging
DA - 2012
UR - http://sd.czasopisma.pan.pl/dlibra/publication/edition/90042
T2 - Metrology and Measurement Systems
DOI - 10.2478/v10178-012-0070-3
ER -