2012
DOI: 10.2478/v10178-012-0070-3
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Standard Uncertainty Determination of the Mean for Correlated Data Using Conditional Averaging

Abstract: 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… Show more

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Cited by 8 publications
(6 citation statements)
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“…New possibilities in this scope for linear and non-linear models in metrological applications appear thanks to the use of functional and numerical conditional characteristics, in particular those of the conditional expected value and the conditional variance [14,15]. The conditional expected value ensures the best estimate of interdependencies of stochastic signals in the mean square sense.…”
Section: Conditional Averaging Of Auto-correlated Datamentioning
confidence: 99%
See 2 more Smart Citations
“…New possibilities in this scope for linear and non-linear models in metrological applications appear thanks to the use of functional and numerical conditional characteristics, in particular those of the conditional expected value and the conditional variance [14,15]. The conditional expected value ensures the best estimate of interdependencies of stochastic signals in the mean square sense.…”
Section: Conditional Averaging Of Auto-correlated Datamentioning
confidence: 99%
“…In a simplified model of averaging non-correlated M fragments of x(t), after exceeding the ( ) p x t x = level [14], the assessment of the relative standard uncertainty of the conditional value of arithmetic mean is:…”
Section: Conditional Averaging Of Auto-correlated Datamentioning
confidence: 99%
See 1 more Smart Citation
“…To assess and eliminate uncertainty related to the incorrect choice of the number of experts, it is important to consider the statements of the probability theory (namely, the expression of an error confidence level) [22,23] and represent the evaluation of the expert number C expert at the given confidence probability P within the range of values inherent in metrology -namely, from 0.9 to 0.99 with the error Δ. Using the expression for calculating a confidence interval, the formula for calculating C expert , which is a prototype of the number of observations, could be written as follows: 2 2…”
Section: Wrong Choice Of the Number Of Expertsmentioning
confidence: 99%
“…Measured data are not random sampling data with normal distribution but autocorrelated time series. Calculation of confidence interval is not straightforward for such data [9], [10]. The number of samples required for the mean estimation was determined by means of the experimental method from the long time measurement.…”
Section: Time Lag [Number Of Samples]mentioning
confidence: 99%