The phrase "accurate variance" is correct and usable in written English.
It can be used in contexts where you are discussing statistical data, measurements, or differences that are precise and reliable.
Example: "The study revealed an accurate variance in the test scores, indicating a significant difference between the two groups."
Alternatives: "precise difference" or "exact variation."
Section 3 presents the calculation process of the accurate variance for fuzzy variables.
For binary outcomes, linearization methods of penalized quasi-likelihood (PQL) or marginal quasi-likelihood (MQL) provide relatively accurate variance estimates for fixed effects.
One of the benefits of using linear regression equations to define cVC rather than directly incorporating the logistic function is the calculation of an accurate variance estimate, whereas logistic regression functions are based on maximum likelihood and thus variances often do not converge on true values of variance and confidence levels are approximations.
Accurate variance estimation on both polygenes and QTL genes could improve selection schemes for animals.
However, distributing sampling time across more days also resulted in less accurate variance components when the block size was large.
Incorporating more informative priors, for example shrinkage priors, can produce more precise and accurate variance component estimates.
To begin with, this paper presents the calculation process of accurate variances using the original definition for three kinds of fuzzy variables with regular credibility distributions, that is, the symmetric triangular fuzzy number, the asymmetric triangular fuzzy number, and the Gaussian fuzzy number.
Price et al., (2005) noted technical challenges in obtaining accurate variances for the weighted statistics and proposed an improved bootstrap.
Estimating nutrient excretion with urinary creatinine and body weight on average is accurate, but variance is likely underestimated.
Although Donoho [1] gives the universal upper bound of threshold and researchers try to achieve even better solutions in [2, 4], performances of these algorithms largely hinge on accurate noise variance estimation.
To address this issue, the exact decision threshold for the probability of false-alarm for the MME detector with finite numbers of cooperative SUs and samples has been derived in [33], which will be discussed in Section 3. When accurate noise variance and PU signal power are unknown, blind moment-based spectrum sensing algorithms can be applied [34].
