finite variance in a sentence

"finite variance" in Chinese  
  1. Let be two random variables with means and finite variances respectively.
  2. If in addition each has finite variance, then by the central limit theorem,
  3. Modeling the changes by distributions with finite variance is now known to be inappropriate.
  4. Without the finite variance assumption, the limit may be a stable distribution that is not normal.
  5. This example serves to show that the hypothesis of finite variance in the central limit theorem cannot be dropped.
  6. It's difficult to find finite variance in a sentence.
  7. These formulas are accurate extrapolations of a random walk, or Wiener process, whose steps have finite variance.
  8. In general, modeling the changes by distributions with finite variance is, increasingly, said to be inappropriate.
  9. A finite variance is required in addition in order for the standard errors of estimates of the L-moments to be finite.
  10. Many other distributions for which the expected value does exist also do not have a finite variance because the integral in the variance definition diverges.
  11. This is a generalization of the requirement that have finite variance, and is necessary for this strong form of Chebyshev's inequality in infinite dimensions.
  12. Similar to Stein's method the zero bias transform is often applied to sums of random variables with each summand having finite variance an mean zero.
  13. Here, the \ varepsilon _ i are unobserved stochastic error terms with zero mean and finite variance, which may not be the same for all i.
  14. The central limit theorem states that the sum of a number of independent and identically distributed random variables with finite variances will tend to a normal distribution as the number of variables grows.
  15. Some authors use the term to refer to those distributions which do not have all their power moments finite; and some others to those distributions that do not have a finite variance.
  16. Whereas the central limit theorem for sums of random variables requires the condition of finite variance, the corresponding theorem for products requires the corresponding condition that the density function be square-integrable.
  17. More:   1  2

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