efficient estimator造句

例句与造句

1. Mean squared error is used for obtaining efficient estimators, a widely used class of estimators.
2. It is also an efficient estimator, i . e . its estimation variance achieves the Cram閞 Rao lower bound ( CRLB ).
3. The primary goal is to obtain an efficient estimator \ widehat { \ boldsymbol \ beta } for the parameter \ boldsymbol \ beta, based on the data.
4. Despite its simplicity, the interdecile range of a sample drawn from a normal distribution can be divided by 2.56 to give a reasonably efficient estimator of the standard deviation of a normal distribution.
5. However, with clean data or in theoretical settings, they can sometimes prove very good estimators, particularly for platykurtic distributions, where for small data sets the mid-range is the most efficient estimator.
6. It's difficult to find efficient estimator in a sentence. 用efficient estimator造句挺难的
7. In some cases an unbiased efficient estimator exists, which, in addition to having the lowest variance among unbiased estimators, satisfies the Cram閞 Rao bound, which is an absolute lower bound on variance for statistics of a variable.
8. Despite its drawbacks, in some cases it is useful : the midrange is a highly efficient estimator of ?, given a small sample of a sufficiently platykurtic distribution, but it is inefficient for mesokurtic distributions, such as the normal.
9. However, it finds some use in special cases : it is the maximally efficient estimator for the center of a uniform distribution, trimmed mid-ranges address robustness, and as an L-estimator, it is simple to understand and compute.
10. A more efficient estimator is given by instead taking the 7 % trimmed range ( the difference between the 7th and 93rd percentiles ) and dividing by 3 ( corresponding to 86 % of the data falling within ?.5 standard deviations of the mean in a normal distribution ); this yields an estimator having about 65 % efficiency.
11. For small sample sizes ( " n " from 4 to 20 ) drawn from a sufficiently platykurtic distribution ( negative excess kurtosis, defined as ? 2 = ( ? 4 / ( ? 2 ) ?) & minus; 3 ), the mid-range is an efficient estimator of the mean " ? ".
12. Thus in that case, the corresponding \ widehat { \ boldsymbol { \ beta } } _ { k } would be a more efficient estimator of \ boldsymbol { \ beta } compared to \ widehat { \ boldsymbol { \ beta } } _ \ mathrm { ols }, based on using the mean squared error as the performance criteria.
13. For estimating the standard deviation of a normal distribution, the scaled interdecile range gives a reasonably efficient estimator, though instead taking the 7 % trimmed range ( the difference between the 7th and 93rd percentiles ) and dividing by 3 ( corresponding to 86 % of the data of a normal distribution falling within 1.5 standard deviations of the mean ) yields an estimate of about 65 % efficiency.