Sentence examples for interesting estimator from inspiring English sources

Therefore, the linear least mean squares estimator has the following form dhat{x}=gamma begin{pmatrix} -1-cos(theta)&sin(theta) -sin(theta)&-1-cos(theta) end{pmatrix} hat{x},dt, which is not an interesting estimator in practice as there is no dy term, similar to Example 1.

Although the main purpose of the example applications was to demonstrate the performance of the estimator, some interesting preliminary results about the two applications were also obtained.

A balanced sampling design has the interesting property that Horvitz Thompson estimators of totals for a set of balancing variables are equal to the totals we want to estimate, therefore the variance of Horvitz Thompson estimators of variables of interest are reduced in function of their correlations with the balancing variables.

Although we have considered several interesting classes of visual motion estimators, the space of possible motion estimators is much larger.

It is interesting to observe that the estimator in (25) takes a simple form when the optimal preamble is used.

We remark that although the CRB is a bound for unbiased estimators, it still is interesting to compare it with the proposed biased estimators.

In order to understand the derivation of the STO estimator below, it is interesting to investigate the amplitude of the received preamble |y k [ m] | on the different subcarriers k for all sample instants m.

It is interesting to notice the link between cross-correntropy estimators and information-theoretic estimators, when the variables X and Y are independent.

It is interesting to see that the leave-one-out CoD estimator beats the more complex cross-validation CoD estimator for small number of bins and large sample size.

It may also be interesting to explore the use of the LASSO estimator for GWAS studies as a means to shrink the variable set in place of REML estimators.

The latter is more complicated and interesting because an optimal strategy consists not only of an estimator, but also of a sampling design, and the estimator may depend on the design and on the labels of sampled individuals, whereas in iid sampling, design issues and labels do not exist.