Sentence examples for estimation methods such as from inspiring English sources

The channel impulse response (CIR) can be estimated by channel estimation methods such as zero forcing and MMSE.

In order to blindly estimate a sequence transmitted over a sparse channel, popular sparse channel estimation methods such as matching pursuit (MP), orthogonal matching pursuit (OMP), and basis pursuit (BP) [12 14] can be combined with the VA or stack algorithm using a joint channel estimation and data detection framework.

However, this requires maximum likelihood estimation based on adaptive Gaussian quadrature to be used; other estimation methods such as penalized quasi likelihood (PQL) can lead to biased estimates of the covariate effects [ 18].

Important examples of failure arise in the use of simulation based estimation methods such as indirect inference.

Comparisons are conducted against a denoising technique based on the wavelet transform and also with traditional drift estimation methods such as Kalman filtering and running average.

Conventional frequency response estimation methods such as the "H1" and "H2" methods often yield measured frequency response functions which are contaminated by the presence of non-linearities and hence make it difficult to extract underlying linear system properties.

In this approach, f(x) is first estimated using a standard density estimation method such as a histogram or kernel density estimator, and the entropy is then computed.

The main message is that the theoretical loss of efficiency and accuracy using SR is likely to be minimal as evident in these simulation studies but as illustrated below, SR can provide additional information to aid the fitting of a suitable distribution which is not attainable by using an estimation method such as PWM or MLE directly.

A typical implementation of a basic multi-component IF estimation method such as that proposed in [42] includes the following two steps: (a) T-F transformation: The given signal is first mapped to the T-F domain using a suitable TFD which is chosen based on the characteristics of the signal under analysis.

A formal test of overdispersion may be applied, the outcome of which may suggest an alternative estimation method, such as a negative binomial (negbin) approach [ 20, 21].

It is still necessary for the mass estimation of loose parts, one function of a loose part monitoring system, to develop a new method due to the high estimation error of conventional methods such as Hertz's impact theory and the frequency ratio method.