Site Analysis of cantewindsfarm.com (visit)
Estimated value: €81,184 |
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| Server IP | 168.144.154.73 |
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| Server Location | Canada - CA , CAN
Region: ON , Ontario
City: Toronto
Postal code: m5j2r8
Latitude: 43.6667
, Longitude: -79.4168
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| | Alexa Traffic Rank | 12,365,752 | | Archive of the website | | | See how this website looked in the past | Link to the archive of the site |
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Estimation process
The entire purpose of estimation theory is to arrive at an estimator, and preferably an implementable one that could actually be used. The estimator takes the measured data as input and produces an estimate of the parameters.
It is also preferable to derive an estimator that exhibits optimality. Estimator optimality usually refers to achieving minimum average error over some class of estimators, for example, a minimum variance unbiased estimator. In this case, the class is the set of unbiased estimators, and the average error measure is variance (average squared error between the value of the estimate and the parameter). However, optimal estimators do not always exist.
These are the general steps to arrive at an estimator:
* In order to arrive at a desired estimator for estimating a single or multiple parameters, it is first necessary to determine a model for the system. This model should incorporate the process being modeled as well as points of uncertainty and noise. The model describes the physical scenario in which the parameters apply.
* After deciding upon a model, it is helpful to find the limitations placed upon an estimator. This limitation, for example, can be found through the Cramér-Rao bound.
* Next, an estimator needs to be developed or applied if an already known estimator is valid for the model. The estimator needs to be tested against the limitations to determine if it is an optimal estimator (if so, then no other estimator will perform better).
* Finally, experiments or simulations can be run using the estimator to test its performance.
After arriving at an estimator, real data might show that the model used to derive the estimator is incorrect, which may require repeating these steps to find a new estimator. A non-implementable or infeasible estimator may need to be scrapped and the process started anew.
In summary, the estimator estimates the parameters of a physical model based on measured data.
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