Because it is easily influenced by extreme values (i.e. it is not unbiased).
what is the use and application of ratio estimator?
I think, the estimate is a numerical value, wile the estimator is a function or operator, which can be generate more estimates according to some factors. For example (xbar) is estimator for (meu), which can be various when the sample size in various, the value that will be produced is an (estimate), but (xbar) is estimator.
An outlier pulls the median towards it. For example 1,2,3 Median=2 1,2,3,7 Median=2.5
it doesnt have a median.
There's is always going to be a median. Never is there not going to be a median.
I believe you want to say, "as the sample size increases" I find this definition on Wikipedia that might help: In statistics, a consistent sequence of estimators is one which converges in probability to the true value of the parameter. Often, the sequence of estimators is indexed by sample size, and so the consistency is as sample size (n) tends to infinity. Often, the term consistent estimator is used, which refers to the whole sequence of estimators, resp. to a formula that is used to obtain a term of the sequence. So, I don't know what you mean by "the value of the parameter estimated F", as I think you mean the "true value of the parameter." A good term for what the estimator is attempting to estimate is the "estimand." You can think of this as a destination, and your estimator is your car. Now, if you all roads lead eventually to your destination, then you have a consistent estimator. But if it is possible that taking one route will make it impossible to get to your destination, no matter how long you drive, then you have an inconsistent estimator. See related links.
Estimator is the correct spelling.
In statistics, an efficient estimator is an estimator that estimates the quantity of interest in some "best possible" manner
what is the use and application of ratio estimator?
what is another name for estimator
The answer depends on what variable the maximum likelihood estimator was for: the mean, variance, maximum, median, etc. It also depends on what the underlying distribution is. There is simply too much information that you have chosen not to share and, as a result, I am unable to provide a more useful answer.
Answer this question Critria of good estimator
The best point estimator of the population mean would be the sample mean.
There are four main properties associated with a "good" estimator. These are: 1) Unbiasedness: the expected value of the estimator (or the mean of the estimator) is simply the figure being estimated. In statistical terms, E(estimate of Y) = Y. 2) Consistency: the estimator converges in probability with the estimated figure. In other words, as the sample size approaches the population size, the estimator gets closer and closer to the estimated. 3) Efficiency: The estimator has a low variance, usually relative to other estimators, which is called relative efficiency. Otherwise, the variance of the estimator is minimized. 4) Robustness: The mean-squared errors of the estimator are minimized relative to other estimators.
I think, the estimate is a numerical value, wile the estimator is a function or operator, which can be generate more estimates according to some factors. For example (xbar) is estimator for (meu), which can be various when the sample size in various, the value that will be produced is an (estimate), but (xbar) is estimator.
The majority of the major car manufacturers have a car payment estimator on their web sites. Most banking institutions may have this function as well. The payment is just an estimator.
The proof that demonstrates the unbiased estimator of variance involves showing that the expected value of the estimator equals the true variance of the population. This is typically done through mathematical calculations and statistical principles to ensure that the estimator provides an accurate and unbiased estimate of the variance.