online assignment help, online homework help, economics online tutor online homework help, economics online tutor, online assignment help
online assignment help, economics online tutor, online homework help submit homework for free, submit assignment for free online homework help, online assignment helponline tutor help, online live tutoring
tutor help desk about tutorhelpdesk.com how tutorhelpdesk services work tutorhelpdesk affordable pricing sample homework, sample assignment frequently asked questions, faq contact tutorhelpdesk tutorhelpdesk blog
 
 
Login / Register

 
Submit HomeWork Assignment for free quote

 
Questions & Answers
FORUM


 
online assignment help, online homework help
Math Assignment Help
Physics Assignment Help
Chemistry Assignment Help
Biology Assignment Help
Biotechnology Assignment Help
Economics Assignment Help
Statistics Assignment Help
Accounting Assignment Help
Finance Assignment Help
Management Assignment Help
Operations Management Assignment Help
Computer Science Assignment Help
Engineering Assignment Help

 

     Properties Of Good Estimator Homework Help
     Properties Of Good Estimator Assignment Help

Properties of Good Estimator

A distinction is made between an estimate and an estimator. The numerical value of the sample mean is said to be an estimate of the population mean figure. On the other hand, the statistical measure used, that is, the method of estimation is referred to as an estimator. A good estimator, as common sense dictates, is close to the parameter being estimated. Its quality is to be evaluated in terms of the following properties:

1. Unbiasedness.

An estimator is said to be unbiased if its expected value is identical with the population parameter being estimated. That is if θ is an unbiased estimate of θ, then we must have E (θ) = θ. Many estimators are “Asymptotically unbiased” in the sense that the biases reduce to practically insignificant value (Zero) when n becomes sufficiently large. The estimator S2 is an example.
It should be noted that bias is estimation is not necessarily undesirable. It may turn out to be an asset in some situations.

2. Consistency.

If an estimator, say θ, approaches the parameter θ closer and closer as the sample size n increases, θ is said to be a consistent estimator of θ. Stating somewhat more rigorously, the estimator θ is said is be a consistent estimator of θ if, as n approaches infinity, the probability approaches 1 that θ will differ from the parameter θ by no more than an arbitrary constant.
The sample mean is an unbiased estimator of µ no matter what form the population distribution assumes, while the sample median is an unbiased estimate of µ only if the population distribution is symmetrical. The sample mean is better than the sample median as an estimate of µ in terms of both unbiasedness and consistency.

3. Efficiency.

The concept of efficiency refers to the sampling variability of an estimator. If two competing estimators are both unbiased, the one with the smaller variance (for a given sample size) is said to be relatively more efficient. Stated in a somewhat different language, an estimator θ is said to be more efficient than another estimator θ2 for θ if the variance of the first is less than the variance of the second. The smaller the variance of the estimator, the more concentrated is the distribution of the estimator around the parameter being estimated and, therefore, the better this estimator is.

4. Sufficiency.

An estimator is said to be sufficient if it conveys much information as is possible about the parameter which is contained in the sample. The significance of sufficiency lies in the fact that if a sufficient estimator exists, it is absolutely unnecessary to considered any other estimator; a sufficient estimator ensures that all information a sample a sample can furnished with respect to the estimation of a parameter is being utilized.
Many methods have been devised for estimating parameters that may provide estimators satisfying these properties. The two important methods are the least square method and the method of maximum likelihood.
 
For more help in Properties of Good Estimator click the button below to submit your homework assignment
 
 
 
 
 
   
math homework help | physics homework help | chemistry homework help | biology homework help | economics homework help | statistics homework help | accounting homework help | finance homework help | finance assignment help | management homework help | operations management homework help | computer science homework help | engineering homework help

Home - Contact us - Reviews - Links - Careers - Sitemap - Terms & Conditions - Privacy Policy - Disclaimer - XML Sitemap
Copyright 2010-2014 Tutorhelpdesk.com