00:01
Hello students, in this question, estimator means it is an estimator or point estimator is a statistic that is a function of the data that is used to infer the value of an unknown parameter in a statistical model then it is called estimator.
00:16
So in estimator we have a types so unbiased estimator, consistent estimator and efficient estimator.
00:23
Here unbiased estimator means unbiased estimator means an estimator is a statistic that is used to estimate a population parameter.
00:36
An unbiased estimator is a statistic that has an expected value that has an expected expected value equal to the true value of equal to the true value of equal to the true value of population parameter being estimated population parameter population parameter being estimated population parameter being estimated being estimated.
01:10
So in other words the expected value of an unbiased estimator is the same as the actual population parameter.
01:18
So an unbiased is an estimator is unbiased if its expected value converges to the true population parameter as expectation of theta is equal to theta where theta is the population parameter being estimated and the expectation of theta is the expected value of the estimator and after that after that the next estimator is the next estimator is consistent estimator consistent estimator.
01:49
So consistent estimator and a consistent estimator is a statistic that converges in probability to the true value of the population parameter as the sample size grows larger.
02:01
So it is also a statistic that converges it converges converges in probability in probability to the probability to the true value probability to the true value of the population parameter of the population parameter as the as the sample size as the sample size grows larger sample size grows larger sample size grows larger...