Theory of Estimation

A quantity obtained by applying certain rule or formula is known as

• sample

• Test statistic

• Estimate

• Estimator

Consistency of an estimator can be checked by comparing

• Mean

• Mean Square

• Variance

• Standard Deviation

If Var(T₂)<Var(T₁) then T₂ is

• Unbiased

• Efficient

• Sufficient

• Consistent

If V(θ)→0 as n→∞, then θ is said to be

• Unbiased

• Sufficient

• Efficient

• Consistent

An estimator T_n is said to be as sufficeint statistic for a parameter function τ(θ) if it contains all the information which is contained in the

• Population

• Parameter

• Sample

• Statistic

The way of finding the unknown value of the population parameter from the sample values by using a formula is called

• Estimator

• Estimation

• Estimate

• Bias

The probability that the confidence interval does not contain the population parameter is denoted by

• α

• β

• 1-α

• 1-β

When the variance of an estimator obtains its Cramer-Rao lower bound for variance, then this estimator will be

• Unbiased

• Efficient

• Sufficient

• Consistent

The formula for mean squared error of an estimator T_n is

• bias+V(T_n)

• [bias+V(T_n)]²

• bias²+[V(T_n)]²

• bias²+V(T_n)

If t is a consistent estimator of θ then

• t is a consistent estimator of θ²

• t² is a consistent estimator of θ

• t² is a consistent estimator of θ²

• None of them