Statistical Inference
Neyman Fisher Factorization theorem is also known as:
Theorem of sufficient estimators
Rao Black-well theorem
Estimator
None of these
Theorem of sufficient estimators
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Mean square of an estimator is equal to:A. Variance + (Bias)2
B. E(x) + (Bias)2
C. (Bias)2
D. Variance
If expected value of an estimator is equal to its respective parameter then this property known is:
A. Biasedness
B. Estimation
C. Unbiasedness
D. Both B and C
In case of unbiased estimators, the estimator having minimum variance is called an:
A. Efficient estimator.
B. Sufficient
C. Both A and B
D. Consistent
Statistic may be an:
A. Estimator
B. Estimate
C. Both A and B
D. None of these
If then the statistic โtโ is called:
A. Complete
B. Unbiased
C. Both A and B
D. Sufficient
In Cramer-Rao Inequality var (T) is called:
A. Complete
B. Minimum variance bond
C. Efficient
D. None of these
The value which is obtain by applying an estimator on sample information is known as an:
A. Estimation
B. Estimator
C. Both A and B
D. Estimate
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