" What are the properties of a point estimator? "

A point estimator is a statistic used to estimate a population parameter. The properties of a point estimator are: 1. *Unbiasedness*: The estimator's expected value equals the population parameter. 2. *Consistency*: The estimator converges to the population parameter as the sample size increases. 3. *Efficiency*: The estimator has the smallest variance among all unbiased estimators. 4. *Sufficiency*: The estimator uses all available information in the sample. 5. *Completeness*: The estimator is based on a complete statistic, which contains all information about the parameter. 6. *Minimum variance bound*: The estimator achieves the lowest possible variance among all unbiased estimators. 7. *Asymptotic normality*: The estimator is approximately normally distributed as the sample size increases. 8. *Asymptotic efficiency*: The estimator achieves the lowest possible variance in large samples. 9. *Consistent asymptotic normality*: The estimator is asymptotically normal and consistent. 10. *Second-order efficiency*: The estimator is efficient up to a second-order approximation. 11. *Median unbiasedness*: The estimator's median equals the population parameter. 12. *Pitman closeness*: The estimator is closest to the population parameter in terms of average squared error. 13. *Asymptotic unbiasedness*: The estimator is unbiased in large samples. 14. *Finite sample property*: The estimator has good properties in small samples. 15. *Robustness*: The estimator is insensitive to outliers and misspecification. These properties ensure that a point estimator is reliable, accurate, and efficient in estimating population parameters.