SUMMARY
The discussion centers on the unbiasedness of the Method of Moments Estimator (MME) for the parameter θ, derived from the sample mean and sample variance. The key equations presented include \(E(X) = \bar{X}\) and \(\theta = \frac{1}{n} \sum_{i=1}^{n} X_{i}^{2}\), confirming that the MME is indeed an unbiased estimator. The notation clarification emphasizes that \(\hat{\theta} = \frac{1}{n} \sum_{i=1}^{n} X_i^2\) should be used to avoid confusion. The final conclusion is that \(E(\hat{\theta}) = \bar{X^2} = \theta\), validating the unbiased nature of the estimator.
PREREQUISITES
- Understanding of statistical estimators, specifically Method of Moments
- Familiarity with expectation and variance calculations
- Knowledge of sample mean and sample variance concepts
- Proficiency in mathematical notation and symbols used in statistics
NEXT STEPS
- Study the properties of unbiased estimators in statistical theory
- Learn about the Method of Moments and its applications in parameter estimation
- Explore the derivation of variance and expectation in statistical contexts
- Investigate other estimation methods, such as Maximum Likelihood Estimation (MLE)
USEFUL FOR
Statisticians, data analysts, and students studying statistical estimation methods who seek to deepen their understanding of unbiased estimators and the Method of Moments.