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Normal Unbiased Estimator implies the Mean Absolute & square-root Mean Squared Loss are Proportional
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Classroom Contents
Parameter Estimation
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- 1 Empirical Substitution: Frequency Substitution
- 2 Empirical Substitution: Method of Moments
- 3 Maximum Likelihood Estimation
- 4 Prior and Posterior Distributions
- 5 Bayes Estimation
- 6 Bayes Estimation for the Variance of a Normal Distribution
- 7 Bayesian Estimation - General Linear Model
- 8 Sufficient Statistic
- 9 Fisher-Neyman Factorization Theorem
- 10 One-to-One Functions of Sufficient Statistics
- 11 Sufficient Statistics - Examples
- 12 Jointly Sufficient Statistics - Examples
- 13 Distribution of a sufficient statistics from a 1-parameter exponential family
- 14 Minimal Sufficient Statistics
- 15 Minimally Sufficient Statistic and Maximum Likelihood Estimation
- 16 Ancillary Statistic
- 17 Ancillary Statistic: Example
- 18 Complete Statistics
- 19 Basu's Theorem
- 20 Basu's Theorem: Examples
- 21 Unbiased Estimate and Mean Squared Error
- 22 Unbiased Estimates for Population Std Dev using the Sample Mean Absolute Dev and the Sample Std Dev
- 23 Normal Unbiased Estimator implies the Mean Absolute & square-root Mean Squared Loss are Proportional
- 24 Rao - Blackwell Theorem
- 25 Lehmann - Scheffe Theorem
- 26 Fisher's Information: Examples
- 27 Fisher's Information: Cauchy Distribution
- 28 Cramer-Rao Lower Bound / Inequality
- 29 Exponential Family: Cramer-Rao Lower Bound
