b.lect21 - Outline Introduction General Principles:...

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Unformatted text preview: Outline Introduction General Principles: Unbiasedness and Standard Error Comparing Any Two Estimators The Method of Moments Lecture 21 Chapter 7: Point Estimation: Fitting Models to Data Michael Akritas Michael Akritas Lecture 21 Chapter 7: Point Estimation: Fitting Models to Dat Outline Introduction General Principles: Unbiasedness and Standard Error Comparing Any Two Estimators The Method of Moments Introduction General Principles: Unbiasedness and Standard Error Definition of Unbiased Estimators The Standard Error Selection Criterion Comparing Any Two Estimators Are There Biased Estimators? The Mean Square Error (MSE) Selection Criterion The Method of Moments The Method for Two-Parameter Models Examples of Moment Estimators Michael Akritas Lecture 21 Chapter 7: Point Estimation: Fitting Models to Dat Outline Introduction General Principles: Unbiasedness and Standard Error Comparing Any Two Estimators The Method of Moments Michael Akritas Lecture 21 Chapter 7: Point Estimation: Fitting Models to Dat Outline Introduction General Principles: Unbiasedness and Standard Error Comparing Any Two Estimators The Method of Moments I In Chapter 6 we saw the nonparametric approach to estimation: Sample characteristics are used as estimators of the corresponding population characteristics. Michael Akritas Lecture 21 Chapter 7: Point Estimation: Fitting Models to Dat Outline Introduction General Principles: Unbiasedness and Standard Error Comparing Any Two Estimators The Method of Moments I In Chapter 6 we saw the nonparametric approach to estimation: Sample characteristics are used as estimators of the corresponding population characteristics. I The parametric approach estimates population parameters indirectly: Michael Akritas Lecture 21 Chapter 7: Point Estimation: Fitting Models to Dat Outline Introduction General Principles: Unbiasedness and Standard Error Comparing Any Two Estimators The Method of Moments I In Chapter 6 we saw the nonparametric approach to estimation: Sample characteristics are used as estimators of the corresponding population characteristics. I The parametric approach estimates population parameters indirectly: (a) Assume the population distribution belongs in a given parametric family, or model, of distributions; Michael Akritas Lecture 21 Chapter 7: Point Estimation: Fitting Models to Dat Outline Introduction General Principles: Unbiasedness and Standard Error Comparing Any Two Estimators The Method of Moments I In Chapter 6 we saw the nonparametric approach to estimation: Sample characteristics are used as estimators of the corresponding population characteristics. I The parametric approach estimates population parameters indirectly: (a) Assume the population distribution belongs in a given parametric family, or model, of distributions; (b) estimate the model parameters; Michael Akritas Lecture 21 Chapter 7: Point Estimation: Fitting Models to Dat Outline Introduction General Principles: Unbiasedness and Standard Error Comparing Any Two Estimators...
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b.lect21 - Outline Introduction General Principles:...

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