Download presentation

Presentation is loading. Please wait.

Published byKaylee Joyce Modified over 4 years ago

1
Agenda of Week XI Review of Week X Factor analysis Illustration Method of maximum likelihood Principal component analysis Usages, basic model Objective, estimation

2
Review of Week X Factor analysis Illustration Method of maximum likelihood

3
Method of Maximum Likelihood Properties of estimators Biasedness Efficiency: Minimum variance Consistency: increasing n population Estimation of mean and variance Hogg and Craig book

4
Method of Maximum Likelihood Likelihood ratio function After taking logarithm to L in order to change multiplication into summation, differentiate log L with respect to theta. Find theta satisfying the above differential equations.

5
Method of Maximum Likelihood Example of factor analysis Factor loading matrix estimation Properties of MLS estimators Asymptotically unbiased Efficient Consistent

6
Principal Component Analysis Usages Academic performance Capability of baseball team Wealth of a country Data structure p variables n observations

7
PCA Basic model Principal component vector Common factor vector Characteristics

8
PCA Objective

9
PCA Proof.

10
PCA 1. Comparison between V(C i ) Trance and determinant 2. Coefficient of determination of V(C i )

11
PCA PCA based on correlation matrix 1. Standardization of variables 2. Calculation of eigen values and eigen vectors for correlation matrix

Similar presentations

Presentation is loading. Please wait....

OK

Stat 223 Introduction to the Theory of Statistics

Stat 223 Introduction to the Theory of Statistics

© 2018 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google