Download presentation

Presentation is loading. Please wait.

Published byKaylee Joyce Modified over 2 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

© 2016 SlidePlayer.com Inc.

All rights reserved.

Ads by Google