OLS versus MLE Example
Download
1 / 3

OLS versus MLE Example - PowerPoint PPT Presentation


  • 94 Views
  • Uploaded on

OLS versus MLE Example. Here is the data:. OLS versus MLE Example. Here is the SAS code:. OLS fitting using PROC GLM:. proc glm data=data; model y = x; run ;. Call the procedure for data set “data” Regression model: y = b 0 + b 1 x. MLE fitting using PROC GENMOD:.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' OLS versus MLE Example' - janus


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

OLS versus MLE Example

Here is the data:


OLS versus MLE Example

Here is the SAS code:

OLS fitting using PROC GLM:

procglm data=data;

model y = x;

run;

Call the procedure for data set “data”

Regression model: y = b0 + b1x

MLE fitting using PROC GENMOD:

Call the procedure for data set “data”

Model: y = b0 + b1x

Assume normally distributed errors

Use an identity link (the link function

describes the relationship between y

and the linear portion of the model)

procgenmod data=data;

model y = x / dist=normal link=identity;

run;


OLS versus MLE Example

Output from PROC GLM

Output from PROC GENMOD

Source DF SS Mean Sq

Model 1 81.2051 81.2051

Error 8 1.0533 0.1316

Corrected Total 9 82.2584

R-Square Root MSE

0.987195 0.362857

Parameter Estimate SE

Intercept 0.297333 0.2478

X 0.992121 0.0399

Criterion DF Value Value/DF

Deviance 8 1.0533 0.1317

Scaled Deviance 8 10.0000 1.2500

Log Likelihood -2.9362

Parameter DF Estimate SE

Intercept 1 0.2973 0.2217

X 1 0.9921 0.0357

Scale 1 0.3245 0.0726

MLE s2 = SS/n = 0.10533

Scale = f = (s2)2

Note that SE estimates differ; MLE variance estimates are biased at low sample sizes

Ln(L) = -n/2*ln(2pes2)

= -5ln(17.07947*0.10533) = -2.9361

K = 3 (intercept, x, s)

K = 3 (intercept, x, scale)


ad