turn on suggestions

Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

Find a Community

- Home
- /
- Analytics
- /
- Forecasting
- /
- How to maximise a likelihood function in the form ...

Topic Options

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Highlight
- Email to a Friend
- Report Inappropriate Content

09-11-2014 06:10 AM

Hi There,

I hope you can help me. I have a problem which can best be explained by the following:

I randomly select 50 to 60 mice (from a population of 1000) and race them across the room. Each mouse is marked by a number between 1 to 1000. I record the race winner's ID and its weight. I repeat this race for each minute, for 100 000 minutes; and the results are compiled to form my data.

I would like now to assess the explanatory power of weight to the probability of winning and towards this end I would like to estimate a coefficient (Beta) that will maximise a likelihood function in the form of:

L = sum_operator(from t=1 to T) log[ exp(V*) / sum_operator(from j=1 to J)[exp(V)] ],

where V represents a linear regression model (V = Beta*Weight), V* represents the winner, t represent minutes and j represents the number of mice for the particular race.

Is there a set of SAS code that can do this? Other then brute-force, I'm hoping there's an elegant code to solve the above problem.

Thanks

Joe