How to maximise a likelihood function in the form of a Multinomial Logit Model
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NOTE: must specific solution in SAS
Hi There,
I hope someone can help me with this . 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
Nº del proyecto: #6441172
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Hi, I am Nikhil Gupta. I am having total 9 years of extensive experience into Analytics. I rarely bid on this website. However, I am planning to be active on this website. I worked on tools like Excel, SAS, R & SPSS. G Más
5 freelancers están ofertando un promedio de $32 por este trabajo
Hello, Thank you for your invitation, I am a Statistician and computer programmer involved in United Nations projects in Geneva. Please when you need this job to be done? Hear from you soon Best Fouad
Hi There, I am a statistician and SAS programmer. I have a base version of SAS 9.1.3. I am able to write the required function for you. If you want to see my work sample let me know. Thanks, Kazi
Hi I have taken a look on the your project details which you have stated and i will like to bid for the same. I have 8 + years of experience and i have track record of completing which project i take in stipula Más