Obtain maximum likelihood estimates

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SUMMARY

The discussion centers on obtaining maximum likelihood estimates (MLE) for a Poisson distribution with parameter λ, specifically for modeling the arrival rate of calls per hour. Participants emphasize the importance of understanding the MLE procedure for various distributions, particularly in the context of random samples X1, X2,..., Xn. A reference to Wolfram MathWorld's entry on MLE is provided as a valuable resource for further exploration of this statistical method.

PREREQUISITES
  • Understanding of Poisson distribution and its properties
  • Familiarity with maximum likelihood estimation (MLE) concepts
  • Basic statistical sampling techniques
  • Knowledge of statistical software for calculations (e.g., R or Python)
NEXT STEPS
  • Study the derivation of maximum likelihood estimates for Poisson distribution
  • Learn how to implement MLE in R using the 'fitdistr' function
  • Explore the application of MLE in real-world scenarios, such as call center analytics
  • Review additional statistical distributions and their MLE procedures
USEFUL FOR

Statisticians, data analysts, and anyone involved in modeling arrival rates or similar stochastic processes will benefit from this discussion.

TomJerry
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Problem :
It is assumed that the arrival of the number of calls X per hour follow a poisson distribution with parameter \lambda, A random sample X1 = x1 , X2 = x2,...xn is taken .Obtain the maximum likelihood estimates of the average arrival rate.

Please can you provide me with some reference material so that i can solve such questions on my own .
 
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TomJerry said:
Problem :
It is assumed that the arrival of the number of calls X per hour follow a poisson distribution with parameter \lambda, A random sample X1 = x1 , X2 = x2,...xn is taken .Obtain the maximum likelihood estimates of the average arrival rate.

Please can you provide me with some reference material so that i can solve such questions on my own .

Wikipedia's entry on MLE?
 

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