Stats - mle poisson distribution -- quick question

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SUMMARY

The maximum likelihood estimator (MLE) for a Poisson distribution is defined as the sample mean, represented mathematically as \(\hat{\lambda} = \bar{x}\). In the context of the generalized ratio test for multinomial distributions, the estimation of \(\lambda\) can be expressed as \(\sum \frac{(number \times frequency)}{n}\). Both methods yield equivalent results as they fundamentally represent the same calculation of the average occurrence of events in the dataset.

PREREQUISITES
  • Understanding of maximum likelihood estimation (MLE)
  • Familiarity with Poisson distribution properties
  • Knowledge of multinomial distributions
  • Basic statistical concepts such as frequency and averages
NEXT STEPS
  • Study the derivation of MLE for Poisson distributions
  • Explore the application of generalized ratio tests in statistical analysis
  • Learn about the relationship between Poisson and multinomial distributions
  • Investigate practical examples of estimating parameters in statistical models
USEFUL FOR

Statisticians, data analysts, and students studying statistical inference, particularly those focusing on Poisson and multinomial distributions.

binbagsss
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This is probably a stupid question , but,

It's easy enough to show that the mle of a poission distribution is ## \bar{x}##: ## \hat{ \lambda}= \bar{x} ##

But,I'm then looking at the generalized ratio test section of my book, multinomial, it esitmates ## \lambda ## for some data by ## \sum \frac{( number X frequency that number occurred)}{ n} ##

how are these 2 equivalent?

Thanks in advance.
 
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binbagsss said:
This is probably a stupid question , but,

It's easy enough to show that the mle of a poission distribution is ## \bar{x}##: ## \hat{ \lambda}= \bar{x} ##

But,I'm then looking at the generalized ratio test section of my book, multinomial, it esitmates ## \lambda ## for some data by ## \sum \frac{( number X frequency that number occurred)}{ n} ##

how are these 2 equivalent?

Thanks in advance.
It's the same thing. Suppose you have a sample 9, 5, 7, 8, 9, 9. You can get the sum by just adding them, or you can rewrite it as 1 x 5 + 1x 7 + 1 x 8 + 3 x 9.
 

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