Inferential statistics-maximum likelihood function

In summary, the maximum likelihood estimates of the parameters for this problem are p11=0.9, p12=0.06, p21=0.03, and p22=0.01.
  • #1
kimkibun
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Homework Statement



suppose that n cylindrical shafts are selected at random from the production of the machine and their diameters and lengths are measured. it is found that N11 have both measurements within the tolerance limits, N12 have satisfactory lengths but unsatisfactory diameters, N21 have satisfactory diameters but unsatisfactory lengths, and N22 are unsatisfactory to both measurements. ƩNij=n. each shaft may be regarded as a drawing from a multinomial population with density

p11x11p12x12p21x21(1-p11-p12-p21)x22; for xij=0,1; Ʃpij=1​

having three parameters. what are the maximum likelihood estimates of the parameter if N11=90, N12=6, N21=3, and N22=1?

Homework Equations





The Attempt at a Solution


my solution is attached. i just want to know if my answers are correct.
 

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  • #2


Hello, thank you for sharing your solution. Your answers seem to be correct based on the information provided in the forum post. However, it would be helpful to see your calculations and steps in finding the maximum likelihood estimates so that we can provide feedback and ensure that your approach is correct. Additionally, it would be beneficial to include any assumptions or limitations that you made in your solution. Overall, your solution appears to be reasonable and well-supported.
 

Related to Inferential statistics-maximum likelihood function

1. What is the maximum likelihood function in inferential statistics?

The maximum likelihood function is a statistical method used to estimate the parameters of a probability distribution by finding the set of values that maximizes the likelihood of obtaining the observed data. It is commonly used in hypothesis testing and model selection.

2. How is the maximum likelihood function calculated?

The maximum likelihood function is calculated by taking the product of the probability density function (PDF) of the distribution for each data point and then maximizing this function with respect to the parameters of the distribution.

3. What is the relationship between the maximum likelihood function and the likelihood ratio test?

The likelihood ratio test is a statistical test used to compare two nested models, where one model is a simplified version of the other. The test statistic is the ratio of the maximum likelihood values for each model. If this ratio is large enough, it indicates that the more complex model fits the data significantly better than the simpler model.

4. What are some assumptions made when using the maximum likelihood function?

Some common assumptions made when using the maximum likelihood function include: the data follows a specific probability distribution, the data points are independent of each other, and the parameters are continuous and not categorical. Additionally, the maximum likelihood function assumes that the sample size is sufficiently large.

5. What are the limitations of using the maximum likelihood function in inferential statistics?

One limitation of the maximum likelihood function is that it assumes the data follows a specific probability distribution, which may not always be the case in real-world scenarios. Additionally, the method is highly sensitive to outliers and can produce biased estimates if the data is not normally distributed. It also does not account for sample size when comparing models, so a larger sample size may lead to a significant result even if the difference between models is small.

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