Probability Problem - Likelihood-Ratio Test

In summary: The rest of the solution involves computing the expected values of the logs and then using the observed values of the logs to compute the likelihood ratio.In summary, the conversation discusses a crossover experiment involving 100 women with dysmenorrhea and two different analgesics. The results show that 60 women reported greater relief with the new analgesic and 40 with the standard one. The conversation then introduces the concept of pi, the probability that the new analgesic is judged better, and the desire to estimate and test this value. The likelihood-ratio test is proposed as a method to compare the observed data to a null hypothesis, and a 95% confidence interval is suggested to be constructed using a sequence of grid points on pi
  • #1
Renyulb28
2
0

Homework Statement


A sample of 100 women suffer from dysmenorrhea. A new analgesic is
claimed to provide greater relief than a standard one. After using each
analgesic in a crossover experiment, 40 reported greater relief with the
standard analgesic and 60 reported greater relief with the new one.
Analyze these data.
pi denotes the probability that the new one is judged better. It is desired to estimate
pi and test H0: pi =0.5 against Ha: pi =/= 0.5.
Conduct a likelihood-ratio test and construct a likelihood-based
95% confidence interval. Interpret

Homework Equations



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The Attempt at a Solution


I know that in order to solve for the likelihood-ratio statistic and compare it to chi-square (3.84), I must solve for lambda

attempt 1: without log-likelihood

Pthyg.png


which gives me 0.003, which is much smaller than 3.84, and thus fail to reject, but this doesn't seem right, as the Wald test and Score test both rejected the null hypothesis.
Also, I have no idea where to start on the confidence interval calculation, and the professor just sent me this
Here is the detail. LRS < chisquare_0.05(1) => -1.96 < sqrt(LRS) < 1.96.
In R, generate a sequence of grid point on pi, and evaluate sqrt(LRS)
at each pi, and find the pi values at which sqrt(LRR) = -+1.96.
 
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  • #2
I suggest you use the formula given after the statement of the problem.

What you did looks like an attempt to compute the liklihood ratio before taking logs. If that was your aim, you make two errors. One error is that the ratio should be a ratio, not a difference. The second error is that you have one term representing an outcome where only 50 women said the new analgesic is better. The liklihood ratio test doesn't involve hypothesizing different data. It only involves hypothesizing a different explanation for the data that was actually observed.


The ratio of the liklihoods is

[tex] \frac{ \frac{ 100!}{60! 40!} (0.5)^{60}(0.5)^{40}}{\frac{100!}{60! 40!}(0.6)^{60}(0.4)^{40}} [/tex]

The factorials "cancel out" in the ratio so the expression becomes:

[tex] = \frac{(0.5)^{60}(0.5)^{40}}{ (0.6)^{60} (0.4)^{40} } [/tex]
[tex] = (\frac{0.5}{0.6})^{60} (\frac{0.5}{0.4})^{40} [/tex]

The formula you gave after the problem is based on taking logs of this ratio and multiplying by 2.

For example,

[tex] \log( (\frac{0.5}{0.6})^{60} (\frac{0.5}{0.4})^{40}) = \log((\frac{0.5}{0.6})^{60}) + \log((\frac{0.5}{0.4})^{40}) [/tex]
[tex] = 60 \log(\frac{0.5}{0.6}) + 40 \log(\frac{0.5}{0.4}) [/tex]
 

Related to Probability Problem - Likelihood-Ratio Test

What is a likelihood-ratio test?

A likelihood-ratio test is a statistical method used to compare the likelihood of two different statistical models for a given set of data. It is used to determine which model is a better fit for the data.

How is a likelihood-ratio test calculated?

A likelihood-ratio test is calculated by taking the ratio of the maximum likelihood estimates for the two models being compared. This ratio is then compared to a critical value from a chi-square distribution to determine if the difference in likelihood is statistically significant.

What is the null hypothesis in a likelihood-ratio test?

The null hypothesis in a likelihood-ratio test is that there is no significant difference between the two models being compared. In other words, the simpler model is just as good of a fit for the data as the more complex model.

What is the alternative hypothesis in a likelihood-ratio test?

The alternative hypothesis in a likelihood-ratio test is that there is a significant difference between the two models being compared. This means that the more complex model is a better fit for the data than the simpler model.

When should a likelihood-ratio test be used?

A likelihood-ratio test should be used when comparing two nested models, meaning one model is a simplified version of the other. It is commonly used in regression analysis and is useful for determining if adding additional variables to a model significantly improves its fit.

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