Stats: mle with two parameters

In summary: N1 + N4 + N5)/2n, where n = sum (Ni). x2(hat) is similar.In summary, the equations give the probability of a genotype, given the genotypes of the parents and the allele that is being passed on.
  • #1
bennyska
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Homework Statement


in a genetics situation, we have two variables, x1 and x2, such that both x1 and x2 >0, and x1+x2<1.
we have:
p1 = x12
p2 = x22
p3 = (1-x1-x2)2
p4 = 2x1x2
p5 = 2x1(1-x1-x2)
p6 = 2x2(1-x1-x2)

find the mles for x1 and x2.

Homework Equations



the answer (from the book): x1(hat) = (2N1 + N4 + N5)/2n, where n = sum (Ni). x2(hat) is similar.

The Attempt at a Solution


doing the usual mle stuff, i have sum(Ni*ln(pi)), take the derivative, set to zero, and solve for my parameter. when i do this work, i get an answer similar (but incorrect) to that of the book, with a little problem: in order to get n, i need all the Nis. when i take the derivative with respect to x1, there is one term (p2) that doesn't have x1 in it, so it drops out, and i lose that term (N2) that i think i need. i can't define x1 by x2 (i think, since x1 + x2 < 1 doesn't tell me too much). my teacher assigned this one to us, without having done the problem herself, and then she found herself unable to do it. we figure there's some little trick involved, but I'm not seeing it. also, i haven't really done mles with more than one variable, so maybe that's what I'm missing. any help would be greatly appreciated.
 
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  • #2
bennyska said:

Homework Statement


in a genetics situation, we have two variables, x1 and x2, such that both x1 and x2 >0, and x1+x2<1.
we have:
p1 = x12
p2 = x22
p3 = (1-x1-x2)2
p4 = 2x1x2
p5 = 2x1(1-x1-x2)
p6 = 2x2(1-x1-x2)

.

We have them, but what are they? Are the p's functions?
 
  • #3
sorry for my vagueness. the actual problem is something like this: a certain gene has 3 alleles, resulting in six possible genotypes. each p is the probability of being a certain genotype. each parent has an x1 or x2 probability of passing that allele to the child.
 

FAQ: Stats: mle with two parameters

1. What is MLE in statistics?

MLE stands for Maximum Likelihood Estimation. It is a method used in statistics to estimate the parameters of a probability distribution by maximizing the likelihood function. This helps to find the most likely values of the parameters that best fit the observed data.

2. What are the two parameters in MLE?

In MLE, there are two parameters that need to be estimated - the location parameter (μ) and the scale parameter (σ). The location parameter determines the center of the distribution, while the scale parameter determines the spread of the distribution.

3. How is MLE different from other methods of parameter estimation?

MLE is different from other methods of parameter estimation because it uses the likelihood function to estimate the parameters, rather than the probability density function or the cumulative distribution function. This makes it a more flexible and robust method of estimation.

4. What are some common applications of MLE with two parameters?

MLE with two parameters is commonly used in various fields such as finance, economics, biology, and engineering. It can be used to estimate parameters in regression models, survival analysis, and time series analysis, among others.

5. What are the advantages of using MLE with two parameters?

One of the main advantages of MLE with two parameters is that it provides unbiased estimates of the parameters. It also has good efficiency properties, meaning that it produces estimates with low variance. Additionally, MLE is a flexible method and can be applied to a wide range of distributions and data types.

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