Method of Moments: Solving For Theta

In summary, the method of moments is a statistical technique used to estimate the parameters of a probability distribution based on a set of sample data. It works by equating the sample moments to their theoretical counterparts and can provide accurate estimates when certain assumptions are met. These assumptions include representative sample data, accurate estimation of moments, and a continuous distribution with a finite number of parameters. However, the method of moments may not work well if the underlying distribution is not well-represented by the sample data, if the sample size is too small, or if the distribution is not known. It may also have limitations for distributions with heavier tails or extreme values.
  • #1
semidevil
157
2
maybe I'm missing some stupid algebraic mistake..but given theta/theta + 1 = y. how does this imply that the method of moment is theta = y/ 1-y??
 
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  • #2
I am not sure what you mean by the method of moment. However, given y=x/(x+1), then x=y/(1-y) is just elementary algebra.

Style note for future - use parentheses!
 
  • #3


The method of moments is a statistical technique used to estimate the parameters of a population based on a set of sample data. In this case, we are trying to solve for the parameter theta given the equation theta/theta + 1 = y.

To solve for theta, we can use the method of moments by equating the theoretical moments (based on the population) to the sample moments (based on the sample data). In this case, the first moment (mean) is theta and the second moment (variance) is theta + 1.

By setting these two moments equal to the sample moments, we can solve for theta. This is done by setting theta equal to the sample mean (y) and theta + 1 equal to the sample variance (y). This results in the equation theta = y/1-y.

Therefore, the method of moments implies that theta is equal to y/1-y. This is the estimated value of the parameter theta based on the sample data. It is possible that there may be some algebraic mistakes in your calculations, but the general approach of using the method of moments to solve for theta is correct.
 

Related to Method of Moments: Solving For Theta

1. What is the method of moments?

The method of moments is a statistical technique used to estimate the parameters of a probability distribution based on a set of sample data. It involves equating the theoretical moments of the distribution to the sample moments to find the values of the parameters.

2. How does the method of moments work?

The method of moments works by equating the sample moments, such as the mean and variance, to their theoretical counterparts. This creates a system of equations that can be solved to find the values of the parameters of the distribution.

3. What is the purpose of using the method of moments?

The purpose of using the method of moments is to estimate the parameters of a probability distribution when the underlying distribution is unknown or cannot be easily determined. It is a simple and intuitive method that can provide accurate estimates when certain assumptions are met.

4. What are the assumptions of the method of moments?

The method of moments relies on the assumptions that the sample data is representative of the underlying population, and that the moments of the distribution can be accurately estimated from the sample moments. It also assumes that the distribution is continuous and has a finite number of parameters.

5. What are the limitations of the method of moments?

The method of moments may not provide accurate estimates if the underlying distribution is not well-represented by the sample data, or if the sample size is too small. It also assumes that the distribution is known, which may not always be the case. Additionally, it may not work well for distributions with heavier tails or extreme values.

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