Compute Expectation for $X_{1}^\frac{1}{2}$ with Family $f(x,\theta)$

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In summary, expectation for $X_{1}^\frac{1}{2}$ is defined as the average value of the square root of a given variable in a dataset. It is calculated by multiplying each possible value of the variable by its probability of occurrence and summing up all these values. To compute expectation for $X_{1}^\frac{1}{2}$, the formula E($X_{1}^\frac{1}{2}$) = $\int_{-\infty}^{\infty} x^\frac{1}{2} f(x,\theta)dx$ is used, where $f(x,\theta)$ represents the probability density function of the variable $X_{1}$. This family of probability density
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Fermat1
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consider a density family $f(x,\theta)=\frac{exp(-{\sqrt{x}}/{\theta})}{2{\theta}^2}$.

Let $X_{1}$ have the density above. Compute $E(X_{1}^\frac{1}{2})$.

Integration by parts doesn't work since the derivative of ${\sqrt{x}}$ never vanishes, so how do I compute the expectation?
 
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Have you got this?

\(\displaystyle E(X_1^{1/2})=\int_0^{\infty} \sqrt{x}\exp(-\sqrt{x}/\theta)/2\theta^2dx=\int_0^{\infty} t^2\exp(-t/\theta)/\theta^2dt\)
 
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Thanks, I'm a bit out of practice with integrals
 

FAQ: Compute Expectation for $X_{1}^\frac{1}{2}$ with Family $f(x,\theta)$

What is the definition of expectation for $X_{1}^\frac{1}{2}$?

Expectation is a concept in statistics that represents the average value of a random variable, which is calculated by multiplying each possible value of the variable by its probability of occurrence and summing up all these values. In the case of $X_{1}^\frac{1}{2}$, it refers to the average or expected value of the square root of the first variable in a given dataset.

How is expectation for $X_{1}^\frac{1}{2}$ calculated?

To compute expectation for $X_{1}^\frac{1}{2}$, you need to apply the formula E($X_{1}^\frac{1}{2}$) = $\int_{-\infty}^{\infty} x^\frac{1}{2} f(x,\theta)dx$, where f(x,$\theta$) represents the probability density function of the variable $X_{1}$. This integral is solved by integrating over the entire range of possible values for $X_{1}$ and taking into account the probability of each value occurring.

What is the role of the family $f(x,\theta)$ in computing expectation for $X_{1}^\frac{1}{2}$?

The family $f(x,\theta)$ represents the set of probability density functions that can be used to model the variable $X_{1}$. This family is characterized by a parameter $\theta$, which can take on different values and determine the shape and characteristics of the probability distribution. By specifying the family $f(x,\theta)$, we can calculate the expectation for $X_{1}^\frac{1}{2}$ using the appropriate probability density function.

Can expectation for $X_{1}^\frac{1}{2}$ be calculated for any family $f(x,\theta)$?

Yes, expectation for $X_{1}^\frac{1}{2}$ can be computed for any family $f(x,\theta)$ as long as the variable $X_{1}$ follows a continuous probability distribution. This includes commonly used distributions such as the normal, uniform, and exponential distributions, among others. However, for discrete distributions, the concept of expectation may be slightly different and would require a different formula for calculation.

How is the expectation for $X_{1}^\frac{1}{2}$ related to the concept of variance?

The expectation for $X_{1}^\frac{1}{2}$ and the variance are two important measures of a random variable's central tendency and variability, respectively. In fact, the variance of a variable $X$ is defined as Var($X$) = E($X^{2}$) - [E($X$)]$^{2}$. Therefore, by computing the expectation for $X_{1}^\frac{1}{2}$, we can also indirectly calculate the variance of the variable $X_{1}$, which provides information about its spread or dispersion around the expected value.

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