What Range of Exponents Keeps Expected Values Finite in Uniform Distributions?

In summary, the random variable X has a uniform density on the interval [0,2] with a probability density function of p(x)=1/2 for x in the interval [0,2] and p(x)=0 otherwise. The range of a for which E[X^a] is finite is all values of a except -1. This is because when a=-1, the integral becomes ln(x) evaluated from 0 to 2, which is infinite. This means that the expected value of X^a is finite for all values of a except -1.
  • #1
gjones89
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Homework Statement



The random variable X has uniform density on the interval [0,2], so that p(x)=1/2 for x in the interval [0,2] and p(x)=0 otherwise. Give the range of a (between minus/plus infinity) such that E[X^a] < infinity.

Homework Equations



The Attempt at a Solution



I integrated (1/2)x^a on the interval 0 to 2. This gave me an answer of (2^a)/(a+1). The only way I can see this is infinite is if a=-1. Therefore is it correct to say that E[X^a] is finite for all values of a except -1?
 
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  • #2


Your solution is correct. The range of a for which E[X^a] is finite is all values of a except -1. This is because when a=-1, the integral becomes ln(x) evaluated from 0 to 2, which is infinite. This means that the expected value of X^a is finite for all values of a except -1.
 

FAQ: What Range of Exponents Keeps Expected Values Finite in Uniform Distributions?

What is a finite moment in statistics?

A finite moment in statistics refers to a statistical measure that describes the center or spread of a set of data. It is a numerical value that summarizes the characteristics of a data set, such as mean, variance, and standard deviation.

How is a finite moment calculated?

A finite moment is calculated by taking the sum of a set of values raised to a specific power, then dividing by the total number of values in the set. For example, the first moment is calculated by dividing the sum of all values by the total number of values, while the second moment is calculated by dividing the sum of squared values by the total number of values.

What is the difference between a finite moment and an infinite moment?

The main difference between a finite moment and an infinite moment is that a finite moment is a calculated value based on a set of data with a finite number of values, while an infinite moment is a theoretical concept that is calculated based on a continuous distribution of values. In other words, a finite moment can be calculated for any set of data, while an infinite moment is only applicable to theoretical distributions.

Why are finite moments important in statistics?

Finite moments are important in statistics because they provide a concise summary of a data set and allow for easy comparison between different data sets. They also serve as the basis for many statistical tests and models, such as the Central Limit Theorem and the moment generating function.

What are some real-world applications of finite moments in statistics?

Finite moments have many real-world applications in statistics, such as in finance for analyzing stock market trends, in physics for measuring particle motion, and in engineering for analyzing signal processing data. They are also commonly used in social sciences for studying human behavior and in biology for analyzing genetic data.

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