Characteristic function

In summary, the conversation discusses proving the continuity of the characteristic function x_A in a metric space (X,d). The key to the proof is finding an element V in the set of neighbourhoods V_d(x) that is a subset of both A and X\A. This can be done by assuming x_A is continuous and using the preimage of a closed subset of R under x_A. However, it is unclear how to find such an element V and further discussion is needed to determine a solution.
  • #1
Carl140
49
0

Homework Statement



Let (X,d) be a metric space, A subset of X, x_A: X->R the characteristic
function of A. (R is the set of all real numbers)

Let V_d(x) denote the set of neighbourhoods of x with respect the metric d.
Prove that x_A is continuous in x (x in X) if and only if there
exists an element V in V_d(x) such that V is a subset of A and V is a
subset of X\A.




The Attempt at a Solution



OK, so assume x_A is continuous then for each closed subset of R
the preimage of this closed subset under x_A must be closed.
Take V= {0} then V is closed so (x_A)^(-1) = { y in X: x_A(y) = 0} = X\A.

But I don't see how this helps..I don't know hwo to find such V in V_d(x).
 
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  • #2
My work:
So assume it is continuous at a point c.
Then put e=1/2 then there exists delta > 0 such that if d(x,c)< delta
then |x_A(x) - x_A(c)|< 1/2.
So we have 2 cases: x and c in A or x,c in X\A.
Then I don't know what to take as V..
 
  • #3
Am I reading this right? V is a subset of both A and X\A? This is only possible if V is empty, and the empty set is certainly not a neighborhood of anything.
 

1. What is a characteristic function?

A characteristic function is a mathematical function that describes the probability distribution of a random variable. It is used to summarize the properties of a distribution and can be used to calculate various statistics, such as mean, variance, and skewness.

2. How is a characteristic function related to a probability density function?

A characteristic function is the Fourier transform of the probability density function. This means that the characteristic function contains all of the information about the distribution, including its shape, location, and spread.

3. What is the importance of characteristic functions in statistics?

Characteristic functions are important in statistics because they provide a way to describe and analyze distributions. They can also be used to find the distribution of a sum of random variables and to test for independence of variables.

4. How is the characteristic function used in hypothesis testing?

In hypothesis testing, the characteristic function is used to calculate the likelihood ratio test statistic. This statistic is compared to a critical value to determine whether to reject or fail to reject the null hypothesis.

5. Can characteristic functions be used for any type of distribution?

Yes, characteristic functions can be used for any type of distribution, including discrete and continuous distributions. However, for some distributions, the characteristic function may be more difficult to calculate or interpret.

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