Is this a characteristic function?

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If φ is a characteristic function, then e^{φ-1} is also a characteristic function because it is continuous at 0 and can be expressed as a limit of a product of characteristic functions. The discussion confirms that weighted sums of characteristic functions maintain the characteristic function property. Additionally, the function φ(t) = e^{-t^2}/(1 + sin^2(t)) is not a characteristic function of a discrete distribution, and its classification remains complex due to varying definitions of characteristic functions. The consensus is that the first part of the question is correct, affirming the properties of characteristic functions.
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1. If \phi is a characteristic function, than is e^{\phi-1} also a characteristic function?

I know some general rules like that a product or weighted sum of characteristic functions are also characteristic functions, also a pointwise limit of characteristic functions is one if it's continuous at 0.

So I think the answet is yes, because e^{\phi-1} is continuous at 0 and it's a limit of the product \phi_n(t)^n
where
\phi_n(t)=1+\frac{\phi(t)-1}{n},
and \phi_n is obviously a characteristic function.

Is this correct?

2. Is \phi(t)=\frac{e^{-t^2}}{1+\sin^2(t)} a characteristic function?
Here I can only prove, that it's not a characteristic function from a discrete distribution. I tried integrating it to get the inverse Fourier transform, but it's too difficult.
 
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I know of several different definitions of "characteristic function". What is your definition?
 
HallsofIvy said:
I know of several different definitions of "characteristic function". What is your definition?
In probability theory, the characteristic function is the Fourier transform of the density function. More generally it is ∫eitxdF(x), where F(x) is the distribution function.
 
The answer for 1. is yes. You can use the weighted sum, where the weights are > 0 and the sum = 1.
eφ-1 = 1/e{1 + φ + φ2/2 + ...φn/n! ...}.
Each φn is a characteristic function (note 1 is the ch. f. of unit dist. at 0) and 1/n! sums to e.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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