It is well known that i.i.d. (independent and identically distributed) normal random variables are transformed into i.i.d. normal random variables by any orthogonal transformation. Less well known are nonlinear transformations with the above-mentioned property. In this work we present nonlinear tran
On certain characterization of normal distribution
โ Scribed by Krzysztof Oleszkiewicz
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 160 KB
- Volume
- 33
- Category
- Article
- ISSN
- 0167-7152
No coin nor oath required. For personal study only.
โฆ Synopsis
A conjecture of Bobkov and Houdr6 (1995), recently proved by , stated that if X and Y are symmetric i.i.d, real random variables such that P(I(X + Y)/x/~l > t) <~ P(IXI > t) for any t > 0, then X has normal distribution. In this note, we give some generalization of their result with a short and simple proof which can be useful in some other cases.
๐ SIMILAR VOLUMES
Suppose X,, X,, ..., X, are independent and identically distributed random variables with absolutely continuous distribution function F. It is known that if F is standard normal distribution then (i) 2 X : is a chi-square with n degrees of freedom and (ii) nX2 is a chi-square with 1 degrees of freed