Smoothness (probability theory)

In probability theory and statistics, smoothness of a density function is a measure which determines how many times the density function can be differentiated, or equivalently the limiting behavior of distribution’s characteristic function. Formally, we call the distribution of a random variable X ordinary smooth of order β [1] if its characteristic function satisfies

d0|t|−β≤|φX(t)|≤d1|t|−βas t->∞

for some positive constants d0, d1, β. The examples of such distributions are gamma, exponential, uniform, etc.

The distribution is called supersmooth of order β [1] if its characteristic function satisfies

d0|t|β0exp⁡(−|t|β/γ)≤|φX(t)|≤d1|t|β1exp⁡(−|t|β/γ)as t->∞

for some positive constants d0, d1, β, γ and constants β0, β1. Such supersmooth distributions have derivatives of all orders. Examples: normal, Cauchy, mixture normal.

## References

1. ↑ 1.0 1.1 Fan, Jianqing (1991). "On the optimal rates of convergence for nonparametric deconvolution problems". The Annals of Statistics 19 (3): 1257–1272. doi:10.1214/aos/1176348248.

* Lighthill, M. J. (1962). Introduction to Fourier analysis and generalized functions. London: Cambridge University Press.

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