arXiv subjects
Publications and source records attributed to Elisabeth Werner.
The Error Function \begin{eqnarray} V(x) & \equiv & \sqrtπ e^{x^2} [1 - \hbox{erf}(x)] \\ & = & \int_0^\infty \frac{ e^{-u} }{\sqrt{x^2 + u}} du = 2 e^{x^2}\int_x^\infty e^{-t^2} dt \nonumber \end{eqnarray} arises in many contexts, from probability to mathematical physics. We give estimates for the Error Function from above and below which are optimal within a certain class of functions.
Motivated by the Blaschke-Santaló inequality, we define for a convex body K in ${\bf R}^n$ and for $t \in {\bf R}$ the Santaló-regions S(K,t) of K. We investigate properties of these sets and relate them to a concept of Affine Differential Geometry, the affine surface area of K.
Let $K$ be a convex body in ${\bf R}^n$ and $B$ be the Euclidean unit ball in ${\bf R}^n$. We show that $$\mbox{lim}_{t\rightarrow 0} \frac{|K| -|K_t|}{|B| - |B_t|}= \frac{as(K)}{as(B)},$$ where $as(K)$ respectively $as(B)$ is the affine surface area of $K$ respectively $B$ and $\{K_t\}_{t\geq 0}$, $\{B_t\}_{t\geq 0}$ are general families of convex bodies constructed from $K$, $B$ satifying certain conditions. As a corollary we get results obtained in [M-W], [Schm],[S-W] and[W].
Let $μ$ be a Gaussian measure (say, on ${\bf R}^n$) and let $K, L \subset {\bf R}^n$ be such that K is convex, $L$ is a "layer" (i.e. $L = \{x : a \leq < x,u > \leq b \}$ for some $a$, $b \in {\bf R}$ and $u \in {\bf R}^n$) and the centers of mass (with respect to $μ$) of $K$ and $L$ coincide. Then $μ(K \cap L) \geq μ(K) \cdot μ(L)$. This is motivated by the well-known "positive correlation conjecture" for symmetric sets and a related inequality of Sidak concerning confidence regions for means of multivariate normal distributions. The proof uses an apparently hitherto unknown estimate for the (standard) Gaussian cumulative distribution function: $Φ(x) > 1 - \frac{(8/π)^{1/2}}{3x + (x^2 +8)^{1/2}} e^{-x^2/2}$ (valid for $x > -1$).