Minimax bounds for estimation of normal mixtures

This paper deals with minimax rates of convergence for estimation of density functions on the real line. The densities are assumed to be location mixtures of normals, a global regularity requirement that creates subtle difficulties for the application of standard minimax lower bound methods. Using novel Fourier and Hermite polynomial techniques, we determine the minimax optimal rate - slightly larger than the parametric rate - under squared error loss. For Hellinger loss, we provide a minimax lower bound using ideas modified from the squared error loss case.Comment: Published in at http://dx.doi.org/10.3150/13-BEJ542 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Critical curvature of large-NN nonlinear O(N)O(N) sigma model on S2S^2

We study the nonlinear $O(N)$ sigma model on $S^2$ with the gravitational coupling term, by evaluating the effective potential in the large-$N$ limit. It is shown that there is a critical curvature $R_c$ of $S^2$ for any positive gravitational coupling constant $\xi$, and the dynamical mass generation takes place only when $R<R_c$. The critical curvature is analytically found as a function of $\xi$ $(>0)$, which leads us to define a function looking like a natural generalization of Euler-Mascheroni constant.Comment: 7 pages, LaTe