Improved critical eigenfunction restriction estimates on Riemannian surfaces with nonpositive curvature

We show that one can obtain improved $L^4$ geodesic restriction estimates for eigenfunctions on compact Riemannian surfaces with nonpositive curvature. We achieve this by adapting Sogge's strategy in proving improved critical $L^p$ estimates. We first combine the improved $L^2$ restriction estimate of Blair and Sogge and the classical improved $L^\infty$ estimate of B\'erard to obtain an improved weak-type $L^4$ restriction estimate. We then upgrade this weak estimate to a strong one by using the improved Lorentz space estimate of Bak and Seeger. This estimate improves the $L^4$ restriction estimate of Burq, G\'erard and Tzvetkov and Hu by a power of $(\log\log\lambda)^{-1}$. Moreover, in the case of compact hyperbolic surfaces, we obtain further improvements in terms of $(\log\lambda)^{-1}$ by applying the ideas from recent works of Chen, Sogge and Blair, Sogge. We are able to compute various constants that appeared in the work of Chen and Sogge explicitly, by proving detailed oscillatory integral estimates and lifting calculations to the universal cover $\mathbb H^2$.Comment: 29 pages, 4 figures, minor correction

Derivative Formula and Applications for Degenerate Diffusion Semigroups

By using the Malliavin calculus and solving a control problem, Bismut type derivative formulae are established for a class of degenerate diffusion semigroups with non-linear drifts. As applications, explicit gradient estimates and Harnack inequalities are derived.Comment: 18 page

A test for model specification of diffusion processes

We propose a test for model specification of a parametric diffusion process based on a kernel estimation of the transitional density of the process. The empirical likelihood is used to formulate a statistic, for each kernel smoothing bandwidth, which is effectively a Studentized $L_2$-distance between the kernel transitional density estimator and the parametric transitional density implied by the parametric process. To reduce the sensitivity of the test on smoothing bandwidth choice, the final test statistic is constructed by combining the empirical likelihood statistics over a set of smoothing bandwidths. To better capture the finite sample distribution of the test statistic and data dependence, the critical value of the test is obtained by a parametric bootstrap procedure. Properties of the test are evaluated asymptotically and numerically by simulation and by a real data example.Comment: Published in at http://dx.doi.org/10.1214/009053607000000659 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org