Nonparametric Inference via Bootstrapping the Debiased Estimator

In this paper, we propose to construct confidence bands by bootstrapping the debiased kernel density estimator (for density estimation) and the debiased local polynomial regression estimator (for regression analysis). The idea of using a debiased estimator was recently employed by Calonico et al. (2018b) to construct a confidence interval of the density function (and regression function) at a given point by explicitly estimating stochastic variations. We extend their ideas of using the debiased estimator and further propose a bootstrap approach for constructing simultaneous confidence bands. This modified method has an advantage that we can easily choose the smoothing bandwidth from conventional bandwidth selectors and the confidence band will be asymptotically valid. We prove the validity of the bootstrap confidence band and generalize it to density level sets and inverse regression problems. Simulation studies confirm the validity of the proposed confidence bands/sets. We apply our approach to an Astronomy dataset to show its applicabilityComment: Accepted to the Electronic Journal of Statistics. 64 pages, 6 tables, 11 figure

Polyelectrolyte Adsorption on Charged Substrate

The behavior of a polyelectrolyte adsorbed on a charged substrate of high-dielectric constant is studied by both Monte-Carlo simulation and analytical methods. It is found that in a low enough ionic strength medium, the adsorption transition is first-order where the substrate surface charge still keeps repulsive. The monomer density at the adsorbed surface is identified as the order parameter. It follows a linear relation with substrate surface charge density because of the electrostatic boundary condition at the charged surface. During the transition, the adsorption layer thickness remains finite. A new scaling law for the layer thickness is derived and verified by simulation.Comment: Proceedings of the 3rd Symposium on Slow Dynamics in Complex Systems, 3-8 November 2003, Sendai, Japa

Unfolding first-principles band structures

A general method is presented to unfold band structures of first-principles super-cell calculations with proper spectral weight, allowing easier visualization of the electronic structure and the degree of broken translational symmetry. The resulting unfolded band structures contain additional rich information from the Kohn-Sham orbitals, and absorb the structure factor that makes them ideal for a direct comparison with angular resolved photoemission spectroscopy experiments. With negligible computational expense via the use of Wannier functions, this simple method has great practical value in the studies of a wide range of materials containing impurities, vacancies, lattice distortions, or spontaneous long-range orders.Comment: 4 pages, 3 figure

Climate Change and Crop Yield Distribution: Some New Evidence From Panel Data Models

This study examines the impact of climate on the yields of seven major crops in Taiwan based on pooled panel data for 15 prefectures over the 1977-1996 period. Unit-root tests and maximum likelihood methods involving a panel data model are explored to obtain reliable estimates. The results suggest that climate has different impacts on different crops and a gradual increase in crop yield variation is expected as global warming prevails. Policy measures to counteract yield variability should therefore be carefully evaluated to protect farmers from exposure to these long-lasting and increasingly climate-related risks.Yield response, Climate change, Panel data, Unit-root test