Mixture of Regression Models with Single-Index

In this article, we propose a class of semiparametric mixture regression models with single-index. We argue that many recently proposed semiparametric/nonparametric mixture regression models can be considered special cases of the proposed model. However, unlike existing semiparametric mixture regression models, the new pro- posed model can easily incorporate multivariate predictors into the nonparametric components. Backfitting estimates and the corresponding algorithms have been proposed for to achieve the optimal convergence rate for both the parameters and the nonparametric functions. We show that nonparametric functions can be esti- mated with the same asymptotic accuracy as if the parameters were known and the index parameters can be estimated with the traditional parametric root n convergence rate. Simulation studies and an application of NBA data have been conducted to demonstrate the finite sample performance of the proposed models.Comment: 28 pages, 2 figure

Nonparametric and Varying Coefficient Modal Regression

In this article, we propose a new nonparametric data analysis tool, which we call nonparametric modal regression, to investigate the relationship among interested variables based on estimating the mode of the conditional density of a response variable Y given predictors X. The nonparametric modal regression is distinguished from the conventional nonparametric regression in that, instead of the conditional average or median, it uses the "most likely" conditional values to measures the center. Better prediction performance and robustness are two important characteristics of nonparametric modal regression compared to traditional nonparametric mean regression and nonparametric median regression. We propose to use local polynomial regression to estimate the nonparametric modal regression. The asymptotic properties of the resulting estimator are investigated. To broaden the applicability of the nonparametric modal regression to high dimensional data or functional/longitudinal data, we further develop a nonparametric varying coefficient modal regression. A Monte Carlo simulation study and an analysis of health care expenditure data demonstrate some superior performance of the proposed nonparametric modal regression model to the traditional nonparametric mean regression and nonparametric median regression in terms of the prediction performance.Comment: 33 page

Rankin-Cohen brackets and formal quantization

In this paper, we use the theory of deformation quantization to understand Connes' and Moscovici's results \cite{cm:deformation}. We use Fedosov's method of deformation quantization of symplectic manifolds to reconstruct Zagier's deformation \cite{z:deformation} of modular forms, and relate this deformation to the Weyl-Moyal product. We also show that the projective structure introduced by Connes and Moscovici is equivalent to the existence of certain geometric data in the case of foliation groupoids. Using the methods developed by the second author \cite{t1:def-gpd}, we reconstruct a universal deformation formula of the Hopf algebra \calh_1 associated to codimension one foliations. In the end, we prove that the first Rankin-Cohen bracket $RC_1$ defines a noncommutative Poisson structure for an arbitrary \calh_1 action.Comment: 21 pages, minor changes and typos correcte