Problems of regression smoothing and curve fitting are addressed via predictive inference in a flexible class of mixture models. Multidimensional density estimation using Dirichlet mixture models provides the theoretical basis for semi-parametric regression methods in which fitted regression functions may be deduced as means of conditional predictive distributions. These Bayesian regression fun...

Statistical methodology is presented for the regression analysis of multiple events in the presence of random eeects and measurement error. Omitted covariates are modeled as random eeects. Our approach to parameter estimation and signiicance testing is to start with a naive model of semi-parametric Poisson process regression, and then to adjust for random eeects and any possible covariate measu...

We present a simple direct approach for solving the ICA problem, using density estimation and maximum likelihood. Given a candidate orthogonal frame, we model each of the coordinates using a semi-parametric density estimate based on cubic splines. Since our estimates have two continuous derivatives , we can easily run a second order search for the frame parameters. Our method performs very favo...

Often researchers find parametric models restrictive and sensitive to deviations from the parametric specifications; semi-nonparametric models are more flexible and robust, but lead to other complications such as introducing infinite dimensional parameter spaces that may not be compact. The method of sieves provides one way to tackle such complexities by optimizing an empirical criterion functi...

Financial time series exhibit time-varying volatilities and non-Gaussian distributions. There has been considerable research on the GARCH models for dealing with these issues related to financial data. Since in practice the true error distribution is unknown, various quasi maximum likelihood methods based on different assumptions on the error distribution have been studied in the literature. Ho...

The aim of this contribution is to provide an adaptive estimation of the long-memory parameter in the classical semi-parametric framework for Gaussian stationary processes using a wavelet method. In particular, the choice of a data-driven optimal band of scales is introduced and developed. Moreover, a central limit theorem for the estimator of the long-memory parameter reaching the minimax rate...

We present a theory of point and interval estimation for nonlinear functionals in parametric, semi-, and non-parametric models based on higher order in‡uence functions (Robins [18], Sec. 9; Li et al. [10], Tchetgen et al, [23], Robins et al, [20]). Higher order in‡uence functions are higher order U-statistics. Our theory extends the …rst order semiparametric theory of Bickel et al. [3] and van ...

Nonparametric resampling methods such as Direct Sampling are powerful tools to simulate new datasets preserving important data features spatial patterns from observed while using only minimal assumptions. However, cannot generate extreme events beyond the range of values. We here propose value theory for stochastic processes extrapolate towards yet unobserved high quantiles. Original first enri...

We present a semi-parametric approach to photographic image synthesis from semantic layouts. The approach combines the complementary strengths of parametric and nonparametric techniques. The nonparametric component is a memory bank of image segments constructed from a training set of images. Given a novel semantic layout at test time, the memory bank is used to retrieve photographic references ...