Search Results for author:
Found 6 papers, 2 papers with code
Multivariate Probabilistic Regression with Natural Gradient Boosting
Our method is robust, works out-of-the-box without extensive tuning, is modular with respect to the assumed target distribution, and performs competitively in comparison to existing approaches.
The Debiased Spatial Whittle Likelihood
We provide a computationally and statistically efficient method for estimating the parameters of a stochastic covariance model observed on a regular spatial grid in any number of dimensions.
Smoothing and Interpolating Noisy GPS Data with Smoothing Splines
A comprehensive methodology is provided for smoothing noisy, irregularly sampled data with non-Gaussian noise using smoothing splines.
The De-Biased Whittle Likelihood
We demonstrate the superior performance of the method in simulation studies and in application to a large-scale oceanographic dataset, where in both cases the de-biased approach reduces bias by up to two orders of magnitude, achieving estimates that are close to exact maximum likelihood, at a fraction of the computational cost.
Exact Simulation of Noncircular or Improper Complex-Valued Stationary Gaussian Processes using Circulant Embedding
This paper provides an algorithm for simulating improper (or noncircular) complex-valued stationary Gaussian processes.
Frequency-Domain Stochastic Modeling of Stationary Bivariate or Complex-Valued Signals
In this paper we provide a joint framework for all three representations in the context of frequency-domain stochastic modeling.
