36 citations · 226 across the 25 of their papers we have counts for
26 papers
Multivariate Poisson intensity estimation via low-rank tensor decomposition
Haotian Xu, Carlos Misael Madrid Padilla, Oscar Hernan Madrid Padilla +1
In this work, we propose new matrix- and tensor-based methodologies for estimating multivariate intensity functions of inhomogeneous point processes. By viewing multivariate intens…
Estimation and Inference for Change Points in Functional Regression Time Series
Shivam Kumar, Haotian Xu, Haeran Cho +1
In this paper, we study the estimation and inference of change points under a functional linear regression model with changes in the slope function. We present a novel Functional R…
Multivariate Density Estimation via Variance-Reduced Sketching
Yifan Peng, Yuehaw Khoo, Daren Wang
Multivariate density estimation is of great interest in various scientific and engineering disciplines. In this work, we introduce a new framework called Variance-Reduced Sketching…
Spatio-temporal model via Locally Adaptive Regression Splines
Carlos Misael Madrid Padilla, Oscar Hernan Madrid Padilla, Daren Wang
This paper focuses on the estimation of a non-parametric regression function in the presence of data with spatio-temporal dependencies. In such a context, we study Locally Adaptive…
Change point detection and inference in multivariable nonparametric models under mixing conditions
Carlos Misael Madrid Padilla, Haotian Xu, Daren Wang +2
This paper studies multivariate nonparametric change point localization and inference problems. The data consists of a multivariate time series with potentially short range depende…
Divide and Conquer Dynamic Programming: An Almost Linear Time Change Point Detection Methodology in High Dimensions
Wanshan Li, Daren Wang, Alessandro Rinaldo
We develop a novel, general and computationally efficient framework, called Divide and Conquer Dynamic Programming (DCDP), for localizing change points in time series data with hig…