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08/17/2023
Sparse reconstruction of ordinary differential equations with inference
Sparse regression has emerged as a popular technique for learning dynami...
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05/24/2023
Modeling Multiple Irregularly Spaced Financial Time Series
In this paper we propose univariate volatility models for irregularly sp...
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06/10/2022
Modeling Multivariate Positive-Valued Time Series Using R-INLA
In this paper we describe fast Bayesian statistical analysis of vector p...
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12/31/2021
An empirical Bayes approach to estimating dynamic models of co-regulated gene expression
Time-course gene expression datasets provide insight into the dynamics o...
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09/17/2021
Graphical models for nonstationary time series
We propose NonStGGM, a general nonparametric graphical modeling framewor...
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07/30/2021
A Survey of Estimation Methods for Sparse High-dimensional Time Series Models
High-dimensional time series datasets are becoming increasingly common i...
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07/30/2020
Random Forests for dependent data
Random forest (RF) is one of the most popular methods for estimating reg...
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06/26/2019
A Debiased MDI Feature Importance Measure for Random Forests
Tree ensembles such as Random Forests have achieved impressive empirical...
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12/09/2018
Low Rank and Structured Modeling of High-dimensional Vector Autoregressions
Network modeling of high-dimensional time series data is a key learning ...
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12/03/2018
Large Spectral Density Matrix Estimation by Thresholding
Spectral density matrix estimation of multivariate time series is a clas...
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10/16/2018
Refining interaction search through signed iterative Random Forests
Advances in supervised learning have enabled accurate prediction in biol...
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04/23/2018
High Dimensional Estimation and Multi-Factor Models
The purpose of this paper is to re-investigate the estimation of multipl...
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11/09/2017
Interpretable Vector AutoRegressions with Exogenous Time Series
The Vector AutoRegressive (VAR) model is fundamental to the study of mul...
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06/26/2017
