Zobrazeno 1 - 10
of 366
pro vyhledávání: '"Matteson, David"'
Autor:
Cho, Jason B., Matteson, David S.
We introduce a novel Bayesian framework for estimating time-varying volatility by extending the Random Walk Stochastic Volatility (RWSV) model with a new Dynamic Shrinkage Process (DSP) in (log) variances. Unlike classical Stochastic Volatility or GA
Externí odkaz:
http://arxiv.org/abs/2408.11315
Autor:
Crozier, Peter A., Leibovich, Matan, Haluai, Piyush, Tan, Mai, Thomas, Andrew M., Vincent, Joshua, Mohan, Sreyas, Morales, Adria Marcos, Kulkarni, Shreyas A., Matteson, David S., Wang, Yifan, Fernandez-Granda, Carlos
Nanoparticle surface structural dynamics is believed to play a significant role in regulating functionalities such as diffusion, reactivity, and catalysis but the atomic-level processes are not well understood. Atomic resolution characterization of n
Externí odkaz:
http://arxiv.org/abs/2407.17669
Vector AutoRegressive Moving Average (VARMA) models form a powerful and general model class for analyzing dynamics among multiple time series. While VARMA models encompass the Vector AutoRegressive (VAR) models, their popularity in empirical applicat
Externí odkaz:
http://arxiv.org/abs/2406.19702
We present a Bayesian method for multivariate changepoint detection that allows for simultaneous inference on the location of a changepoint and the coefficients of a logistic regression model for distinguishing pre-changepoint data from post-changepo
Externí odkaz:
http://arxiv.org/abs/2401.02917
Non-stationary count time series characterized by features such as abrupt changes and fluctuations about the trend arise in many scientific domains including biophysics, ecology, energy, epidemiology, and social science domains. Current approaches fo
Externí odkaz:
http://arxiv.org/abs/2309.00080
Ridge detection is a classical tool to extract curvilinear features in image processing. As such, it has great promise in applications to material science problems; specifically, for trend filtering relatively stable atom-shaped objects in image sequ
Externí odkaz:
http://arxiv.org/abs/2302.00816
Publikováno v:
Data Science in Science 2:1 (2023)
Non-fungible tokens (NFT) have recently emerged as a novel blockchain hosted financial asset class that has attracted major transaction volumes. Investment decisions rely on data and adequate preprocessing and application of analytics to them. Both o
Externí odkaz:
http://arxiv.org/abs/2210.07393
We propose a flexible algorithm for feature detection and hypothesis testing in images with ultra low signal-to-noise ratio using cubical persistent homology. Our main application is in the identification of atomic columns and other features in trans
Externí odkaz:
http://arxiv.org/abs/2209.13584
We present an approach to clustering time series data using a model-based generalization of the K-Means algorithm which we call K-Models. We prove the convergence of this general algorithm and relate it to the hard-EM algorithm for mixture modeling.
Externí odkaz:
http://arxiv.org/abs/2207.00039