[图书][B] Time series: modeling, computation, and inference
R Prado, M West - 2010 - taylorfrancis.com
Focusing on Bayesian approaches and computations using simulation-based methods for
inference, Time Series: Modeling, Computation, and Inference integrates mainstream …
inference, Time Series: Modeling, Computation, and Inference integrates mainstream …
A Bayesian nonparametric approach for estimating individualized treatment-response curves
We study the problem of estimating the continuous response over time of actions from
observational time series—a retrospective dataset where the policy by which the data are …
observational time series—a retrospective dataset where the policy by which the data are …
[HTML][HTML] Analysing the health effects of simultaneous exposure to physical and chemical properties of airborne particles
Background Airborne particles are a complex mix of organic and inorganic compounds, with
a range of physical and chemical properties. Estimation of how simultaneous exposure to air …
a range of physical and chemical properties. Estimation of how simultaneous exposure to air …
Hierarchical B ayesian clustering for nonstationary flood frequency analysis: Application to trends of annual maximum flow in G ermany
Especially for extreme precipitation or floods, there is considerable spatial and temporal
variability in long term trends or in the response of station time series to large‐scale climate …
variability in long term trends or in the response of station time series to large‐scale climate …
A novel spatio-temporal clustering algorithm with applications on COVID-19 data from the United States
S Deb, S Karmakar - Computational Statistics & Data Analysis, 2023 - Elsevier
A new clustering algorithm for spatio-temporal data is developed. The proposed method
leverages a weighted combination of a spatial haversine distance matrix and a spectral …
leverages a weighted combination of a spatial haversine distance matrix and a spectral …
A survey on Bayesian nonparametric learning for time series analysis
N Vélez-Cruz - Frontiers in Signal Processing, 2024 - frontiersin.org
Time series analysis aims to understand underlying patterns and relationships in data to
inform decision-making. As time series data are becoming more widely available across a …
inform decision-making. As time series data are becoming more widely available across a …
Predicting COVID-19 hospitalisation using a mixture of Bayesian predictive syntheses
G Kobayashi, S Sugasawa, Y Kawakubo… - The Annals of Applied …, 2024 - projecteuclid.org
Predicting COVID-19 hospitalisation using a mixture of Bayesian predictive syntheses Page 1
The Annals of Applied Statistics 2024, Vol. 18, No. 4, 3383–3404 https://doi.org/10.1214/24-AOAS1941 …
The Annals of Applied Statistics 2024, Vol. 18, No. 4, 3383–3404 https://doi.org/10.1214/24-AOAS1941 …
On a nonparametric change point detection model in Markovian regimes
AF Martinez, RH Mena - 2014 - projecteuclid.org
Change point detection models aim to determine the most probable grouping for a given
sample indexed on an ordered set. For this purpose, we propose a methodology based on …
sample indexed on an ordered set. For this purpose, we propose a methodology based on …
Hierarchical clustering of unequal-length time series with area-based shape distance
Time-series clustering algorithms have been used in a variety of areas to extract valuable
information from complex and massive data sets. However, these algorithms suffer from two …
information from complex and massive data sets. However, these algorithms suffer from two …
Clustering spatio-temporal series of confirmed COVID-19 deaths in Europe
A Bucci, L Ippoliti, P Valentini, S Fontanella - Spatial statistics, 2022 - Elsevier
The impact of the COVID-19 pandemic varied significantly across different countries, with
important consequences in the definition of control and response strategies. In this work, to …
important consequences in the definition of control and response strategies. In this work, to …