Fast and scalable inference for spatial extreme value models
Canadian Journal of Statistics, EarlyView.Abstract The generalized extreme value (GEV) distribution is a popular model for analyzing and forecasting extreme weather data. To increase prediction accuracy, spatial information is often pooled via a latent Gaussian process (GP) on the GEV parameters. Inference for GEV‐GP models is typically carried out using Markov Chain Monte Carlo (MCMC) methods,
Meixi Chen, Reza Ramezan, Martin Lysy
wiley +1 more source
Three-in-One: Fast and Accurate Transducer for Hybrid-Autoregressive ASR [PDF]
arXivWe present Hybrid-Autoregressive INference TrANsducers (HAINAN), a novel architecture for speech recognition that extends the Token-and-Duration Transducer (TDT) model. Trained with randomly masked predictor network outputs, HAINAN supports both autoregressive inference with all network components and non-autoregressive inference without the predictor.
arxiv
Short-term time series prediction using Hilbert space embeddings of autoregressive processes [PDF]
arXiv, 2016 Linear autoregressive models serve as basic representations of discrete time stochastic processes. Different attempts have been made to provide non-linear versions of the basic autoregressive process, including different versions based on kernel methods.
arxiv
Investors' Portfolio Behavior under Alternative Models of Long-Term Interest Rate Expectations: Unitary, Rational, or Autoregressive [PDF]
, 1979 Benjamin M. Friedman, V. Vance Roley
openalex +1 more source
Probabilistic weighted Dirichlet process mixture with an application to stochastic volatility models
Canadian Journal of Statistics, EarlyView.Abstract In this article, we propose a flexible Bayesian modelling framework and investigate the probabilistic weighted Dirichlet process mixture (pWDPM). The construction and properties of a probabilistic weight function are illustrated. The advantage of the pWDPM under the log‐squared transformed stochastic volatility (SV) model is demonstrated.
Peng Sun, Inyoung Kim, Ki‐Ahm Lee
wiley +1 more source
Auxiliary Guided Autoregressive Variational Autoencoders [PDF]
arXiv, 2017 Generative modeling of high-dimensional data is a key problem in machine learning. Successful approaches include latent variable models and autoregressive models. The complementary strengths of these approaches, to model global and local image statistics respectively, suggest hybrid models that encode global image structure into latent variables while ...
arxiv
Examining the Financial Impact of Biodiversity‐Related Reputational Disasters
Corporate Social Responsibility and Environmental Management, EarlyView.ABSTRACT This research investigates the reaction of financial markets to biodiversity‐related corporate events, utilising an EGARCH model to assess the implications on stock returns and volatility. Results reveal that markets significantly respond to these events, demonstrating heightened sensitivity and volatility that underscore the financial ...
Erdinc Akyildirim, Shaen Corbet
wiley +1 more source
TimeFlow: A Density‐Driven Pseudotime Method for Flow Cytometry Data Analysis
Cytometry Part A, EarlyView.TimeFlow is a new pseudotime computation method for multi‐dimensional flow cytometry data. It orders the cells from the least to most differentiated along their maturation pathway and is useful in modeling the temporal dynamics of surface protein markers along linear trajectories of bone marrow cell populations.
Margarita Liarou +2 more
wiley +1 more source
Two-dimensional autoregressive model in a steganographic method based on the direct spread spectrum
Безопасность информационных технологий, 2016 Considered steganographic method of information security based on the direct spreading. Possibility of two-dimensional autoregressive model application in this steganographic method is investigated.
Rodion Khamzaevich Baltaev +1 more
doaj
Liu-type pretest and shrinkage estimation for the conditional autoregressive model. [PDF]
PLoS One, 2023 Al-Momani M.
europepmc +1 more source