Results 161 to 170 of about 83,569 (221)

Markov Determinantal Point Process for Dynamic Random Sets

Journal of Time Series Analysis, EarlyView.
ABSTRACT The Law of Determinantal Point Process (LDPP) is a flexible parametric family of distributions over random sets defined on a finite state space, or equivalently over multivariate binary variables. The aim of this paper is to introduce Markov processes of random sets within the LDPP framework. We show that, when the pairwise distribution of two
Christian Gouriéroux, Yang Lu
wiley   +1 more source

serojump: A Bayesian tool for inferring infection timing and antibody kinetics from longitudinal serological data. [PDF]

PLoS Comput Biol
Hodgson D, Hay J, Jarju S, Jobe D, Wenlock R, de Silva TI, Kucharski AJ.   +6 more
europepmc   +1 more source

Time‐Varying Dispersion Integer‐Valued GARCH Models

Journal of Time Series Analysis, EarlyView.
ABSTRACT We introduce a general class of INteger‐valued Generalized AutoRegressive Conditionally Heteroscedastic (INGARCH) processes by allowing simultaneously time‐varying mean and dispersion parameters. We call such models time‐varying dispersion INGARCH (tv‐DINGARCH) models.
Wagner Barreto‐Souza, Luiza S. C. Piancastelli, Konstantinos Fokianos, Hernando Ombao   +3 more
wiley   +1 more source

Generalized Probabilistic Approximate Optimization Algorithm. [PDF]

Nat Commun
Abdelrahman AS, Chowdhury S, Morone F, Camsari KY.   +3 more
europepmc   +1 more source

Testing Distributional Granger Causality With Entropic Optimal Transport

Journal of Time Series Analysis, EarlyView.
ABSTRACT We develop a novel nonparametric test for Granger causality in distribution based on entropic optimal transport. Unlike classical mean‐based approaches, the proposed method directly compares the full conditional distributions of a response variable with and without the history of a candidate predictor.
Tao Wang
wiley   +1 more source

Decoding adaptive immunity: advanced strategies in T and B cell repertoire analysis. [PDF]

J Transl Med
Yu X, Peng N, Pan W, Xie S, Tang C, Wang X, Liu H, Shi Y, He Y, Wu W, Weber D, Nie L, Liu Y, Chen Z, Deng Y, Byrne-Steele M, Wang Z, Li S.   +17 more
europepmc   +1 more source

Multiple Chains Markov Switching Vector Autoregression

Journal of Time Series Analysis, EarlyView.
ABSTRACT Both the U.S. stock and bond returns exhibit distinct Markovian regimes. However, because these regimes display limited coherence, conventional models typically require highly parameterized systems to adequately capture their joint distribution.
Leopoldo Catania
wiley   +1 more source

A robust solution for power grid management using a hybrid deterministic and probabilistic model for short term load forecasting. [PDF]

Sci Rep
Serttas F.
europepmc   +1 more source

Navigating Supply Shocks: Sector Resilience and Production Prices Through Stochastic Input–Output Modeling

Mathematical Finance, EarlyView.
ABSTRACT This study develops a novel multivariate stochastic framework for assessing systemic risks, such as climate and nature‐related shocks, within production or financial networks. By embedding a linear stochastic fluid network, interpretable as a generalized vector Ornstein–Uhlenbeck process, into the production network of interdependent ...
Giovanni Amici, Gianluca Fusai, Anna Maria Gambaro, Daniele Marazzina   +3 more
wiley   +1 more source

Bayesian Inference for Joint Estimation Models Using Copulas to Handle Endogenous Regressors

Oxford Bulletin of Economics and Statistics, EarlyView.
ABSTRACT This study proposes a Bayesian approach for finite‐sample inference of the Gaussian copula endogeneity correction. Extant studies use frequentist inference, build on a priori computed estimates of marginal distributions of explanatory variables, and use bootstrapping to obtain standard errors. The proposed Bayesian approach facilitates precise
Rouven E. Haschka
wiley   +1 more source