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Related papers: Bayesian GARMA Models for Count Data

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Robust Bayesian models are appealing alternatives to standard models, providing protection from data that contains outliers or other departures from the model assumptions. Historically, robust models were mostly developed on a case-by-case…

Machine Learning · Statistics 2016-09-08 Chong Wang , David M. Blei

A new approach for Bayesian model averaging (BMA) and selection is proposed, based on the mixture model approach for hypothesis testing in Kaniav et al., 2014. Inheriting from the good properties of this approach, it extends BMA to cases…

Methodology · Statistics 2018-08-02 Merlin Keller , Kaniav Kamary

We introduce a new dynamical system for sequentially observed multivariate count data. This model is based on the gamma--Poisson construction---a natural choice for count data---and relies on a novel Bayesian nonparametric prior that ties…

Machine Learning · Statistics 2017-01-23 Aaron Schein , Mingyuan Zhou , Hanna Wallach

This paper proposes to use a rather new modelling approach in the realm of solar radiation forecasting. In this work, two forecasting models: Autoregressive Moving Average (ARMA) and Neural Network (NN) models are combined to form a model…

Applications · Statistics 2012-03-27 Philippe Lauret , Auline Rodler , Marc Muselli , Mathieu David , Hadja Diagne , Cyril Voyant

In this paper we introduce the Kumaraswamy autoregressive moving average models (KARMA), which is a dynamic class of models for time series taking values in the double bounded interval $(a,b)$ following the Kumaraswamy distribution. The…

Methodology · Statistics 2023-01-16 Fábio Mariano Bayer , Débora Missio Bayer , Guilherme Pumi

We review autoregressive models for the analysis of multivariate count time series. In doing so, we discuss the choice of a suitable distribution for a vectors of count random variables. This review focus on three main approaches taken for…

Methodology · Statistics 2021-09-21 Konstantinos Fokianos

A general challenge in statistics is prediction in the presence of multiple candidate models or learning algorithms. Model aggregation tries to combine all predictive distributions from individual models, which is more stable and flexible…

Methodology · Statistics 2021-09-28 Yuling Yao

We express the classic ARMA time-series model as a directed graphical model. In doing so, we find that the deterministic relationships in the model make it effectively impossible to use the EM algorithm for learning model parameters. To…

Applications · Statistics 2012-08-10 Bo Thiesson , David Maxwell Chickering , David Heckerman , Christopher Meek

Motivated by the application to German interest rates, we propose a timevarying autoregressive model for short and long term prediction of time series that exhibit a temporary non-stationary behavior but are assumed to mean revert in the…

Methodology · Statistics 2021-02-23 Christoph Berninger , Almond Stöcker , David Rügamer

Graphical models are ubiquitous tools to describe the interdependence between variables measured simultaneously such as large-scale gene or protein expression data. Gaussian graphical models (GGMs) are well-established tools for…

Methodology · Statistics 2020-01-09 Nilabja Guha , Veera Baladandayuthapani , Bani K. Mallick

This paper presents a framework for binary autoregressive time series in which each observation is a Bernoulli variable whose success probability evolves with past outcomes and probabilities, in the spirit of GARCH-type dynamics,…

Econometrics · Economics 2026-04-17 Anna Bykhovskaya , Nour Meddahi

Regression models are popular tools in empirical sciences to infer the influence of a set of variables onto a dependent variable given an experimental dataset. In neuroscience and cognitive psychology, Generalized Linear Models (GLMs)…

Applications · Statistics 2020-02-04 Vincent Adam , Alexandre Hyafil

In this paper, we propose a novel and efficient two-stage variable selection approach for sparse GLARMA models, which are pervasive for modeling discrete-valued time series. Our approach consists in iteratively combining the estimation of…

Methodology · Statistics 2022-08-31 Marina Gomtsyan , Céline Lévy-Leduc , Sarah Ouadah , Laure Sansonnet , Thomas Blein

Within the past two decades, Gaussian process regression has been increasingly used for modeling dynamical systems due to some beneficial properties such as the bias variance trade-off and the strong connection to Bayesian mathematics. As…

Systems and Control · Electrical Eng. & Systems 2021-02-11 Thomas Beckers

We use the theory of normal variance-mean mixtures to derive a data augmentation scheme for models that include gamma functions. Our methodology applies to many situations in statistics and machine learning, including Multinomial-Dirichlet…

Methodology · Statistics 2021-06-22 Jingyu He , Nicholas Polson , Jianeng Xu

Beta-binomial/Poisson models have been used by many authors to model multivariate count data. Lora and Singer (Statistics in Medicine, 2008) extended such models to accommodate repeated multivariate count data with overdipersion in the…

Methodology · Statistics 2010-03-08 Mayra Ivanoff Lora , Julio M Singer

We consider a prior for nonparametric Bayesian estimation which uses finite random series with a random number of terms. The prior is constructed through distributions on the number of basis functions and the associated coefficients. We…

Statistics Theory · Mathematics 2015-02-10 Weining Shen , Subhashis Ghosal

Gaussian graphical models (GGMs) are well-established tools for probabilistic exploration of dependence structures using precision matrices. We develop a Bayesian method to incorporate covariate information in this GGMs setup in a nonlinear…

This research proposes a flexible Bayesian extension of the composite Gaussian process (CGP) model of Ba and Joseph (2012) for predicting (stationary or) non-stationary $y(\mathbf{x})$. The CGP generalizes the regression plus stationary…

Methodology · Statistics 2019-06-27 Casey B. Davis , Christopher M. Hans , Thomas J. Santner

Mixture autoregressive (MAR) models provide a flexible way to model time series with predictive distributions which depend on the recent history of the process and are able to accommodate asymmetry and multimodality. Bayesian inference for…

Methodology · Statistics 2020-06-22 Davide Ravagli , Georgi N. Boshnakov