The use of Markov chain Monte Carlo (MCMC) methods for estimating hierarchical models
involves complex data structures and is often described as a revolutionary development. An …

An intermediate-level treatment of Bayesian hierarchical models and their applications, this
book demonstrates the advantages of a Bayesian approach to data sets involving inferences …

Overdispersion in time series of counts is very common and has been well studied by many
authors, but the opposite phenomenon of underdispersion may also be encountered in real …

The limit distribution of the quasi-maximum likelihood estimator (QMLE) for parameters in
the ARMA-GARCH model remains an open problem when the process has infinite 4th …

The paper considers the so-called double AR (p) model, yt=∑ i= 1 p ϕ iyt− i+ η t ω+∑ i= 1 p
α iyt− i 2, where η t∼ iid N (0, 1). It is shown that the necessary and sufficient condition for …

This paper introduces the concept of risk parameter in conditional volatility models of the
form ϵ t= σ t (θ 0) η t and develops statistical procedures to estimate this parameter. For a …

Rate-optimal robust estimation of high-dimensional vector autoregressive models Page 1 The
Annals of Statistics 2023, Vol. 51, No. 2, 846–877 https://doi.org/10.1214/23-AOS2278 © …

In this work, we consider doubly Markov switching AR models, where analytic tractability and
flexibility are quite simply a competitive advantage, which becomes an attractive tool for …

In this article, we develop new bootstrap-based inference for noncausal autoregressions
with heavy-tailed innovations. This class of models is widely used for modeling bubbles and …

This article first proposes a score-based test for a double autoregressive model against a
threshold double autoregressive (AR) model. It is an asymptotically distribution-free test and …