General dependence structures for some models based on exponential families with quadratic variance functions

02/26/2021
by   Luis Nieto-Barajas, et al.
0

We describe a procedure to introduce general dependence structures on a set of random variables. These include order-q moving average-type structures, as well as seasonal, periodic and spatial dependences. The invariant marginal distribution can be in any family that is conjugate to an exponential family with quadratic variance functions. Dependence is induced via latent variables whose conditional distribution mirrors the sampling distribution in a Bayesian conjugate analysis of such exponential families. We obtain strict stationarity as a special case.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/20/2020

Dependence on a collection of Poisson random variables

We propose two novel ways of introducing dependence among Poisson counts...
research
08/11/2023

Quantifying and estimating dependence via sensitivity of conditional distributions

Recently established, directed dependence measures for pairs (X,Y) of ra...
research
10/27/2021

Convolutional Deep Exponential Families

We describe convolutional deep exponential families (CDEFs) in this pape...
research
07/28/2023

Extremal Dependence of Moving Average Processes Driven by Exponential-Tailed Lévy Noise

Moving average processes driven by exponential-tailed Lévy noise are imp...
research
03/13/2022

Estimating a regression function in exponential families by model selection

Let X_1=(W_1,Y_1),…,X_n=(W_n,Y_n) be n pairs of independent random varia...
research
10/08/2017

A Perfect Sampling Method for Exponential Family Random Graph Models

Generation of deviates from random graph models with non-trivial edge de...
research
02/07/2018

Wishart laws and variance function on homogeneous cones

We present a systematic study of Riesz measures and their natural expone...

Please sign up or login with your details

Forgot password? Click here to reset