ﻻ يوجد ملخص باللغة العربية
We propose a flexible model for count time series which has potential uses for both underdispersed and overdispersed data. The model is based on the Conway-Maxwell-Poisson (COM-Poisson) distribution with parameters varying along time to take serial correlation into account. Model estimation is challenging however and require the application of recently proposed methods to deal with the intractable normalising constant as well as efficiently sampling values from the COM-Poisson distribution.
Generalized autoregressive moving average (GARMA) models are a class of models that was developed for extending the univariate Gaussian ARMA time series model to a flexible observation-driven model for non-Gaussian time series data. This work present
Assessing the relative merits of sportsmen and women whose careers took place far apart in time via a suitable statistical model is a complex task as any comparison is compromised by fundamental changes to the sport and society and often handicapped
It is generally known that counting statistics is not correctly described by a Gaussian approximation. Nevertheless, in neutron scattering, it is common practice to apply this approximation to the counting statistics; also at low counting numbers. We
An extension of the latent class model is presented for clustering categorical data by relaxing the classical class conditional independence assumption of variables. This model consists in grouping the variables into inter-independent and intra-depen
Count data are collected in many scientific and engineering tasks including image processing, single-cell RNA sequencing and ecological studies. Such data sets often contain missing values, for example because some ecological sites cannot be reached