This paper presents a novel copula-based autoregressive framework for multilayer arrays of integer-valued time series with tensor structure. It complements recent advances in tensor time series that predominantly focus on real-valued data and overlook the unique properties of integer-valued time...

In the analysis of multivariate stochastic volatility models, many estimation procedures begin by transforming the data, taking the logarithm of the squared returns to obtain a linear state space model. A well-known series representation links the correlations between elements of the observation...

In the last few decades, the study of ordinal data in which the variable of interest is not exactly observed but only known to be in a specific ordinal category has become important. In Psychometrics such variables are analysed under the heading of item response models (IRM). In Econometrics,...

Examining the persistence of volatility transmission over an extended timeframe, regardless of specific events, reveals significant importance, as it uncovers the inherent fundamental and structural drivers that give rise to volatility. However, previous research in South Asia is minimal and has...

This paper presents a distribution-free test, based on the permutation approach, on treatment effects with a multivariate categorical response variable. The motivating example is a typical case-control biomedical study, performed to investigate the effect of the treatment called "assisted motor...

This study develops a multidimensional classification of Latin American and Caribbean countries based on a multidimensional set of economic, social, technological, and environmental indicators. This study develops a multidimensional assessment of the performance of Latin American and Caribbean...