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An extension of the Generalized Autoregressive Score (GAS) model is presented for time series with excess null observations to include explanatory variables. An extension of the GAS model proposed by Harvey and Ito is suggested, and it is applied to precipitation data from a city in Chile. It is concluded that the model provides adequate prediction, and furthermore, an analysis of the relationship between the precipitation variable and the explanatory variables is shown. This relationship is compared with the meteorology literature, demonstrating concurrence. [ABSTRACT FROM AUTHOR]

This paper studies the goodness of fit test for the bivariate Hermite distribution. Specifically, we propose and study a Cramér–von Mises-type test based on the empirical probability generation function. The bootstrap can be used to consistently estimate the null distribution of the test statistics. A simulation study investigates the goodness of the bootstrap approach for finite sample sizes. [ABSTRACT FROM AUTHOR]

With the aim of mitigating the damage caused by the coronavirus disease 2019 (COVID-19) pandemic, it is important to use models that allow forecasting possible new infections accurately in order to face the pandemic in specific sociocultural contexts in the best possible way. Our first contribution is empirical. We use an extensive COVID-19 dataset from nine Latin American countries for the period of 1 April 2020 to 31 December 2021. Our second and third contributions are methodological. We extend relevant (i) state-space models with score-driven dynamics and (ii) nonlinear state-space models with unobserved components, respectively. We use weekly seasonal effects, in addition to the local-level and trend filters of the literature, for (i) and (ii), and the negative binomial distribution for (ii). We find that the statistical and forecasting performances of the novel score-driven specifications are superior to those of the nonlinear state-space models with unobserved components model, providing a potential valid alternative to forecasting the number of possible new COVID-19 infections. [ABSTRACT FROM AUTHOR]

The bivariate Poisson distribution is commonly used to model bivariate count data. In this paper we study a goodness-of-fit test for this distribution. We also provide a review of the existing tests for the bivariate Poisson distribution, and its multivariate extension. The proposed test is consistent against any fixed alternative. It is also able to detect local alternatives converging to the null at the rate n − 1 2 . The bootstrap can be employed to consistently estimate the null distribution of the test statistic. Through a simulation study we investigated the goodness of the bootstrap approximation and the power for finite sample sizes. [ABSTRACT FROM AUTHOR]

Objectives. Demonstrate the advantages of using ordinal alpha when the assumptions for Cronbach's alpha are not met; show the usefulness of ordinal alpha with the Chilean version of the WHO Alcohol Use Disorders Identification Test (AUDIT); and provide the commands in R programming language for performing the respective calculations. Methods. Cronbach's alpha and ordinal alpha were compared in an AUDIT questionnaire administered to 419 students at a Chilean university. Results. In all domains, the result using ordinal alpha was greater than with Cronbach's alpha. This finding was consistent with the literature on the subject. When ordinal alpha was chosen, the reliability of each domain and the survey as a whole was accepted, which does not always happen with Cronbach's alpha. Conclusions. In making decisions about the reliability of a dimension, consideration should be given to an index that meets the required assumptions for using it in order to avoid erroneous conclusions that can have an appreciable effect on research. The values of Cronbach's alpha underestimate the reliability of an instrument when working with ordinal scales. [ABSTRACT FROM AUTHOR]

Objectives: Demonstrate the advantages of using ordinal alpha when the assumptions for Cronbach's alpha are not met; show the usefulness of ordinal alpha with the Chilean version of the WHO Alcohol Use Disorders Identification Test (AUDIT); and provide the commands in R programming language for performing the respective calculations., Methods: Cronbach's alpha and ordinal alpha were compared in an AUDIT questionnaire administered to 419 students at a Chilean university., Results: In all domains, the result using ordinal alpha was greater than with Cronbach's alpha. This finding was consistent with the literature on the subject. When ordinal alpha was chosen, the reliability of each domain and the survey as a whole was accepted, which does not always happen with Cronbach's alpha., Conclusions: In making decisions about the reliability of a dimension, consideration should be given to an index that meets the required assumptions for using it in order to avoid erroneous conclusions that can have an appreciable effect on research. The values of Cronbach's alpha underestimate the reliability of an instrument when working with ordinal scales., Competing Interests: Conflictos de interés. Los autores declaran no tener conflictos de interés.