This paper introduces local distance-based generalized linear models. These models extend (weighted) distance-based linear models first to the generalized linear model framework. Then, a nonparametric version of these models is proposed by means of local fitting. Distances between individuals are the only predictor information needed to fit these models. Therefore, they are applicable, among others, to mixed (qualitative and quantitative) explanatory variables or when the regressor is of functional type. An implementation is provided by the R package dbstats, which also implements other distance-based prediction methods. Supplementary material for this article is available online, which reproduces all the results of this article.

The aim of this study is to identify the factors that influence student-teachers drop-out rates, taking their individual and academic characteristics into account. The study was conducted on 531 student-teachers from the 2009 cohort. This is a quantitative and non experimental study of a longitudinal nature and an explanatory research design. To collect the data a secondary data analysis was used and the data were later analysed by the survival analysis method and modelled by the Cox proportional hazards regression model. The findings show that the individuals variables that explain student-teachers' drop-out rates correspond to the gender and place of origin (Bío-Bío región). On the other hand, the academics variables that explain student-teachers' drop-out rates correspond to the secondary education grade point average, place in the list of selected, secondary education with a scientific and humanities oriented curriculum, total signed up courses, latest academic grade point average and withdrawal from university. It is concluded that the abilities associated with the level of achievement of academic results and social support for students, management constitute important aspects to keep the commitment to stay in school academic program. To the extent that management skills and support are positive, students will have favorable interactions that will support their participation at the institutional level, which promote intellectual and academic development. Finally, it is concluded that the level of institutional policy is relevant to manage the support of the abilities and the adaptation of students as they will contribute in creating a positive balance between academic and social integration, from the configuration of elements they support the development of a context of motivation which will maintain the commitment of students achieving the goal of graduation.

The aim of this study is to predict 200.000 cases of Covid-19 in Spain. Covid-19 Spanish confirmed data obtained from Worldometer from 01 March 2020 – 17 April 2020. The data from 01 March 2020 – 10 April 2020 using to fitting with data from 11 April – 17 April 2020. For the evaluation of the forecasting accuracy measures, we use the mean absolute percentage error (MAPE). Based on the results of SutteARIMA fitting data, the accuracy of SutteARIMA for the period 11 April 2020 - 17 April 2020 is 0.61% and we forecast 20.000 confirmed cases of Spain by the WHO situation report day 90/91 which is 19 April 2020 / 20 April 2020.

Distance-based regression is a prediction method consisting of two steps: from distances between observations we obtain latent variables which, in turn, are the regressors in an ordinary least squares linear model. Distances are computed from actually observed predictors by means of a suitable dissimilarity function. Being generally nonlinearly related with the response, their selection by the usual F tests is unavailable. In this article, we propose a solution to this predictor selection problem by defining generalized test statistics and adapting a nonparametric bootstrap method to estimate their p-values. We include a numerical example with automobile insurance data.