The purpose of our research is to predict the short-term of confirmed cases of in Spain by using SutteARIMA method. Covid-19 Spanish confirmed data obtained from Worldometer and Spain Stock Market data (IBEX 35) data obtained from Yahoo Finance. Data starts from 12 IBEX). In this study, the SutteARIMA method will be used. For the evaluation of the forecasting methods we applied forecasting accuracy measures, mean absolute percentage error (MAPE). Based on the results of ARIMA and SutteARIMA forecasting methods, we conclude that the SutteARIMA method is most suitable than ARIMA to calculate the daily forecasts of confirmed cases of Covid-19 and IBEX in Spain. The MAPE value of 0.1905 (smaller than 0.04 compared to MAPE value of ARIMA) for confirmed cases of Covid-19 in Spain and 0,0202 for IBEX stock. At the end of the analysis, using the SutteARIMA method, we calculate daily forecasts of confirmed cases of Covid-

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Highlights Covid-19 spreading in US. 3.000.000 cases of Covid-19 in US SutteARIMA Forecasting can provide an overview of the conditions to come and can be used as a reference in decision making.

This paper introduces local distance-based generalized linear models. These models extend (weighted) distance-based linear models firstly 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 distancebased prediction methods. Supplementary material for this article is available online, which reproduces all the results of this article.

We consider the problem of nonparametrically predicting a scalar response variable y from a functional predictor χ. We have n observations (χ i , y i ) and we assign a weightis a semi-metric, K is a kernel function and h is the bandwidth. Then we fit a Weighted (Linear) Distance-Based Regression, where the weights are as above and the distances are given by a possibly different semi-metric. This approach can be extended to nonparametric predictions from other kind of explanatory variables (e.g., data of mixed type) in a natural way.

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.

Resumen. El objetivo del artículo es identificar los factores que influyen en la deserción de estudiantes de pedagogía, considerando sus características individuales y académicas. El estudio se realizó con 531 estudiantes de la cohorte 2009. La investigación es de tipo cuantitativo, con un diseño explicativo, longitudinal y no experimental. La información se recolectó a partir de datos secundarios, los cuales fueron analizados según el método de análisis de supervivencia, modelados a través de la regresión de riesgos proporcionales de Cox. Los resultados demostraron que las variables individuales que explican la deserción de los estudiantes corresponden al sexo y la procedencia de la región del Bio Bio. Por otro lado, las variables académicas que explican la deserción universitaria corresponden al promedio de notas de enseñanza media, el lugar en la lista de seleccionados, provenir de un establecimiento secundario científico-humanista, el total de asignaturas inscritas, el último promedio curricular y la suspensión de estudios. Se concluye que las capacidades asociadas al nivel de logro de los resultados académicos y la gestión de apoyo social para los estudiantes, se constituyen en aspectos significativos para mantener el compromiso por permanecer en el programa académico. En la medida que las capacidades y la gestión de apoyo sean positivas, los estudiantes contarán con interacciones favorables que apoyarán su participación a nivel institucional, lo cual favorecerá su desarrollo intelectual y académico. Finalmente, se concluye que a nivel de política institucional resulta relevante gestionar el apoyo de las capacidades y la adaptación de los estudiantes, ya que se contribuirá en la generación de un equilibrio positivo entre la integración académica y social, a partir de la configuración de elementos que apoyarán el desarrollo de un contexto de motivación que permitirá mantener el compromiso de los estudiantes por el logro de la meta de graduación. Palabras clave: deserción estudiantil; educación superior; análisis de supervivencia; modelo de regresión Cox.[en] Explanatory factors the student teachers drop out rates Abstract. 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

This work analyzes and quantifies the significance of private long-term care insurance for the elderly in protecting families from the increased expenses derived from dependency. We propose an economic and financial model for consumption and income deficit evolution. Survival/dependency are modeled by a Markov process with stochastic simulation techniques to obtain random variable distributions. Based on the Spanish survey of household finances data, Spanish families are classified using a cluster analysis for the wealth decumulation period. The conclusion is that, for a generic family, hiring long-term care insurance causes a significant reduction in the probability of lack of liquidity, the mean first time of lack of liquidity (if it occurs), and the mean present value of overall liquidity needs. It is also observed that there are important differences between these impacts on different groups of families. These results show that hiring long-term care insurance would considerably lower financial problems in the decumulation period.