Comparing Bayesian Spatial Conditional Overdispersion and the Besag–York–Mollié Models: Application to Infant Mortality Rates

In this paper, we review overdispersed Bayesian generalized spatial conditional count data models. Their usefulness is illustrated with their application to infant mortality rates from Colombian regions and by comparing them with the widely used Besag–York–Mollié (BYM) models. These overdispersed models assume that excess of dispersion in the data may be partially caused from the possible spatial dependence existing among the different spatial units. Thus, specific regression structures are then proposed both for the conditional mean and for the dispersion parameter in the models, including covariates, as well as an assumed spatial neighborhood structure. We focus on the case of response variables following a Poisson distribution, specifically concentrating on the spatial generalized conditional normal overdispersion Poisson model. Models were fitted by making use of the Markov Chain Monte Carlo (MCMC) and Integrated Nested Laplace Approximation (INLA) algorithms in the specific context of Bayesian estimation methods.This work was supported by Ministerio de Economía y Competitividad (Spain), Agencia Estatal de Investigación (AEI), and the European Regional Development Fund (ERDF), under research grant MTM2016-74931-P (AEI/ERDF, EU), and by the Department of Education of the Basque Government (UPV/EHU Econometrics Research Group) under research grant IT-1359-19

Two-Stage Nonparametric Regression for Longitudinal Data

In the analysis of longitudinal data it is of main interest to investigate the existence of group and individual effects under correlated observations across time. In this paper, we develop a nonparametric two-step procedure that enables us to estimate group effects under a very general form of correlation across time. Moreover, we propose several methods to estimate the bandwidth and show their asymptotyc optimality. Since the asymptotic distribution is untractable, we develop a randomization test that is suitable for testing the group effects. Finally, we apply the estimation procedure, the bandwidth selection criteria and the randomization test to the data from the Iowa Cochlear Implant Project.This work was supported by Dirección General de Enseñanza Superior del Ministerio Español de Educación y Cultura and Universidad del País Vasco (UPV/EHU) under research grant PB95-0346

Survival Analysis Using a Censored Semiparametric Regression Model

In this work we study the effect of several covariates X on a censored response variable T with unknown probability distribution. A semiparametric model is proposed to consider situations where the functional form of the effect of one or more covariates is unknown. We provide its estimation procedure and, in addition, a bootstrap technique to make inference on the parameters. An application with a real dataset is presented, as well as some simulation results, to demonstrate the good behavior of the proposed estimation process and to analyze the effect of the censorship. This new model has an important application field in reliability, survival or lifetime data analysis.censorship, Kaplan-Meier, lifetime data models, bootstrap, nonparametric estimation

Two-Stage Nonparametric Regression for Longitudinal Data

In the analysis of longitudinal data it is of main interest to investigate the existence of group and individual effects under correlated observations across time. In this paper, we develop a nonparametric two-step procedure that enables us to estimate group effects under a very general form of correlation across time. Moreover, we propose several methods to estimate the bandwidth and show their asymptotyc optimality. Since the asymptotic distribution is untractable, we develop a randomization test that is suitable for testing the group effects. Finally, we apply the estimation procedure, the bandwidth selection criteria and the randomization test to the data from the Iowa Cochlear Implant Project.Kernel estimation, bandwidth selection, nonstationary errors, group effects, randomization test

Analysis of Length of Time Spent in Chapter 11 Bankruptcy

This paper investigates original issuers of high yield bonds in Chapter 11 bankruptcy to determine which factors affect the length of time spent in Chapter 11. In order to do this analysis we propose a flexible new duration model, the censored partial regression model. This model allows us to consider the effect of some variable on the duration using a nonparametric functional form. We find that the choice of prepackaged Chapter 11, the length of time negotiating before filling for Chapter 11, the profitability, the highly leveraged transactions, the participation on different disputes, the role of vulture funds and some institutional changes turn out to be relevant to analyze this duration.lifetime data models, censorship, Kaplan-Meier, bootstrap, nonparametric estimation

Two-Stage Nonparametric Regression for Longitudinal Data

In the analysis of longitudinal data it is of main interest to investigate the existence of group and individual effects under correlated observations across time. In this paper, we develop a nonparametric two-step procedure that enables us to estimate group effects under a very general form of correlation across time. Moreover, we propose several methods to estimate the bandwidth and show their asymptotyc optimality. Since the asymptotic distribution is untractable, we develop a randomization test that is suitable for testing the group effects. Finally, we apply the estimation procedure, the bandwidth selection criteria and the randomization test to the data from the Iowa Cochlear Implant Project.This work was supported by Dirección General de Enseñanza Superior del Ministerio Español de Educación y Cultura and Universidad del País Vasco (UPV/EHU) under research grant PB95-0346

Analysis of Length of Time Spent in Chapter 11 Bankruptcy

This paper investigates original issuers of high yield bonds in Chapter 11 bankruptcy to determine which factors affect the length of time spent in Chapter 11. In order to do this analysis we propose a flexible new duration model, the censored partial regression model. This model allows us to consider the effect of some variable on the duration using a nonparametric functional form. We find that the choice of prepackaged Chapter 11, the length of time negotiating before filling for Chapter 11, the profitability, the highly leveraged transactions, the participation on different disputes, the role of vulture funds and some institutional changes turn out to be relevant to analyze this duration.This work was partially supported by Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU), Dirección General de Enseñanza Superior e Investigación Científica del Ministerio Español de Educación y Cultura and Gobierno Vasco under research grants UPV 038.321-HA129/99, PB98-0149, PI-1999-70 and PI-1999-46

Survival Analysis Using a Censored Semiparametric Regression Model

In this work we study the effect of several covariates X on a censored response variable T with unknown probability distribution. A semiparametric model is proposed to consider situations where the functional form of the effect of one or more covariates is unknown. We provide its estimation procedure and, in addition, a bootstrap technique to make inference on the parameters. An application with a real dataset is presented, as well as some simulation results, to demonstrate the good behavior of the proposed estimation process and to analyze the effect of the censorship. This new model has an important application field in reliability, survival or lifetime data analysis.This work was partially supported by Universidad del País Vasco/Euskal Herriko Unibertsitatea (UPV/EHU) and Dirección General de Enseñanza Superior del Ministerio Español de Educación y Cultura under research grants UPV 038.321-HA129/99 and PB-98-0149

Documento español de consenso sobre diagnóstico, estabilización y tratamiento del síndrome inflamatorio multisistémico pediátrico vinculado a SARS-CoV-2 (SIM-PedS).

A new paediatric multisystem inflammatory syndrome, linked to SARS-CoV-2, has been described. The clinical picture is variable and is associated with an active or recent infection due to SARS-CoV-2. A review of the existing literature by a multidisciplinary group of paediatric specialists is presented in this document. Later, they make recommendations on the stabilisation, diagnosis, and treatment of this syndrome