Autoregressive models have played an important role in time series. In this paper, an autoregressive model based on the skew-normal distribution is considered. The estimation of its parameters is carried out by using the expectation–maximization algorithm, whereas the diagnostic analytics are conducted by means of the local influence method. Normal curvatures for the model under four perturbation schemes are established. Simulation studies are conducted to evaluate the performance of the proposed procedure. In addition, an empirical example involving weekly financial return data are analyzed using the procedure with the proposed diagnostic analytics, which has improved the model fit.
Correlated binary responses are commonly described by mixed effects logistic regression models. This article derives a diagnostic methodology based on the Qdisplacement function to investigate local influence of the responses in the maximum likelihood estimates of the parameters and in the predictive performance of the mixed effects logistic regression model. An appropriate perturbation strategy of the probability of success is established, as a form of assessing the perturbation in the response. The diagnostic methodology is evaluated with Monte Carlo simulations. Illustrations with two real-world data sets (balanced and unbalanced) are conducted to show the potential of the proposed methodology.
KeywordsApproximation of integrals • Correlated binary responses • Metropolis-Hastings and Monte Carlo methods • Probability of success • R software
Introduction Suicide is the second leading cause of premature death in people between 15 and 29 years old and the third in young people between 15 and 19 years old. Adolescence is a critical period concerning mental health disorders since there is greater vulnerability to suicidal behaviors. The situation in Latin America is worrying, with Chile being one of the two countries where suicide rates of children and adolescents increase yearly. This study aims to analyze clinical, psychological, family, and social risk factors associated with suicidal behavior in a clinical sample of adolescents treated in the public health system of the Maule region. Methods The study design is cross- sectional. We used a sample of 388 adolescents between 10 and 21 years old admitted to the health system of the Maule Region. The participants were evaluated by applying five measuring instruments (The Barrat Impulsivity Scale, The Difficulties in Emotional Regulation Scale, The Depression, Anxiety and Stress Scale, The General Help-Seeking Questionnaire for mental health problems in adolescents, and The Columbia Suicide Severity Rating Scale) in addition to collecting social and family information and relevant clinical history from the medical records. Results The analysis allowed us to identify distinctive characteristics of adolescent suicidal behavior by describing clinical, psychological, and family social factors. Conclusions Adolescents with a history of suicide attempts are characterized by having suicidal ideation, anxious-depressive symptoms, stress, insomnia, and impulsiveness. Likewise, they report being non-religious, belonging to sexual minorities, and victims of sexual harassment and/or abuse.
Asthma is one of the most common chronic diseases around the world and represents a serious problem in human health. Predictive models have become important in medical sciences because they provide valuable information for data-driven decision-making. In this work, a methodology of data-influence analytics based on mixed-effects logistic regression models is proposed for detecting potentially influential observations which can affect the quality of these models. Global and local influence diagnostic techniques are used simultaneously in this detection, which are often used separately. In addition, predictive performance measures are considered for this analytics. A study with children and adolescent asthma real data, collected from a public hospital of São Paulo, Brazil, is conducted to illustrate the proposed methodology. The results show that the influence diagnostic methodology is helpful for obtaining an accurate predictive model that provides scientific evidence when data-driven medical decision-making.
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