Adriano Kamimura Suzuki

1. The GARCH class of models, initially introduced by Bollerslev (1986), represented a significant advancement in the study of financial time series due to its ability to capture volatility cluster...

Financial time series often exhibit skewness and heavy tails, making it essential to use models that incorporate these characteristics to ensure greater reliability in the results. Furthermore, allowing temporal variation in the skewness parameter can bring significant gains in the analysis of this type of series. However, for more robustness, it is crucial to develop models that balance flexibility and parsimony. In this paper, we propose dynamic skewness stochastic volatility models in the scale mixture of skew normal (SMSN) family (DynSSV‐SMSN), using priors that penalize model complexity. Parameter estimation is carried out using the Hamiltonian Monte Carlo (HMC) method via the RStan package. Simulation results demonstrated that penalizing priors present superior performance in several scenarios compared to the classical choices. In the empirical application to returns of cryptocurrencies, models with heavy tails and dynamic skewness provided a better fit to the data according to the DIC, WAIC, and LOO‐CV information criteria, as well as an improvement in the accuracy of expected shortfall forecasts for Bitcoin applications.

Data imbalance is a critical challenge in classification tasks, especially in the medical field, where accurate disease identification is essential. This study investigates the impact of imbalance in medical image datasets and proposes the use of transfer learning models combined with resampling and data augmentation to improve skin tumor detection. Images from ISIC (International Skin Imaging Collaboration) 2016, 2017, and 2018 were used, and simulations were performed varying the proportion of the minority class between 10% and 50%, considering data augmentation and resampling; we used a threshold-based ensemble of 13 convolutional neural network models.We evaluated the performance of combinations with standardized metrics, placing emphasis on the F₂-score. The results showthat individual models have difficulty identifying malignant tumors when the imbalance is higher (10% malignant and 90% benign samples). However, the use of resampling and data augmentation and ensemble improves the outcome of these models to predict the malignant class.We conclude that the combination of these techniques is effective in dealing with imbalance, providing improvements in predictive performance.

Financial time series often exhibit skewness and heavy tails, making it essential to use models that incorporate these characteristics to ensure greater reliability in the results. Furthermore, allowing temporal variation in the skewness parameter can bring significant gains in the analysis of this type of series. However, for more robustness, it is crucial to develop models that balance flexibility and parsimony. In this paper, we propose dynamic skewness stochastic volatility models in the SMSN family (DynSSV-SMSN), using priors that penalize model complexity. Parameter estimation was carried out using the Hamiltonian Monte Carlo (HMC) method via the \texttt{RStan} package. Simulation results demonstrated that penalizing priors present superior performance in several scenarios compared to the classical choices. In the empirical application to returns of cryptocurrencies, models with heavy tails and dynamic skewness provided a better fit to the data according to the DIC, WAIC, and LOO-CV information criteria.

Este estudo compara modelos de Análise de Sobrevivência para avaliar fatores de risco associados ao desligamento de funcionários em empresas nãopúblicas do Nordeste. Os dados utilizados são da Relação Anual de Informações Socioeconômicas (RAIS) de 2015, abrangendo todos os estados da região. Constatouse que a maioria dos trabalhadores nessas empresas são homens, pardos, brasileiros,com idade entre 18 e 39 anos. Predominam nas capitais e regiões metropolitanas, com salários de até três mínimos e carga horária de 41 a 44 horas semanais. São admitidos por reemprego, permanecem cerca de três meses e são desligados por iniciativa do empregador, sem justa causa. Comparando modelos de sobrevivência com o estimador de Kaplan-Meier e o Critério de Informação de Akaike (AIC), o modelo gama generalizado mostrou-se mais adequado aos dados. As principais covariáveis analisadas incluíram sexo, idade, raça/cor e faixa salarial média em salários mínimos.

Deep Learning models typically require large datasets to achieve high performance, which can be challenging when applied to medical data due to the difficulty in acquiring sufficient observations, particularly when the data is imbalanced between categories like pathological and normal cases. To address this, approaches like transfer learning, ensemble methods, and data augmentation through Generative Adversarial Networks (GANs) can be utilized to improve classification tasks on small datasets. In situations with imbalanced data, resampling techniques are also crucial. This research combines transfer learning with resampling methods to boost the prediction accuracy of minority class samples in small, imbalanced datasets. Additionally, techniques like data retracing and GAN-based augmentation are applied. The dataset used includes small, imbalanced images of skin cancer, aimed at classifying them as malignant or benign. The findings reveal that models using resampling techniques achieve better results, while those without resampling underperform. This underscores the benefit of resampling in enhancing prediction, particularly for the minority class. Furthermore, using GANs for data augmentation improves model performance over those that do not incorporate this technique.

Copulas are quite a popular, flexible, and useful tool for multivariate modeling in many fields of applications, including finance and insurance, actuarial sciences, biomedical studies, geostatistics, and hydrology. In this paper, we use the concept of diagonal function to build new bivariate copulas based on the well known and important Ali–Mikhail–Haq (AMH), Farlie–Gumbel–Morgenstern (FGM) and Plackett families. We also study some dependence properties of the newly constructed semilinear copulas, namely, the Spearman’s [ ] coefficient. In particular, we find that the range of this rank correlation coefficient varies between weak and moderate values for the transformed AMH and FGM families, although it varies between moderate and strong values for the transformed Plackett families. Moreover, in all cases, the Spearman’s [ ] coefficient reaches negative and positive values. Finally, we present a synthetic data generation algorithm, as well as a classical method of estimation of the copula association parameter [ ] for each of the six transformed bivariate families.

1. Some statistical techniques, such as linear or mixed generalized linear models, do not consider the censored observations during the modeling, which cause a loss of relevant information. In this...