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This paper expands traditional stochastic volatility models by allowing for time-varying skewness without imposing it. While dynamic asymmetry may capture the likely direction of future asset returns, it comes at the risk of leading to overparameterization. Our proposed approach mitigates this concern by leveraging sparsity-inducing priors to automatically select the skewness parameter as dynamic, static or zero in a data-driven framework. We consider two empirical applications. First, in a bond yield application, dynamic skewness captures interest rate cycles of monetary easing and tightening and is partially explained by central banks' mandates. In a currency modeling framework, our model indicates no skewness in the carry factor after accounting for stochastic volatility. This supports the idea of carry crashes resulting from volatility surges instead of dynamic skewness.

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Human immunodeficiency virus (HIV) dynamics have been the focus of epidemiological and biostatistical research during the past decades to understand the progression of acquired immunodeficiency syndrome (AIDS) in the population. Although there are several approaches for modeling HIV dynamics, one of the most popular is based on Gaussian mixed-effects models because of its simplicity from the implementation and interpretation viewpoints. However, in some situations, Gaussian mixed-effects models cannot (a) capture serial correlation existing in longitudinal data, (b) deal with missing observations properly, and (c) accommodate skewness and heavy tails frequently presented in patients' profiles. For those cases, mixed-effects state-space models (MESSM) become a powerful tool for modeling correlated observations, including HIV dynamics, because of their flexibility in modeling the unobserved states and the observations in a simple way. Consequently, our proposal considers an MESSM where the observations' error distribution is a skew-t. This new approach is more flexible and can accommodate data sets exhibiting skewness and heavy tails. Under the Bayesian paradigm, an efficient Markov chain Monte Carlo algorithm is implemented. To evaluate the properties of the proposed models, we carried out some exciting simulation studies, including missing data in the generated data sets. Finally, we illustrate our approach with an application in the AIDS Clinical Trial Group Study 315 (ACTG-315) clinical trial data set.

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The pandemic COVID-19 brings with it the need for studies and tools to help those in charge make decisions. Working with classical time series methods such as ARIMA and SARIMA has shown promising results in the first studies of COVID-19. We advance in this branch by proposing a risk factor map induced by the well-known Pearson diagram based on multivariate kurtosis and skewness measures to analyze the dynamics of deaths from COVID-19. In particular, we combine bootstrap for time series with SARIMA modeling in a new paradigm to construct a map on which one can analyze the dynamics of a set of time series. The proposed map allows a risk analysis of multiple countries in the four different periods of the pandemic COVID-19 in 55 countries. Our empirical evidence suggests a direct relationship between the multivariate skewness and kurtosis. We observe that the multivariate kurtosis increase leads to the rise of the multivariate skewness. Our findings reveal that the countries with high risk from the behavior of the number of deaths tend to have pronounced skewness and kurtosis values.

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In several applications, the assumption of normality is often violated in data with some level of skewness, so skewness affects the mean's estimation. The class of skew-normal distributions is considered, given their flexibility for modeling data with asymmetry parameter. In this paper, we considered two location parameter (µ) estimation methods in the skew-normal setting, where the coefficient of variation and the skewness parameter are known. Specifically, the least square estimator (LSE) and the best unbiased estimator (BUE) for µ are considered. The properties for BUE (which dominates LSE) using classic theorems of information theory are explored, which provides a way to measure the uncertainty of location parameter estimations. Specifically, inequalities based on convexity property enable obtaining lower and upper bounds for differential entropy and Fisher information. Some simulations illustrate the behavior of differential entropy and Fisher information bounds.

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The aim of this work was to evaluate the corrosion behavior of the AA6061 and AlSi10Mg alloys produced by extruded and additive manufacturing (selective laser melting, SLM). Alloys were immersed in two electrolytes in H2O and 3.5 wt. % NaCl solutions at room temperature and their corrosion behavior was studied by electrochemical noise technique (EN). Three different methods filtered EN signals, and the statistical analysis was employed to obtain Rn, the localization index (LI), Kurtosis, skew, and the potential spectral density analysis (PSD). The Energy Dispersion Plots (EDP) of wavelets method was employed to determine the type of corrosion and the Hilbert-Huang Transform (HHT), analyzing the Hilbert Spectra. The result indicated that the amplitude of the transients in the time series in potential and current is greater in the AlSi10Mg alloy manufactured by additive manufacturing. The amplitude of the transients decreases in both alloys (AA6061 and AlSi10Mg) as time increases.

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<b>Background and Objective:</b> Forage production in the tropics is generally asymmetrically distributed. Hence the need to use more complex models, especially when multiple comparisons are made and there are very large deviations from normality. The objective of this research is to fit a Generalized Additive Model for Location, Scale and Shape (GAMLSS) model on accumulated dry matter data from <i>Brachiaria brizantha</i> using a model selection algorithm. <b>Materials and Methods:</b> A Box-Cox Power Exponential (BCPE) distribution was adjusted on the dry matter from <i>Brachiaria brizantha</i> data implementing GAMLSS in R (programming language). The accumulated dry matter data for <i>B. brizantha</i> were obtained from a study carried out on a farm in the state of Portuguesa, Venezuela. The explanatory covariate x was the interval between cuts (21, 28, 35 and 42 days). <b>Results:</b> The dependent variable (dry matter) exhibited both skewness and kurtosis. GAMLSS allowed flexible modeling of both the distribution of the dry matter yield from <i>B. brizantha</i> and the dependence of all the parameters of the distribution on intervals between cuttings. For the dry matter yield from <i>B. brizantha</i>, which exhibited skewness and leptokurtosis, the BCPE distribution, provided the best fit. <b>Conclusion:</b> The interval between cuttings showed an effect that is reflected in the average yield of dry matter from <i>B. brizantha</i>. The interval between cuts affected the skewness and the kurtosis of the distribution.

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The sample selection bias problem occurs when the outcome of interest is only observed according to some selection rule, where there is a dependence structure between the outcome and the selection rule. In a pioneering work, J. Heckman proposed a sample selection model based on a bivariate normal distribution for dealing with this problem. Due to the non-robustness of the normal distribution, many alternatives have been introduced in the literature by assuming extensions of the normal distribution like the Student-t and skew-normal models. One common limitation of the existent sample selection models is that they require a transformation of the outcome of interest, which is common R + -valued, such as income and wage. With this, data are analyzed on a non-original scale which complicates the interpretation of the parameters. In this paper, we propose a sample selection model based on the bivariate Birnbaum-Saunders distribution, which has the same number of parameters that the classical Heckman model. Further, our associated outcome equation is R + -valued. We discuss estimation by maximum likelihood and present some Monte Carlo simulation studies. An empirical application to the ambulatory expenditures data from the 2001 Medical Expenditure Panel Survey is presented.

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In biomedical studies, the analysis of longitudinal data based on Gaussian assumptions is common practice. Nevertheless, more often than not, the observed responses are naturally skewed, rendering the use of symmetric mixed effects models inadequate. In addition, it is also common in clinical assays that the patient's responses are subject to some upper and/or lower quantification limit, depending on the diagnostic assays used for their detection. Furthermore, responses may also often present a nonlinear relation with some covariates, such as time. To address the aforementioned three issues, we consider a Bayesian semiparametric longitudinal censored model based on a combination of splines, wavelets, and the skew-normal distribution. Specifically, we focus on the use of splines to approximate the general mean, wavelets for modeling the individual subject trajectories, and on the skew-normal distribution for modeling the random effects. The newly developed method is illustrated through simulated data and real data concerning AIDS/HIV viral loads.

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Bayesian modeling growth curves for quail assuming skewness in errors - To assume normal distributions in the data analysis is common in different areas of the knowledge. However we can make use of the other distributions that are capable to model the skewness parameter in the situations that is needed to model data with tails heavier than the normal. This article intend to present alternatives to the assumption of the normality in the errors, adding asymmetric distributions. A Bayesian approach is proposed to fit nonlinear models when the errors are not normal, thus, the distributions t, skew-normal and skew-t are adopted. The methodology is intended to apply to different growth curves to the quail body weights. It was found that the Gompertz model assuming skew-normal errors and skew-t errors, respectively for male and female, were the best fitted to the data.

Assumir distribuições como a normal nas análises de dados é comum em diferentes áreas do conhecimento. Entretanto, pode-se fazer uso de outras que possuem capacidade de modelar também o parâmetro de assimetria, para as situações em que são necessários modelar dados com caudas mais pesadas que a normal. Este trabalho pretende apresentar alternativas à suposição de normalidade nos erros, dispondo também de distribuições assimétricas. Propõe-se uma abordagem Bayesiana para ajuste de modelos não-lineares quando os erros não são normais. Assim, adotam-se as distribuições t, skewnormal e skew-t. A metodologia visa aplicação em diferentes curvas de crescimento para dados de pesos de codornas. Verifica-se que os modelos de Gompertz com erros skew-normal e skew-t, respectivamente, para machos e fêmeas, são os que melhor se ajustam aos dados.

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Bayesian modeling growth curves for quail assuming skewness in errors - To assume normal distributions in the data analysis is common in different areas of the knowledge. However we can make use of the other distributions that are capable to model the skewness parameter in the situations that is needed to model data with tails heavier than the normal. This article intend to present alternatives to the assumption of the normality in the errors, adding asymmetric distributions. A Bayesian approach is proposed to fit nonlinear models when the errors are not normal, thus, the distributions t, skew-normal and skew-t are adopted. The methodology is intended to apply to different growth curves to the quail body weights. It was found that the Gompertz model assuming skew-normal errors and skew-t errors, respectively for male and female, were the best fitted to the data.

Assumir distribuições como a normal nas análises de dados é comum em diferentes áreas do conhecimento. Entretanto, pode-se fazer uso de outras que possuem capacidade de modelar também o parâmetro de assimetria, para as situações em que são necessários modelar dados com caudas mais pesadas que a normal. Este trabalho pretende apresentar alternativas à suposição de normalidade nos erros, dispondo também de distribuições assimétricas. Propõe-se uma abordagem Bayesiana para ajuste de modelos não-lineares quando os erros não são normais. Assim, adotam-se as distribuições t, skewnormal e skew-t. A metodologia visa aplicação em diferentes curvas de crescimento para dados de pesos de codornas. Verifica-se que os modelos de Gompertz com erros skew-normal e skew-t, respectivamente, para machos e fêmeas, são os que melhor se ajustam aos dados.

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Among the comparative approaches that have been used to understand the patterns of morphologicaldiversification, those related to the detection and evaluation of large-scale evolutionary trends have recentlybeen highlighted. A new method known as the analysis of skewness (ANSKEW) allows partitioning betweenthe passive and driven trends associated with the random occupation of a bounded morphological spaceand a single morphological attractor, respectively. This partitioning provides a better understanding of therelative role of processes that occur at distinct hierarchical levels associated with the macroevolutionarytrends of morphological diversification. In this paper, we used this new approach to understand the patternsof morphological diversification in Erodiscini (Coleoptera, Curculionidae, Otidocephalinae) beetles. Whengenera were used as subclades, ANSKEW revealed that 19.9% of the body size variation in the Erodiscini wasattributable to driven trends, i.e., a morphological attractor, whereas 80.1% of the variation was attributableto the occupation of different adaptive zones by distinct subclades (a passive process), with the passivecomponents being significant (based on 5,000 bootstrap samples). This simple approach to partitioningprovided insights into the intrinsic dynamics of body size evolution in this group without the need to considerexplicit phylogenetic structures. Such analyses could provide a starting point for further evaluation of adaptivevariation at multiple hierarchical levels and of the processes underlying the relationship between variationin body size and other ecological, physiological and behavioral aspects.

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