Flexible Birnbaum-Saunders distribution

In this paper, we propose a bimodal extension of the Birnbaum–Saunders model by including an extra parameter. This new model is termed flexible Birnbaum–Saunders (FBS) and includes the ordinary Birnbaum–Saunders (BS) and the skew Birnbaum–Saunders (SBS) model as special cases. Its properties are studied. Parameter estimation is considered via an iterative maximum likelihood approach. Two real applications, of interest in environmental sciences, are included, which reveal that our proposal can perform better than other competing models.Ministerio de Economía y Competitividad (MINECO). Españ

Generalized modified slash Birnbaum–Saunders distribution

In this paper, a generalization of the modified slash Birnbaum-Saunders (BS) distribution is introduced. The model is defined by using the stochastic representation of the BS distribution, where the standard normal distribution is replaced by a symmetric distribution proposed by Reyes et al. It is proved that this new distribution is able to model more kurtosis than other extensions of BS previously proposed in the literature. Closed expressions are given for the pdf (probability density functio), along with their moments, skewness and kurtosis coefficients. Inference carried out is based on modified moments method and maximum likelihood (ML). To obtain ML estimates, two approaches are considered: Newton-Raphson and EM-algorithm. Applications reveal that it has potential for doing well in real problems

Scale Mixture of Rayleigh Distribution

In this paper, the scale mixture of Rayleigh (SMR) distribution is introduced. It is proven that this new model, initially defined as the quotient of two independent random variables, can be expressed as a scale mixture of a Rayleigh and a particular Generalized Gamma distribution. Closed expressions are obtained for its pdf, cdf, moments, asymmetry and kurtosis coefficients. Its lifetime analysis, properties and Rényi entropy are studied. Inference based on moments and maximum likelihood (ML) is proposed. An Expectation-Maximization (EM) algorithm is implemented to estimate the parameters via ML. This algorithm is also used in a simulation study, which illustrates the good performance of our proposal. Two real datasets are considered in which it is shown that the SMR model provides a good fit and it is more flexible, especially as for kurtosis, than other competitor models, such as the slashed Rayleigh distribution

Extended Half-Power Exponential Distribution with Applications to COVID-19 Data

In this paper, the Extended Half-Power Exponential (EHPE) distribution is built on the basis of the Power Exponential model. The properties of the EHPE model are discussed: the cumulative distribution function, the hazard function, moments, and the skewness and kurtosis coefficients. Estimation is carried out by applying maximum likelihood (ML) methods. A Monte Carlo simulation study is carried out to assess the performance of ML estimates. To illustrate the usefulness and applicability of EHPE distribution, two real applications to COVID-19 data in Chile are discussed

Statistical Inference for a General Family of Modified Exponentiated Distributions

In this paper, a modified exponentiated family of distributions is introduced. The new model was built from a continuous parent cumulative distribution function and depends on a shape parameter. Its most relevant characteristics have been obtained: the probability density function, quantile function, moments, stochastic ordering, Poisson mixture with our proposal as the mixing distribution, order statistics, tail behavior and estimates of parameters. We highlight the particular model based on the classical exponential distribution, which is an alternative to the exponentiated exponential, gamma and Weibull. A simulation study and a real application are presented. It is shown that the proposed family of distributions is of interest to applied areas, such as economics, reliability and finances

Brain and immune system-derived extracellular vesicles mediate regulation of complement system, extracellular matrix remodeling, brain repair and antigen tolerance in multiple sclerosis

Multiple sclerosis (MS) is an immune-mediated central nervous system disease whose course is unpredictable. Finding biomarkers that help to better comprehend the disease’s pathogenesis is crucial for supporting clinical decision-making. Blood extracellular vesicles (EVs) are membrane-bound particles secreted by all cell types that contain information on the disease’s pathological processes. Purpose: To identify the immune and nervous system-derived EV profile from blood that could have a specific role as biomarker in MS and assess its possible correlation with disease state. Results: Higher levels of T cell-derived EVs and smaller size of neuron-derived EVs were associated with clinical relapse. The smaller size of the oligodendrocyte-derived EVs was related with motor and cognitive impairment. The proteomic analysis identified mannose-binding lectin serine protease 1 and complement factor H from immune system cell-derived EVs as autoimmune disease-associated proteins. We observed hepatocyte growth factor-like protein in EVs from T cells and inter-alpha-trypsin inhibitor heavy chain 2 from neurons as white matter injury-related proteins. In patients with MS, a specific protein profile was found in the EVs, higher levels of alpha-1-microglobulin and fibrinogen β chain, lower levels of C1S and gelsolin in the immune system-released vesicles, and Talin-1 overexpression in oligodendrocyte EVs. These specific MS-associated proteins, as well as myelin basic protein in oligodendrocyte EVs, correlated with disease activity in the patients with MS. Conclusion: Neural-derived and immune-derived EVs found in blood appear to be good specific biomarkers in MSfor reflecting the disease stateWe greatly appreciate the support of Morote Traducciones S.L. for their editing assistance. This work was sponsored by a grant from Miguel Servet (CP20/00024 to Laura Otero-Ortega), Miguel Servet (CPII20/00002 to María Guti´errez-Fernández), a predoctoral fellowship (FI18/00026 to Fernando Laso-García), a Río-Hortega grant (CM22/00065 to Gabriel Torres Iglesias and CM20/00047 to Elisa Alonso-Lopez) and byResearch Project (PI21/00918) from the Instituto de Salud Carlos III and co-funded by the European Union and by a grant CA1/RSUE/2021-00753 to Dolores Piniella funded by Ministerio de Universidades, Plan de Recuperacion, ´ Transformacion ´ y Resiliencia y la Universidad Autónoma de Madri

Brain and immune system-derived extracellular vesicles mediate regulation of complement system, extracellular matrix remodeling, brain repair and antigen tolerance in Multiple sclerosis

Background: Multiple sclerosis (MS) is an immune-mediated central nervous system disease whose course is unpredictable. Finding biomarkers that help to better comprehend the disease's pathogenesis is crucial for supporting clinical decision-making. Blood extracellular vesicles (EVs) are membrane-bound particles secreted by all cell types that contain information on the disease's pathological processes. Purpose: To identify the immune and nervous system-derived EV profile from blood that could have a specific role as biomarker in MS and assess its possible correlation with disease state. Results: Higher levels of T cell-derived EVs and smaller size of neuron-derived EVs were associated with clinical relapse. The smaller size of the oligodendrocyte-derived EVs was related with motor and cognitive impairment. The proteomic analysis identified mannose-binding lectin serine protease 1 and complement factor H from immune system cell-derived EVs as autoimmune disease-associated proteins. We observed hepatocyte growth factor-like protein in EVs from T cells and inter-alpha-trypsin inhibitor heavy chain 2 from neurons as white matter injury-related proteins. In patients with MS, a specific protein profile was found in the EVs, higher levels of alpha-1-microglobulin and fibrinogen β chain, lower levels of C1S and gelsolin in the immune system-released vesicles, and Talin-1 overexpression in oligodendrocyte EVs. These specific MS-associated proteins, as well as myelin basic protein in oligodendrocyte EVs, correlated with disease activity in the patients with MS. Conclusion: Neural-derived and immune-derived EVs found in blood appear to be good specific biomarkers in MS for reflecting the disease state.This work was sponsored by a grant from Miguel Servet (CP20/00024 to Laura Otero-Ortega), Miguel Servet (CPII20/00002 to María Gutiérrez-Fernández), a predoctoral fellowship (FI18/00026 to Fernando Laso-García), a Río-Hortega grant (CM22/00065 to Gabriel Torres Iglesias and CM20/00047 to Elisa Alonso-López) and by Research Project (PI21/00918) from the Instituto de Salud Carlos III and co-funded by the European Union and by a grant CA1/RSUE/2021-00753 to Dolores Piniella funded by Ministerio de Universidades, Plan de Recuperación, Transformación y Resiliencia y la Universidad Autónoma de Madrid.S

Techniques to Deal with Off-Diagonal Elements in Confusion Matrices

Confusion matrices are numerical structures that deal with the distribution of errors between different classes or categories in a classification process. From a quality perspective, it is of interest to know if the confusion between the true class A and the class labelled as B is not the same as the confusion between the true class B and the class labelled as A. Otherwise, a problem with the classifier, or of identifiability between classes, may exist. In this paper two statistical methods are considered to deal with this issue. Both of them focus on the study of the off-diagonal cells in confusion matrices. First, McNemar-type tests to test the marginal homogeneity are considered, which must be followed from a one versus all study for every pair of categories. Second, a Bayesian proposal based on the Dirichlet distribution is introduced. This allows us to assess the probabilities of misclassification in a confusion matrix. Three applications, including a set of omic data, have been carried out by using the software R

Techniques to Deal with Off-Diagonal Elements in Confusion Matrices

Confusion matrices are numerical structures that deal with the distribution of errors between different classes or categories in a classification process. From a quality perspective, it is of interest to know if the confusion between the true class A and the class labelled as B is not the same as the confusion between the true class B and the class labelled as A. Otherwise, a problem with the classifier, or of identifiability between classes, may exist. In this paper two statistical methods are considered to deal with this issue. Both of them focus on the study of the off-diagonal cells in confusion matrices. First, McNemar-type tests to test the marginal homogeneity are considered, which must be followed from a one versus all study for every pair of categories. Second, a Bayesian proposal based on the Dirichlet distribution is introduced. This allows us to assess the probabilities of misclassification in a confusion matrix. Three applications, including a set of omic data, have been carried out by using the software R

Flexible Log-Linear Birnbaum–Saunders Model

Rieck and Nedelman (1991) introduced the sinh-normal distribution. This model was built as a transformation of a N(0,1) distribution. In this paper, a generalization based on a flexible skew normal distribution is introduced. In this way, a more general model is obtained that can describe a range of asymmetric, unimodal and bimodal situations. The paper is divided into two parts. First, the properties of this new model, called flexible sinh-normal distribution, are obtained. In the second part, the flexible sinh-normal distribution is related to flexible Birnbaum–Saunders, introduced by Martínez-Flórez et al. (2019), to propose a log-linear model for lifetime data. Applications to real datasets are included to illustrate our findings