Francisco Novoa-Muñoz

The likelihood equations arising from a progressively Type II censored sample drawn from a Type I Generalized Logistic Distribution do not yield closed-form solutions for the scale and shape parameters. To address this, we derive maximum likelihood estimators of the unknown parameters by means of the Expectation–Maximization (EM) algorithm. The expected Fisher information matrix is obtained using the missing information principle, allowing for the computation of asymptotic standard errors. A simulation study is presented to illustrate the performance and practical implementation of the proposed inferential procedures.

The Poisson–Three-Parameter Lindley (PTPL) distribution constitutes a flexible Poisson mixture model for overdispersed count data, encompassing several classical count distributions as special or limiting cases. Despite its growing use in applied contexts, no formal goodness-of-fit test specifically designed for this distribution is currently available. In this paper, we propose and study a new goodness-of-fit test for the PTPL model based on a Cramér-von Mises type distance between the empirical and theoretical probability generating functions (PGFs). For polynomial weight functions, the test statistic admits an explicit closed-form representation; in practice, it is computed efficiently via numerical quadrature. The null distribution of the statistic is approximated via parametric bootstrap. We establish theoretical properties of the proposed procedure, including consistency against fixed alternatives and the validity of the bootstrap approximation. Monte Carlo simulations with sample sizes n ∈ {50,100,150,200,500} for size evaluation and n = 250 for power comparisons, and weight exponents a ∈ {0,1,2}, show that the empirical size is well controlled at both the 5% and 10% nominal levels, and that the test exhibits competitive power against Poisson, Negative Binomial, COM-Poisson, and Zero-Inflated Poisson alternatives. Areal data application to five overdispersed count datasets further illustrates the practical utility of the method.

Environmental tobacco smoke (ETS) remains a significant public health concern in Chile despite the implementation of comprehensive smoke-free policies. This study aimed to identify sociodemographic, behavioral, and biochemical factors associated with daily ETS exposure across different environments among Chilean adults. A cross-sectional analysis was conducted using data from the 2016–2017 Chilean National Health Survey (ENS), a nationally representative survey with a complex multistage sampling design. The analytical sample included 6,233 individuals aged 15 years or older, of whom 5,520 had biochemical measurements; missing data were addressed using multiple imputation. Six outcome variables representing daily hours (0–24) of ETS exposure were analyzed across home, workplace, and other environments on weekdays and weekends. Given the high proportion of zero values (~88.5%) and evidence of overdispersion (dispersion = 6.2), Poisson, negative binomial, and zero-inflated Poisson models were formally compared using likelihood-based criteria, while quasi-Poisson models were used exclusively to assess overdispersion and the robustness of standard errors. Survey-weighted generalized linear models were fitted, and exponentiated coefficients were interpreted as mean ratios (MR). Negative Binomial models provided the best fit (AIC = 6,300 vs. 11,470 for Poisson), with consistent findings across sensitivity analyses. ETS exposure was higher among men, urban residents, and individuals reporting insomnia. Age was inversely associated with exposure (MR ≈ 0.99 per year), indicating lower expected exposure hours among older individuals. Women showed lower exposure compared to men (MR = 0.69; 95% CI: 0.55–0.87), and rural residence was associated with reduced exposure (MR = 0.61; 95% CI: 0.45–0.84). Insomnia was associated with higher exposure (MR = 1.74; 95% CI: 1.10–2.73). Descriptive analyses indicated that exposure was highest in “other environments,” particularly during weekends. These findings suggest that ETS exposure in Chile is unevenly distributed and shaped by sociodemographic and behavioral factors. Accounting for complex survey design and distributional characteristics of exposure data improves the validity and interpretability of results. Targeted public health strategies focusing on high-risk groups and environments are needed, while integrating behavioral and contextual determinants into tobacco control policies.

Background/Objectives: Stuttering is a neurodevelopmental disorder of speech fluency. It emerges most commonly between 2 and 5 years old, often causing social exclusion and stigma. In Latin America, cultural misconceptions regarding its causes exacerbate these psychosocial challenges. This study validated a culturally adapted instrument for Chile to measure public attitudes toward stuttering. The instrument provides a psychometrically sound method to assess and address stigma within educational and community settings. Methods: A total of 756 Chilean adults (mean age = 36.7 years, SD = 15.8; 64% women, 36% men) were recruited using stratified probability sampling to reflect the national demographics. Ethical approval and informed consent were obtained. The subsection underwent rigorous cross-cultural adaptation (translation, expert review, cognitive debriefing n = 30, pre-testing n = 50). Analysis employed polychoric matrices, EFA, CFA with WLSMV, and multiple reliability/validity indices. Results: Joint analysis showed poor fit (CFI = 0.72, RMSEA = 0.12), confirming independence. Beliefs (14 items): three-factor CFA fit excellent (CFI = 0.993, RMSEA = 0.034); factors: competence/normality (α = 0.85), psychological causes (α = 0.78), and help/support (α = 0.72). Reactions (11 items): four-factor fit adequate (CFI = 0.97, RMSEA = 0.043); factors: distant concern (α = 0.82), personal concern (α = 0.79), media sources (α = 0.75), and formal sources (α = 0.77). Validity was supported; bifactor models favored multidimensionality. Conclusions: The adapted subsection is psychometrically robust and effectively captures Chilean-specific attitudes toward childhood stuttering. It provides a reliable tool for quantifying public stigma and misconceptions, particularly in educational and school contexts, thereby supporting the design of targeted school-based policies and interventions to reduce bullying, promote inclusion, and safeguard the psychosocial well-being of children and adolescents who stutter.

In this Editorial, we are pleased to introduce the Special Issue of the journal Axioms entitled “Advances in Statistical Simulation and Computing”

El estudio desarrolla un marco conceptual y tecnológico en el Nivel de Madurez Tecnológica, cuya sigla es (TRL) 2 para una plataforma digital integral de aseguramiento de la calidad y acreditación de carreras de pedagogía en Chile. Se sustenta en los lineamientos de la Comisión Nacional de Acreditación (CNA, 2018), la Ley 20.903 y el ciclo de mejora continua (PDCA). Se utilizó un diseño de investigación aplicada y exploratoria-conceptual, con revisión documental y validación de expertos externos. La muestra estuvo conformada por 30 académicos y académicas de seis universidades estatales, quienes evaluaron la pertinencia, factibilidad y consistencia técnica de la propuesta. Los resultados evidencian consenso en la necesidad de una herramienta digital que sistematice evidencias, genere indicadores automáticos y anticipe brechas mediante analítica predictiva. Se concluye que la propuesta cumple los criterios de TRL 2, aportando a la transformación digital y a la equidad territorial en la formación inicial docente, con proyección a TRL 3 mediante un prototipo experimental.

When modeling real-world data, we face the challenge of determining which probability distribution best represents the data. To address this intricate problem, we rely on goodness-of-fit tests. However, when the data come from a bivariate negative binomial distribution, the literature reveals no existing goodness-of-fit test for this distribution. For this reason, in this article, we propose and study a computationally convenient goodness-of-fit test for the bivariate negative binomial distribution. This test is based on a bootstrap approximation and a parallelization strategy. To this end, we use a reparameterization technique based on the probability generating function and a Cramér-von Mises-type statistic. From the simulation studies, we conclude that the results converge to the established nominal levels as the sample size increases, and in all cases considered, the parametric bootstrap method provides an accurate approximation of the null distribution of the statistic we propose. Additionally, we verify the power of the proposed test, as well as its application to five real datasets. To accelerate the massive computational work, we employ the parallelization strategy that, according to Novoa-Muñoz (2024), was the most efficient among the techniques he analyzed.