Multivariate Conway-Maxwell-Poisson Distribution: Sarmanov Method and Doubly Intractable Bayesian Inference

In this article, a multivariate count distribution with Conway-Maxwell (COM)-Poisson marginals is proposed. To do this, we develop a modification of the Sarmanov method for constructing multivariate distributions. Our multivariate COM-Poisson (MultCOMP) model has desirable features such as (i) it admits a flexible covariance matrix allowing for both negative and positive nondiagonal entries;(ii) it overcomes the limitation of the existing bivariate COM-Poisson distributions in the literature that do not have COM-Poisson marginals;(iii) it allows for the analysis of multivariate counts and is not just limited to bivariate counts. Inferential challenges are presented by the likelihood specification as it depends on a number of intractable normalizing constants involving the model parameters. These obstacles motivate us to propose Bayesian inferential approaches where the resulting doubly intractable posterior is handled with via the noisy exchange algorithm or the Grouped Independence Metropolis–Hastings algorithm. Numerical experiments based on simulations are presented to illustrate the proposed Bayesian approach. We demonstrate the potential of the MultCOMP model through a real data application on the numbers of goals scored by the home and away teams in the English Premier League from 2018 to 2021. Here, our interest is to assess the effect of a lack of crowds during the COVID-19 pandemic on the well-known home team advantage. A MultCOMP model fit shows that there is evidence of a decreased number of goals scored by the home team, not accompanied by a reduced score from the opponent. Hence, our analysis suggests a smaller home team advantage in the absence of crowds, which agrees with the opinion of several football experts. Supplementary materials for this article are available online.

Do Innovation Metrics Reflect Sustainable Policy Making in Europe? A Comparative Study Case on the Carpathian and Alpine Mountain Regions

This paper questions the evaluation of innovation systems and innovation measurements and the effectiveness of innovation policies applied at the territorial level by assessing whether the existing European regional scoreboard is effective in providing accurate inputs for decision-makers in mountainous regions. The aim of the research is to provide, through comparative analysis by using statistical multi-methods of two mountainous macro-regions (the Alps and the Carpathians), a possible and available path to develop novel perspectives and alternative views on innovation systems' performance for informed and territorial-based policy making by using the indicators of the Regional Innovation Scoreboard. The methodology used includes descriptive statistics, chi-square bivariate test, Student's t test, one-way ANOVA with Bonferroni post hoc multiple comparisons, multilinear regression analysis, and decision tree with CRT (classification and regression trees) algorithm. Our results emphasize the similarities and differences between the Alpine and Carpathian mountain regions, find the best predictors for each mountain region, and provide a scientific basis for the development of a holistic approach linking measurement theory, innovation systems, innovation policies, and their territorial approach toward sustainable development of mountain areas. The paper's contribution is relevant in the context of remote, rural, and mountain areas, which are usually left behind in terms of innovation chances and in the context of the COVID-19 aftermath with budget constraints. The present results are pertinent for designing effective smart specialization strategies in these regions due to the difficulties that most remote areas and less developed regions are facing in developing innovation policies. © 2023 by the authors.

The Multivariate Skewed Log-Birnbaum–Saunders Distribution and Its Associated Regression Model

In this article, a multivariate extension of the unit-sinh-normal (USHN) distribution is presented. The new distribution, which is obtained from the conditionally specified distributions methodology, is absolutely continuous, and its marginal distributions are univariate USHN. The properties of the multivariate USHN distribution are studied in detail, and statistical inference is carried out from a classical approach using the maximum likelihood method. The new multivariate USHN distribution is suitable for modeling bounded data, especially in the (0,1)p region. In addition, the proposed distribution is extended to the case of the regression model and, for the latter, the Fisher information matrix is derived. The numerical results of a small simulation study and two applications with real data sets allow us to conclude that the proposed distribution, as well as its extension to regression models, are potentially useful to analyze the data of proportions, rates, or indices when modeling them jointly considering different degrees of correlation that may exist in the study variables is of interest.

Reply to Chan, et al.

We appreciate the comments from Chan et al. for our study, and have carefully responded to the comments of Chan et al. and are very grateful for their praise of our research. We agree that smoking might be a risk factor of the severity of COVID-19 as mentioned by Chan et al., but in our study, smoking was not so robust compared with our conclusion. Also, we strongly agreed with the opinion of Chan, et al. that COVID-19 patients with diabetes or other chronic diseases might worsen the situation of the disease. But these factors were out of the scope of our study and we had published other research on this topic related to diabetes. Because of the limited sample size and original medical records, our study could not cover many factors suggested as Chan, et al. But we wish our study will be a useful and meaningful pilot study for the future studies. This article is protected by copyright. All rights reserved.

Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems

Understanding real-world dynamical phenomena remains a challenging task. Across various scientific disciplines, machine learning has advanced as the go-to technology to analyze nonlinear dynamical systems, identify patterns in big data, and make decision around them. Neural networks are now consistently used as universal function approximators for data with underlying mechanisms that are incompletely understood or exceedingly complex. However, neural networks alone ignore the fundamental laws of physics and often fail to make plausible predictions. Here we integrate data, physics, and uncertainties by combining neural networks, physics informed modeling, and Bayesian inference to improve the predictive potential of traditional neural network models. We embed the physical model of a damped harmonic oscillator into a fully-connected feed-forward neural network to explore a simple and illustrative model system, the outbreak dynamics of COVID-19. Our Physics Informed Neural Networks seamlessly integrate data and physics, robustly solve forward and inverse problems, and perform well for both interpolation and extrapolation, even for a small amount of noisy and incomplete data. At only minor additional cost, they self-adaptively learn the weighting between data and physics. They can serve as priors in a Bayesian Inference, and provide credible intervals for uncertainty quantification. Our study reveals the inherent advantages and disadvantages of Neural Networks, Bayesian Inference, and a combination of both and provides valuable guidelines for model selection. While we have only demonstrated these different approaches for the simple model problem of a seasonal endemic infectious disease, we anticipate that the underlying concepts and trends generalize to more complex disease conditions and, more broadly, to a wide variety of nonlinear dynamical systems.

Likelihood-Free Dynamical Survival Analysis applied to the COVID-19 epidemic in Ohio.

The Dynamical Survival Analysis (DSA) is a framework for modeling epidemics based on mean field dynamics applied to individual (agent) level history of infection and recovery. Recently, the Dynamical Survival Analysis (DSA) method has been shown to be an effective tool in analyzing complex non-Markovian epidemic processes that are otherwise difficult to handle using standard methods. One of the advantages of Dynamical Survival Analysis (DSA) is its representation of typical epidemic data in a simple although not explicit form that involves solutions of certain differential equations. In this work we describe how a complex non-Markovian Dynamical Survival Analysis (DSA) model may be applied to a specific data set with the help of appropriate numerical and statistical schemes. The ideas are illustrated with a data example of the COVID-19 epidemic in Ohio.

Cross Sectional Survey on Work Life Balance among Indian Nurses during Second Wave of Covid–19 Pandemic

Background of the study: Nurses are challenged by work and family commitment at the end of each day during the periods of covid - 19 pandemics. In India majority nurses are working throughout the week and striving hard for achieving the balance between their work and professional life. Objectives: To examine the work life balance among Indian Nurses during Second Wave of Covid - 19 Pandemic. Methodology: Research design proposed for the study is 'Descriptive' type, a cross sectional research method. Settings of the study includes Central government hospitals, state government hospitals and NABH accredited private hospitals. Primary data was collected from the staff nurses and nursing officers through structured work life balance questionnaire (google form). Sampling methods adopted for this study was non - probability Convenience sampling technique. Data collection period was March 2021. Data analysis was done through descriptive and inferential statistics. Results: In this study majority of the nurses 55(61.8%) had either positive nor negative work life balance., others 34(38.2%) were with positive work life balance. Conclusion: Based on the results of this study we concluded, that nurses in India are physically, mentally, socially and functionally strong enough to adjust their work life balance particularly during the second wave of covid - 19 in India, those experiences gained by them during the first wave of Covid - 19 equipped them to strengthen their work life balance.

Public Debt and Economic Growth in India: The New Evidence

The study gives new evidence on the effects of public debt on economic growth in India with key macroeconomic indicators from 1980 to 2019. In the past decade, and after the COVID-19 pandemic, there is a substantial rise in public debt, which reached 90% of the GDP in April 2021. Therefore, it is imperative to study the impact of different public debt sources on the Indian economy to help policymakers frame informed debt management policies. The long-run equilibrium relationship and cointegrating coefficients are calculated using Johansen cointegration and fully modified ordinary least square techniques. Toda and Yamamoto's (1995) Granger causality test is used as a short-run diagnostic test for the long-run equilibrium relationship. The study's major findings suggest that domestic debt, total factor productivity (TFP) and exports are the major determinants of economic development in the long run. In contrast, economic prosperity determines the growth of external debt, debt service payments and TFP in the short run. It is recommended that the government should control and channel public debt productively for favourable growth effects.

Food import demand with structural breaks, economic embargo and the COVID-19 pandemic in a wealthy, highly import-dependent country

PurposeQatar, a wealthy country with an open economy has limited arable land. To meet its domestic food demand, the country heavily relies on food imports. Additionally, the over three year-long economic embargo enforced by regional neighbors and the covariate shock of the COVID-19 pandemic have demonstrated the country's vulnerability to food insecurity and potential for structural breaks in macroeconomic data. The purpose of this paper is to examine short- and long-run determinants of Qatar's imports of aggregate food, meats, dairy and cereals in the presence of structural breaks.Design/methodology/approachThe authors use 24 years of food imports, gross domestic product (GDP) and consumer price index (CPI) data obtained from Qatar's Planning and Statistics Authority. They use the autoregressive distributed lag (ARDL) cointegration framework and Chambers and Pope's exact nonlinear aggregation approach.FindingsUnit root tests in the presence of structural breaks reveal a mixture of I (1) and I (0) variables for which standard cointegration techniques do not apply. The authors found evidence of a significant long-run relationship between structural changes and food imports in Qatar. Impulse response functions indicate full adjustments within three-quarters of a year in the event of an exogenous shock to imports.Research limitations/implicationsAn exogenous shock of one standard deviation on this variable would reduce Qatar's food imports by about 2.5% during the first period but recover after the third period.Originality/valueThe failure of past aggregate food demand studies to go beyond standard unit root testing creates considerable doubt about the accuracy of their elasticity estimates. The authors avoid that to provide more credible findings.

Likelihood Ratio Tests for Lorenz Dominance

In testing hypotheses pertaining to Lorenz dominance (LD), researchers have examined second- and third-order stochastic dominance using empirical Lorenz processes and integrated stochastic processes with the aid of bootstrap analysis. Among these topics, analysis of third-order stochastic dominance (TSD) based on the notion of risk aversion has been examined using crossing (generalized) Lorenz curves. These facts motivated the present study to characterize distribution pairs displaying the TSD without second-order (generalized Lorenz) dominance. It further motivated the development of likelihood ratio (LR) goodness-of-fit tests for examining the respective hypotheses of the LD, crossing (generalized) Lorenz curves, and TSD through approximate Chi-squared distributions. The proposed LR tests were assessed using simulated distributions, and applied to examine the COVID-19 regional death counts of bivariate samples collected by the World Health Organization between March 2020 and February 2021.

To curb research misreporting, replace significance and confidence by compatibility: A Preventive Medicine Golden Jubilee article.

It is well known that the statistical analyses in health-science and medical journals are frequently misleading or even wrong. Despite many decades of reform efforts by hundreds of scientists and statisticians, attempts to fix the problem by avoiding obvious error and encouraging good practice have not altered this basic situation. Statistical teaching and reporting remain mired in damaging yet editorially enforced jargon of "significance", "confidence", and imbalanced focus on null (no-effect or "nil") hypotheses, leading to flawed attempts to simplify descriptions of results in ordinary terms. A positive development amidst all this has been the introduction of interval estimates alongside or in place of significance tests and P-values, but intervals have been beset by similar misinterpretations. Attempts to remedy this situation by calling for replacement of traditional statistics with competitors (such as pure-likelihood or Bayesian methods) have had little impact. Thus, rather than ban or replace P-values or confidence intervals, we propose to replace traditional jargon with more accurate and modest ordinary-language labels that describe these statistics as measures of compatibility between data and hypotheses or models, which have long been in use in the statistical modeling literature. Such descriptions emphasize the full range of possibilities compatible with observations. Additionally, a simple transform of the P-value called the surprisal or S-value provides a sense of how much or how little information the data supply against those possibilities. We illustrate these reforms using some examples from a highly charged topic: trials of ivermectin treatment for Covid-19.

Characterization of cereal consumption in Cotonou, Benin

The cereal group occupies a prominent place in the dietary habits of the populations of southern Benin and there are few recent consumption data on cereals. This study aims to assess the consumption, acquisition and supply of cereals to households in Cotonou. A semi-directive survey with KoB°Collect was conducted with 345 households to collect individual cereal food consumption data. The survey data studied by inferential statistics showed that the most consumed cereals are corn (99%, p=l), rice (85%, p=0.936), wheat (35%, p=0.999), sorghum (15%, p=0.659), millet (10%, p=0.971) and fonio at less than 5%. The most common mode of acquisition is buying from secondary market (95%, p=0.987) and street (85%, p=0.999) retailers. The most used preservation techniques are: drying at room temperature (70%, p=0.619) and keeping the product away from light (30%, p=0.806). Households most often dry in areas laid out at home (70%, p=0.984) or at the edge of the road (30%, p=0.939). Storage places are very diverse: the kitchen (45%, p=0.871), the bedroom (40%, 0.998), the living room (25%, p=0.900) and the store (20, 0.931). In addition, the supply costs of cereals increased from 0.009 USD to 0.056 USD between 2020 and 2021. This vertiginous rise in prices is due to the covid19 pandemic. The various data emitted make it possible not only to have fresh data but also to invest them in the assessment of health risks for the achievement of a high level of protection of the health and life of consumers.

Statistical Inference to the Parameter of the Inverse Power Ishita Distribution under Progressive Type-II Censored Data with Application to COVID-19 Data

The goal of the article is the inference about the parameters of the inverse power ishita distribution (IPID) using progressively type-II censored (Prog–II–C) samples. For IPID parameters, maximum likelihood and Bayesian estimates were obtained. Two bootstrap “confidence intervals” (CIs) are also proposed in addition to “approximate confidence intervals” (ACIs). In addition, Bayesian estimates for “squared error loss” (SEL) and LINEX loss functions are provided. The Gibbs within Metropolis–Hasting samplers process is used to provide Bayes estimators of unknown parameters also “credible intervals” (CRIs) of them by using the “Markov Chain Monte Carlo” (MCMC) technique. Then, an application of the suggested approaches is considered a set of real-life data this data set COVID-19 data from France of 51 days recorded from 1 January to 20 February 2021 formed of mortality rate. To evaluate the quality of the proposed estimators, a simulation study is conducted.

A new one-parameter discrete exponential distribution: Properties, inference, and applications to COVID-19 data

A new one-parameter discrete length-biased exponential distribution called the discrete moment exponential (DMEx) distribution is introduced using the survival discretizing approach. We derive the reliability measures including survival function, hazard function, residual reliability function, and the second rate of failure function. Further, the mathematical properties of the DMEx distribution are derived. The parameters of the DMEx distribution are estimated using seven estimation methods. A simulation study is carried out to explore the behavior of the proposed estimators. It is observed that the maximum likelihood approach provides efficient estimates. Finally, the DMEx is adopted for fitting the number of COVID-19 deaths in China and Europe countries. It is shown that the DMEx distribution fits the data better than other competing discrete distributions.

Measuring Basic Reproduction Number to Assess Effects of Nonpharmaceutical Interventions on Nosocomial SARS-CoV-2 Transmission.

Outbreaks of SARS-CoV-2 infection frequently occur in hospitals. Preventing nosocomial infection requires insight into hospital transmission. However, estimates of the basic reproduction number (R0) in care facilities are lacking. Analyzing a closely monitored SARS-CoV-2 outbreak in a hospital in early 2020, we estimated the patient-to-patient transmission rate and R0. We developed a model for SARS-CoV-2 nosocomial transmission that accounts for stochastic effects and undetected infections and fit it to patient test results. The model formalizes changes in testing capacity over time, and accounts for evolving PCR sensitivity at different stages of infection. R0 estimates varied considerably across wards, ranging from 3 to 15 in different wards. During the outbreak, the hospital introduced a contact precautions policy. Our results strongly support a reduction in the hospital-level R0 after this policy was implemented, from 8.7 to 1.3, corresponding to a policy efficacy of 85% and demonstrating the effectiveness of nonpharmaceutical interventions.

Truncated Cauchy Power Weibull-G Class of Distributions: Bayesian and Non-Bayesian Inference Modelling for COVID-19 and Carbon Fiber Data

The Truncated Cauchy Power Weibull-G class is presented as a new family of distributions. Unique models for this family are presented in this paper. The statistical aspects of the family are explored, including the expansion of the density function, moments, incomplete moments (IMOs), residual life and reversed residual life functions, and entropy. The maximum likelihood (ML) and Bayesian estimations are developed based on the Type-II censored sample. The properties of Bayes estimators of the parameters are studied under different loss functions (squared error loss function and LINEX loss function). To create Markov-chain Monte Carlo samples from the posterior density, the Metropolis–Hasting technique was used with posterior density. Using non-informative and informative priors, a full simulation technique was carried out. The maximum likelihood estimator was compared to the Bayesian estimators using Monte Carlo simulation. To compare the performances of the suggested estimators, a simulation study was carried out. Real-world data sets, such as strength measured in GPA for single carbon fibers and impregnated 1000-carbon fiber tows, maximum stress per cycle at 31,000 psi, and COVID-19 data were used to demonstrate the relevance and flexibility of the suggested method. The suggested models are then compared to comparable models such as the Marshall–Olkin alpha power exponential, the extended odd Weibull exponential, the Weibull–Rayleigh, the Weibull–Lomax, and the exponential Lomax distributions.

COVID-19 Lockdown Unravels the Complex Interplay between Environmental Conditions and Human Activity

During the COVID-19 epidemic, draconian countermeasures forbidding nonessential human activities have been adopted in several countries worldwide, providing an unprecedented setup for testing and quantifying the current impact of humankind on climate and for driving potential sustainability policies in the postpandemic era from a perspective of complex systems. In this study, we consider heterogeneous sources of environmental and human activity observables, considered as components of a complex socioenvironmental system, and apply information theory, network science, and Bayesian inference to analyze their structural relations and nonlinear dynamics between January 2019 and August 2020 in northern Italy, i.e., before, during, and after the national lockdown. The topological structure of a complex system strongly impacts its collective behavior;therefore, mapping this structure is essential to fully understand the functions of the system as a whole and its fragility to unexpected disruptions or shocks. To this aim, we unravel the causal relationships between the 16 environmental conditions and human activity variables, mapping the backbone of the complex interplay between intervening physical observables—such as NO2 emissions, energy consumption, intervening climate variables, and different flavors of human mobility flows—to a causal network model. To identify a tipping point during the period of observation, denoting the presence of a regime shift between distinct network states (i.e., before and during the shock), we introduce a novel information-theoretic method based on statistical divergence widely used in statistical physics. We find that despite a measurable decrease in NO2 concentration, due to an overall decrease in human activities, locking down a region as a climate change mitigation is an insufficient remedy to reduce emissions. Our results provide a functional characterization of socioenvironmental interdependent systems, and our analytical framework can be used, more generally, to characterize environmental changes and their interdependencies using statistical physics.

ESTUDIO ESTADÍSTICO SOBRE LA RELACIÓN ENTRE LAS HABILIDADES DIGITALES E INVESTIGATIVAS EN ESTUDIANTES DE PEDAGOGÍA DE LA UNIVERSIDAD NACIONAL DE HUANCAVELICA-PERU

This paper contains the results of an investigation carried out in the National University of Huancavelica, Peru, where the authors intended to determine how both digital and investigative skills are related each other in students of magisterial formation of this university in times of COVID-19. In moments where the in-person classes do not exist due to this pandemic and when this situation has been extended in the time for more than one year, it is necessary that university students in general and in particular those that have magisterial formation in this university, to have the enough abilities in the use of the new technologies of internet and also the basic research abilities that is part of what is requested in a competent professional of the pedagogic careers. Therefore, the authors use tools of the statistical inference to carry out this study. The obtained results are important for the managers, professors and students of the university, because it will allow us identifying the strengths and weaknesses with which the students of these disciplines count in these aspects. Thus, the managers will be able to trace politics to reinforce the strengths and to rectify the weaknesses, with the purpose of improving the educational work in this university. To the authors' knowledge this is the first time that it is carried out a study of this type in this center of higher education under these circumstances. © 2022 Universidad de La Habana. All rights reserved.

Tze Leung Lai and Anna Choi's contribution to the discussion of 'Testing by betting: A strategy for statistical and scientific communication' by Glenn Shafer

Comments on an article by Glenn Shafer (see record 2021-44219-001). It is exciting to follow Glenn Shafer's investigations into forecasting, betting, reasoning with uncertainty and foundational issues in probability, beginning with his 1973 PhD thesis at Princeton and culminating in Shafer on the Dempster-Shafer theory of belief functions, and its evolution during the past five decades to the present paper on betting scores and game-theoretic probability. Betting scores are particularly relevant in this momentous year of intensive global search for COVID19 vaccines and treatments, and upcoming presidential and congressional elections in the United States, about which pundits keep giving time-varying forecasts of the outcomes while betting markets on presidential election odds have been particularly active, similar to online sports betting markets. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Estimating the generation interval and inferring the latent period of COVID-19 from the contact tracing data.

The coronavirus disease 2019 (COVID-19) emerged by end of 2019, and became a serious public health threat globally in less than half a year. The generation interval and latent period, though both are of importance in understanding the features of COVID-19 transmission, are difficult to observe, and thus they can rarely be learnt from surveillance data empirically. In this study, we develop a likelihood framework to estimate the generation interval and incubation period simultaneously by using the contact tracing data of COVID-19 cases, and infer the pre-symptomatic transmission proportion and latent period thereafter. We estimate the mean of incubation period at 6.8 days (95 %CI: 6.2, 7.5) and SD at 4.1 days (95 %CI: 3.7, 4.8), and the mean of generation interval at 6.7 days (95 %CI: 5.4, 7.6) and SD at 1.8 days (95 %CI: 0.3, 3.8). The basic reproduction number is estimated ranging from 1.9 to 3.6, and there are 49.8 % (95 %CI: 33.3, 71.5) of the secondary COVID-19 infections likely due to pre-symptomatic transmission. Using the best estimates of model parameters, we further infer the mean latent period at 3.3 days (95 %CI: 0.2, 7.9). Our findings highlight the importance of both isolation for symptomatic cases, and for the pre-symptomatic and asymptomatic cases.