Abstract: The objective of this paper is to estimate the unknown parameter for inverted Topp – Leone (ITL) distribution using the E-Bayesian estimation method under type-II censored sampling. The squared error loss function and the LINEX loss function are used to estimate the unknown parameter. The effectiveness of the E-Bayesian estimators in comparison to the Bayesian and maximum likelihood estimators based on mean square error is investigated using a Monte Carlo simulation. PubDate: Tue, 20 Dec 2022 03:36:34 +000

Abstract: A similarity and a difference between bivariate negative binomial distribution and bivariate geometric distribution is presented. The distribution of negative binomial difference and geometric difference and the corresponding characteristic function are presented. PubDate: Tue, 20 Dec 2022 03:36:18 +000

Abstract: The class of bivariate integer-valued time series models, described via copula theory, is gaining popularity in the literature because of applications in health sciences, engineering, financial management and more. Each time series follows a Markov chain with the serial dependence captured using copula-based distribution functions from the Poisson and the zero-inflated Poisson margins. The copula theory is again used to capture the dependence between the two series.However, the efficiency and adaptability of the copula are being challenged because of the discrete nature of data and also in the case of zero-inflation of count time series. Likelihood-based inference is used to estimate the model parameters for simulated and real data with the bivariate integral of copula functions. While such copula functions offer great flexibility in capturing dependence, there remain challenges related to identifying the best copula type for a given application. This paper presents a survey of the literature on bivariate copula for discrete data with an emphasis on the zero-inflated nature of the modelling. We demonstrate additional experiments on to confirm that the copula has potential as greater research area. PubDate: Wed, 26 Oct 2022 03:53:04 +000

Abstract: Throughout the history of our country, different policies have left an incentive for favorable changes, however, none by itself has managed to combat the problems of chronic malnutrition, to which the current pandemic is added. The state of Hidalgo is in a nutritional transition, with persistent child undernutrition and the predominance of chronic diseases associated with malnutrition (undernutrition, overweight and obesity). Part of this research aims to contribute (in a second phase) to the adequacy of current public policy in the fight against malnutrition and, of course, to the current needs experienced by the SARS-CoV-2 infection contingency. This work develops the application of simple sampling and the stages involved in this statistical tool, whose objective is to establish which part of the reality under study should be studied in order to make inferences about a given population. From the period contemplated between April 28, 2020 and March 8, 2022, the 84 municipalities of the state of Hidalgo reported a total of 86,124 confirmed cases of SARS-CoV-2 infection, from which a sample size of 1,054 subjects has been calculated (representativeness of 91.35% of the target population). The correct application of mathematics in the context of health should allow us to enjoy good health, especially if these results are focused on the promotion and prevention of diseases and their complications; mathematics has surpassed the frontiers of knowledge in various areas and its implementation in this case with respect to public policy and nutrition. PubDate: Wed, 26 Oct 2022 03:48:26 +000

Abstract: Mean plausible values can be computed when Bayesian structural equation modeling (BSEM) is performed. As mean plausible values do not preserve the factor inter-correlations, they yield path coefficients that are different from the estimated path coefficients of the model. As it might be of interest to perform exactly the same predictions on the level of mean plausible values that have been estimated by BSEM, correlation-preserving mean plausible values were proposed. An example for the computation of the correlation preserving mean plausible values is given and the corresponding syntax can be found in the Appendix. PubDate: Wed, 26 Oct 2022 03:26:09 +000

Abstract: Research results on the same subject, extracted from scientific papers or clinical trials, are combined to determine a consensus. We are primarily concerned with combining p-values from experiments that may be correlated. We have two methods, a non-Bayesian method and a Bayesian method. We use a model to combine these results and assume the combined results follow a certain distribution, for example, chi-square or normal. The distribution requires independent and identically distributed (iid) random variables. When the data are correlated or non-iid, we cannot assume such distribution. In order to do so, the combined results from the model need to be adjusted, and the adjustment is done “indirectly” through two test statistics. Specifically, one test statistic (TS** ) is obtained for the non-iid data and the other is the test statistic (TS) is obtained for iid data. We use the ratio between the two test statistics to adjust the model test statistic (TS**) for its non-iid violation. The adjusted TS** is named as “effective test statistics” (ETS), which is then used for statistical inferences with the assumed distribution. As it is difficult to estimate the correlation, to provide a more coherent method for combining p-values, we also introduce a novel Bayesian method for both iid data and non-iid data. The examples are used to illustrate the non-Bayesian method and additional examples are given to illustrate the Bayesian method. PubDate: Fri, 21 Oct 2022 04:31:03 +000