International journal of statistics and probability最新文献

{"title":"X-exponential-G Family of Distributions With Applications","authors":"Shahid Mohammad","doi":"10.5539/ijsp.v13n1p40","DOIUrl":"https://doi.org/10.5539/ijsp.v13n1p40","url":null,"abstract":"A new family of continuous distributions called the X-exponential-G (XE-G) family is proposed. Explicit expressions are derived for the ordinary and incomplete moments, generating functions, mean deviation about the mean and median, Shannon and R'{e}nyi entropies, and order statistics of this new family. Estimation of the parameters of the new family is done using the method of maximum likelihood. Assessment of the performance of the maximum likelihood estimates is carried out through a simulation study using the quantile function of the XE-G distribution. The usefulness of this new family is illustrated by modeling two real datasets.","PeriodicalId":89781,"journal":{"name":"International journal of statistics and probability","volume":"13 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140413573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparison of Copulas Based on the Range Distribution and Its Application","authors":"A. Nanthakumar","doi":"10.5539/ijsp.v13n1p55","DOIUrl":"https://doi.org/10.5539/ijsp.v13n1p55","url":null,"abstract":"This paper compares some Archimedean Copulas based on the range distribution and its application. We show that the Clayton Copula is better than the other Copula models considered in this paper. Also, Clayton Copula performed better than the regular one-way ANOVA when there were no outliers. However, when the data had outliers, the Clayton Copula performed almost at the same level (power-wise) as the regular one-way ANOVA. \u0000 \u0000 ","PeriodicalId":89781,"journal":{"name":"International journal of statistics and probability","volume":"9 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140411750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparisons of the Satterthwaite Approaches for Fixed Effects in Linear Mixed Models","authors":"Waseem Alnosaier","doi":"10.5539/ijsp.v13n1p22","DOIUrl":"https://doi.org/10.5539/ijsp.v13n1p22","url":null,"abstract":"Four approximate F- tests derived by Fai and Cornelious in 1996 to make inference for fixed effects in mixed linear models of rank greater than one. Two of these approaches derived by introducing a Wald-type statistic distributed approximately as an F distribution, and the denominator degrees of freedom computed by matching the approximated one moment of the Wald-type statistic with the exact one moment of the F distribution. The other two approaches were derived by introducing a scaled Wald-type statistic to be distributed approximately as an F distribution, and the denominator degrees of freedom and the scale factor computed by matching the two moments of the statistic with the moments of the F distribution. This paper proposes two more approximate F-tests analogous to the four approaches where an adjusted estimator of the variance of the estimate of fixed effects used. In addition, the paper evaluates and compares the performance of the six approaches analytically, and some useful results are presented. Also, a simulation study for block designs was run to assess and compare the performance of the approaches based on their observed test levels. The simulation study shows that the approaches usually perform reasonably based on their test levels, and in some cases some approaches found to more adequately than other approaches.","PeriodicalId":89781,"journal":{"name":"International journal of statistics and probability","volume":"14 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140418531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating and Calibrating Markov Chain Sample Error Variance","authors":"Yann Vestring, Javad Tavakoli","doi":"10.5539/ijsp.v13n1p10","DOIUrl":"https://doi.org/10.5539/ijsp.v13n1p10","url":null,"abstract":"Markov chain Monte Carlo (MCMC) methods are a powerful and versatile tool with applications spanning a wide spectrum of fields, including Bayesian inference, computational biology, and physics. One of the key challenges in applying MCMC algorithms is to deal with estimation error. The main result in this article is a closed form, non-asymptotic solution for the sample error variance of a single MCMC  estimate. Importantly, this result assumes that the state-space is finite and discrete. We demonstrate with examples how this result can help estimate and calibrate MCMC estimation error variance in the more general case, when the state-space is continuous and/or unbounded.","PeriodicalId":89781,"journal":{"name":"International journal of statistics and probability","volume":"21 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140419429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Predictive Model to Predict a Cyberattack Using Self Normalizing Neural Networks","authors":"Oluwapelumi Eniodunmo, Raid Al-Aqtash","doi":"10.5539/ijsp.v12n6p60","DOIUrl":"https://doi.org/10.5539/ijsp.v12n6p60","url":null,"abstract":"A cyberattack is an unauthorized access and a threat to information systems. Intelligent intrusion systems rely on advancements in technology to detect cyberattacks. In this article, the KDD CUP 99 dataset, from the Third International Knowledge Discovery and Data mining Tools Competition that was held in 1999, is considered, and a class of neural networks, known as Self-Normalizing Neural Networks, is utilized to build a predictive model to detect cyberattacks in the KDD CUP 99 dataset. The accuracy and the precision of the self-normalizing neural network is compared with that of the k-nearest neighbors and the support vector machines, in addition to other models in literature. The self-normalizing neural network appears to perform better than other models in predicting cyberattacks, while also being efficient in predicting a normal connection.","PeriodicalId":89781,"journal":{"name":"International journal of statistics and probability","volume":" 30","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139144917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integer-Valued First Order Autoregressive (INAR(1)) Model With Negative Binomial (NB) Innovation For The Forecasting Of Time Series Count Data","authors":"Nasiru Mukaila Olakorede, S. Olanrewaju","doi":"10.5539/ijsp.v12n6p23","DOIUrl":"https://doi.org/10.5539/ijsp.v12n6p23","url":null,"abstract":"This paper is about the theoretical investigation of integer-valued first order autoregressive (INAR(1)) model with negative binomial (NB) innovation for the forecasting of time series count data. The study makes use of the Conditional Least squares (CLS) estimator to estimate the parameter of INAR(1) model, and Maximum Likelihood Estimator (MLE) to estimate the mean (μ ) and the dispersion parameter (K) of the NB distribution. A simulation experiment based on theoretical generated data were addressed under different parameter values α =0.2, 0.6, 0.8, different sample sizes n=30, 90, 120, 600 for the class of INAR(1) model, and μ  =0.85, 1.5, 2,  K=1,2, 4 for the NB distribution. The Monte Carlo simulations were conducted with codes written in R, all results were based on 1000 runs. The estimation of parameter for the class of INAR(1) model gives a better result when the number of observations is small and the parameter value is high. The NB estimation gives a better result when the number of observations is small and with large K values. The forecasting accuracy of the model at different lead time period l =1, 3, 5, 7, 9, 15 were investigated with codes written in R. The results showed that the minimum mean square error (MMSE) produced when the number of lead times forecasts is between one and five were less than that produced when the numbers of lead times forecast were greater than five. The MMSE increased when the number of lead time periods increases. This result indicates that forecasting with this class of model is better with short time frame of predictions. The study was applied to the number of deaths arising from COVID-19 in Nigeria which consist of count time series data of 48 observations (weekly data), from January 2021 to December 2021.","PeriodicalId":89781,"journal":{"name":"International journal of statistics and probability","volume":"32 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139153363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bayesian Hidden Markov Modelling of Blood Type Distribution for Covid-19 Cases Using Poisson Distribution","authors":"Johnson Joseph Kwabina Arhinful, Okyere Gabriel Asare, Adebanji Atinuke Olusola, Owusu -Ansah Emmanuel Degraft Johnson, Burnett Tetteh Accam","doi":"10.5539/ijsp.v12n6p34","DOIUrl":"https://doi.org/10.5539/ijsp.v12n6p34","url":null,"abstract":"This paper proposes a model to describe the blood types distribution of new Coronavirus (COVID-19) cases using the Bayesian Poisson - Hidden Markov Model (BP-HMM). With the help of the Gibbs sampler algorithm, using OpenBugs, the study first identifies the number of hidden states fitting European (EU) and African (AF) data sets of COVID-19 cases by blood type frequency. The study then compares the state-dependent mean of infection within and across the two geographical areas. The study findings show that the number of hidden states and infection rate within and across the two geographical areas differ according to blood type.","PeriodicalId":89781,"journal":{"name":"International journal of statistics and probability","volume":"87 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139153502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling Factors that Predict Differences in Childhood Mortality in Lagos Communities Using Prognostic Logistic and Poisson Regression Models","authors":"W. Akanji, R. Kareem, J. A. Akinyemi, M. Ekum","doi":"10.5539/ijsp.v12n6p1","DOIUrl":"https://doi.org/10.5539/ijsp.v12n6p1","url":null,"abstract":"Lagos State is a city with one of the largest concentration of people in the world with heterogenous behaviour and cultural beliefs. There are different prognostic models in the medical sciences, yet their real life application, especially to childhood mortality is limited. There are variations in childhood mortality rate across different communities in Lagos State. Childhood mortality is a subject of interest to World Health Organization (WHO) and one of the major Millennium Development Goals. In 2014, the Special Adviser to the Lagos State Governor on Public Health, Dr. Yewande Adeshina said that under-5 and infant mortality rates in Lagos state have reduced due to various health interventions being implemented in the State. However, the truth of the matter is that childhood mortality is still high and this is an indication that we still have lots of work to do in this regard. In this paper, prognostic models were used in modelling factors that predict the differences in childhood mortality in Lagos communities. Six models, two each from logistic regression, linear regression and Poisson regression models were used. Primary data were collected from mothers that fall in the age bracket (15-49), who reside in any of the 5 divisions of Lagos State, namely Ikorodu, Badagry, Lagos Mainland/Ikeja, Lagos Island and Epe. Five variables were identified as covariates. The prognostic multi-variable models were employed. The binary logistic regression model with 5 covariates was selected as the best model for the binary response variable, while the Poisson regression model with 4 covariates was selected as the best model for the count response variable. At the end of the research, Ikorodu, Badagry and Epe communities have higher than expected childhood mortality rates. Also, we estimated childhood mortality rate in Lagos State and measured the variations in childhood mortality across Lagos communities. The factors that predict these variations were detected and control measures were recommended to reduce the difference in childhood mortality rate in Lagos State.","PeriodicalId":89781,"journal":{"name":"International journal of statistics and probability","volume":"104 S7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139154233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Vanderbilt Nigeria Biostatistics Training Program (VN-BioStat): Results from a Skills Workshop.","authors":"Bryan E Shepherd, Nafiu Hussaini, Alex Huang, Chelsea Van Wyk, Meira S Kowalski, Donna J Ingles, C William Wester, Chun Li, Muktar H Aliyu","doi":"10.5539/ijsp.v12n6p66","DOIUrl":"10.5539/ijsp.v12n6p66","url":null,"abstract":"<p><p>The Vanderbilt-Nigeria Biostatistics Training Program (VN-BioStat) aims to establish a research and training platform for biostatisticians doing HIV-related research in Nigeria, including enhancing mid-level biostatistics capacity through annual workshops. This paper describes findings from the inaugural workshop in Kano, Nigeria. Participants were surveyed before and after the workshop to assess their self-perceived familiarity with and confidence in their abilities to use statistical software and apply specific statistical techniques, as well as to gather feedback regarding the conduct of the workshop and future topic areas. Of the 23 participants enrolled in the workshop, 22 (96%) completed both pre- and post-workshop assessments. In both pre-workshop and post-workshop surveys, participants ranked their confidence in statistical skills using Likert scales. Scores were transformed to a 0-100 scale, and averages computed. Participants also shared open-ended feedback about the workshop and suggested future topic areas. Before the training, the average participant reported having either a \"beginner\" (30% of participants) or \"moderate\" (43%) level of familiarity with R. Many participants (65%) rated themselves as having \"moderate\" or \"expert\" familiarity with SPSS. Pre-workshop averages for confidence ranged from 26 to 64, with lowest confidence in \"expanding continuous covariates in regression models and interpret results\" and highest confidence in \"fitting and interpreting results from a linear regression model\". Post-workshop averages for confidence were all above 70. The lowest post-workshop score (74) was for \"fit and interpret results from a semiparametric linear transformation model\". The greatest increase in confidence was observed in \"expanding continuous covariates in regression models using splines and interpreting results\" and the lowest increase was in \"fitting and interpreting results from a linear regression model.\" Participants offered positive feedback on instructor effectiveness (4.9/5) and overall course quality (4.9/5). While the overall course was rated on a 0-100 scale as \"moderately difficult\" (mean ± SD: 40.5 ± 17.5), the participants felt the course was highly organized (87.7 ± 17.8), and the information was moderately easy to learn (81.9 ± 15.9). Suggestions for future workshops included providing supplementary resources for out-of-classroom learning and releasing codes in advance to enhance participants' preparation. Among suggestions for future workshop topics, 80% of respondents listed survival analysis. Lessons learned provide insight into how short-term training opportunities can be leveraged to build biostatistics capacity in similar settings.</p>","PeriodicalId":89781,"journal":{"name":"International journal of statistics and probability","volume":"12 6","pages":"66-72"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10843821/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139693712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 12, No. 5","authors":"Wendy Smith","doi":"10.5539/ijsp.v12n5p42","DOIUrl":"https://doi.org/10.5539/ijsp.v12n5p42","url":null,"abstract":"Reviewer Acknowledgements for International Journal of Statistics and Probability, Vol. 12, No. 5","PeriodicalId":89781,"journal":{"name":"International journal of statistics and probability","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135888554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}