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The Semi-Hyperbolic Distribution and Its Applications 半双曲分布及其应用

Stats Pub Date : 2023-10-21 DOI: 10.3390/stats6040071

Roman V. Ivanov

引用次数: 0

An Archimedean Copulas-Based Approach for m-Consecutive-k-Out-of-n: F Systems with Exchangeable Components 具有可交换分量的m-连续-k- of-n: F系统的阿基米德copulas方法

Stats Pub Date : 2023-10-20 DOI: 10.3390/stats6040070

Ioannis S. Triantafyllou

引用次数: 0

Exploring the Consumer Acceptance of Nano Clothing Using a PLS-SEM Analysis 利用PLS-SEM分析探索消费者对纳米服装的接受程度

Stats Pub Date : 2023-10-19 DOI: 10.3390/stats6040069

Andreea-Ionela Puiu, Rodica Ianole-Călin, Elena Druică

引用次数: 0

Comparison between Two Algorithms for Computing the Weighted Generalized Affinity Coefficient in the Case of Interval Data 区间数据加权广义关联系数两种计算算法的比较

Stats Pub Date : 2023-10-13 DOI: 10.3390/stats6040068

Áurea Sousa, Osvaldo Silva, Leonor Bacelar-Nicolau, João Cabral, Helena Bacelar-Nicolau

{"title":"Comparison between Two Algorithms for Computing the Weighted Generalized Affinity Coefficient in the Case of Interval Data","authors":"Áurea Sousa, Osvaldo Silva, Leonor Bacelar-Nicolau, João Cabral, Helena Bacelar-Nicolau","doi":"10.3390/stats6040068","DOIUrl":"https://doi.org/10.3390/stats6040068","url":null,"abstract":"From the affinity coefficient between two discrete probability distributions proposed by Matusita, Bacelar-Nicolau introduced the affinity coefficient in a cluster analysis context and extended it to different types of data, including for the case of complex and heterogeneous data within the scope of symbolic data analysis (SDA). In this study, we refer to the most significant partitions obtained using the hierarchical cluster analysis (h.c.a.) of two well-known datasets that were taken from the literature on complex (symbolic) data analysis. h.c.a. is based on the weighted generalized affinity coefficient for the case of interval data and on probabilistic aggregation criteria from a VL parametric family. To calculate the values of this coefficient, two alternative algorithms were used and compared. Both algorithms were able to detect clusters of macrodata (aggregated data into groups of interest) that were consistent and consonant with those reported in the literature, but one performed better than the other in some specific cases. Moreover, both approaches allow for the treatment of large microdatabases (non-aggregated data) after their transformation into macrodata from the huge microdata.","PeriodicalId":93142,"journal":{"name":"Stats","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135917806","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}

引用次数: 1

On the Vector Representation of Characteristic Functions 关于特征函数的向量表示

Stats Pub Date : 2023-10-10 DOI: 10.3390/stats6040067

Wolf-Dieter Richter

引用次数: 0

Newcomb–Benford’s Law in Neuromuscular Transmission: Validation in Hyperkalemic Conditions 神经肌肉传递中的Newcomb-Benford定律:在高钾血症条件下的验证

Stats Pub Date : 2023-10-09 DOI: 10.3390/stats6040066

Adriano Silva, Sergio Floquet, Ricardo Lima

{"title":"Newcomb–Benford’s Law in Neuromuscular Transmission: Validation in Hyperkalemic Conditions","authors":"Adriano Silva, Sergio Floquet, Ricardo Lima","doi":"10.3390/stats6040066","DOIUrl":"https://doi.org/10.3390/stats6040066","url":null,"abstract":"Recently, we demonstrated the validity of the anomalous numbers law, known as Newcomb–Benford’s law, in mammalian neuromuscular transmission, considering different extracellular calcium. The present work continues to examine how changes in extracellular physiological artificial solution can modulate the first digit law in the context of spontaneous acetylcholine release at the neuromuscular junction. Using intracellular measurements, we investigated if the intervals of miniature potentials collected at the neuromuscular junction obey the law in a hyperkalemic environment. When bathed in standard Ringer’s solution, the experiments provided 22,582 intervals extracted from 14 recordings. On the other hand, 690,385 intervals were obtained from 12 experiments in a modified Ringer’s solution containing a high potassium concentration. The analysis showed that the intervals, harvested from recordings at high potassium, satisfactorily obeyed Newcomb–Benford’s law. Furthermore, our data allowed us to uncover a conformity fluctuation as a function of the number of intervals of the miniature potentials. Finally, we discuss the biophysical implications of the present findings.","PeriodicalId":93142,"journal":{"name":"Stats","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135094154","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}

引用次数: 0

Computationally Efficient Poisson Time-Varying Autoregressive Models through Bayesian Lattice Filters 基于贝叶斯格滤波器的计算高效泊松时变自回归模型

Stats Pub Date : 2023-10-09 DOI: 10.3390/stats6040065

Yuelei Sui, Scott H. Holan, Wen-Hsi Yang

引用次数: 0

Effective Sample Size with the Bivariate Gaussian Common Component Model 二元高斯共分量模型的有效样本量

Stats Pub Date : 2023-10-08 DOI: 10.3390/stats6040064

Letícia Ellen Dal Canton, Luciana Pagliosa Carvalho Guedes, Miguel Angel Uribe-Opazo, Tamara Cantu Maltauro

{"title":"Effective Sample Size with the Bivariate Gaussian Common Component Model","authors":"Letícia Ellen Dal Canton, Luciana Pagliosa Carvalho Guedes, Miguel Angel Uribe-Opazo, Tamara Cantu Maltauro","doi":"10.3390/stats6040064","DOIUrl":"https://doi.org/10.3390/stats6040064","url":null,"abstract":"Effective sample size (ESS) consists of an equivalent number of sampling units of a georeferenced variable that would produce the same sampling error, as it considers the information that each georeferenced sampling unit contains about itself as well as in relation to its neighboring sampling units. This measure can provide useful information in the planning of future georeferenced sampling for spatial variability experiments. The objective of this article was to develop a bivariate methodology for ESS (ESSbi), considering the bivariate Gaussian common component model (BGCCM), which accounts both for the spatial correlation between the two variables and for the individual spatial association. All properties affecting the univariate methodology were verified for ESSbi using simulation studies or algebraic methods, including scenarios to verify the impact of the BGCCM common range parameter on the estimated ESSbi values. ESSbi was applied to real organic matter (OM) and sum of bases (SB) data from an agricultural area. The study found that 60% of the sample observations of the OM–SB pair contained spatially redundant information. The reduced sample configuration proved efficient by preserving spatial variability when comparing the original and reduced OM maps, using SB as a covariate. The Tau concordance index confirmed moderate accuracy between the maps.","PeriodicalId":93142,"journal":{"name":"Stats","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135251306","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}

引用次数: 0

A Shared Frailty Model for Left-Truncated and Right-Censored Under-Five Child Mortality Data in South Africa 南非五岁以下儿童死亡率数据左截右删的共享脆弱性模型

Stats Pub Date : 2023-10-06 DOI: 10.3390/stats6040063

Tshilidzi Mulaudzi, Yehenew Kifle, Roel Braekers

{"title":"A Shared Frailty Model for Left-Truncated and Right-Censored Under-Five Child Mortality Data in South Africa","authors":"Tshilidzi Mulaudzi, Yehenew Kifle, Roel Braekers","doi":"10.3390/stats6040063","DOIUrl":"https://doi.org/10.3390/stats6040063","url":null,"abstract":"Many African nations continue to grapple with persistently high under-five child mortality rates, particularly those situated in the Sub-Saharan region, including South Africa. A multitude of socio-economic factors are identified as key contributors to the elevated under-five child mortality in numerous African nations. This research endeavors to investigate various factors believed to be associated with child mortality by employing advanced statistical models. This study utilizes child-level survival data from South Africa, characterized by left truncation and right censoring, to fit a Cox proportional hazards model under the assumption of working independence. Additionally, a shared frailty model is applied, clustering children based on their mothers. Comparative analysis is performed between the results obtained from the shared frailty model and the Cox proportional hazards model under the assumption of working independence. Within the scope of this analysis, several factors stand out as significant contributors to under-five child mortality in the study area, including gender, birth province, birth year, birth order, and twin status. Notably, the shared frailty model demonstrates superior performance in modeling the dataset, as evidenced by a lower likelihood cross-validation score compared to the Cox proportional hazards model assuming independence. This improvement can be attributed to the shared frailty model’s ability to account for heterogeneity among mothers and the inherent association between siblings born to the same mother, ultimately enhancing the quality of the study’s conclusions.","PeriodicalId":93142,"journal":{"name":"Stats","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135351051","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}

引用次数: 0

Ensemble Algorithms to Improve COVID-19 Growth Curve Estimates 改进COVID-19增长曲线估计的集成算法

Stats Pub Date : 2023-09-29 DOI: 10.3390/stats6040062

Raydonal Ospina, Jaciele Oliveira, Cristiano Ferraz, André Leite, João Gondim

{"title":"Ensemble Algorithms to Improve COVID-19 Growth Curve Estimates","authors":"Raydonal Ospina, Jaciele Oliveira, Cristiano Ferraz, André Leite, João Gondim","doi":"10.3390/stats6040062","DOIUrl":"https://doi.org/10.3390/stats6040062","url":null,"abstract":"In January 2020, the world was taken by surprise as a novel disease, COVID-19, emerged, attributed to the new SARS-CoV-2 virus. Initial cases were reported in China, and the virus rapidly disseminated globally, leading the World Health Organization (WHO) to declare it a pandemic on 11 March 2020. Given the novelty of this pathogen, limited information was available regarding its infection rate and symptoms. Consequently, the necessity of employing mathematical models to enable researchers to describe the progression of the epidemic and make accurate forecasts became evident. This study focuses on the analysis of several dynamic growth models, including the logistics, Gompertz, and Richards growth models, which are commonly employed to depict the spread of infectious diseases. These models are integrated to harness their predictive capabilities, utilizing an ensemble modeling approach. The resulting ensemble algorithm was trained using COVID-19 data from the Brazilian state of Paraíba. The proposed ensemble model approach effectively reduced forecasting errors, showcasing itself as a promising methodology for estimating COVID-19 growth curves, improving data forecasting accuracy, and providing rapid responses in the early stages of the pandemic.","PeriodicalId":93142,"journal":{"name":"Stats","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135246022","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}

引用次数: 0