Journal of Probability and Statistics最新文献

Flexible Lévy-Based Models for Time Series of Count Data with Zero-Inflation, Overdispersion, and Heavy Tails 零通胀、过度分散和重尾计数数据时间序列的灵活莱维模型

IF 1.1

Journal of Probability and Statistics Pub Date : 2023-11-30 DOI: 10.1155/2023/1780404

Confort Kollie, Philip Ngare, B. Malenje

{"title":"Flexible Lévy-Based Models for Time Series of Count Data with Zero-Inflation, Overdispersion, and Heavy Tails","authors":"Confort Kollie, Philip Ngare, B. Malenje","doi":"10.1155/2023/1780404","DOIUrl":"https://doi.org/10.1155/2023/1780404","url":null,"abstract":"The explosion of time series count data with diverse characteristics and features in recent years has led to a proliferation of new analysis models and methods. Significant efforts have been devoted to achieving flexibility capable of handling complex dependence structures, capturing multiple distributional characteristics simultaneously, and addressing nonstationary patterns such as trends, seasonality, or change points. However, it remains a challenge when considering them in the context of long-range dependence. The Lévy-based modeling framework offers a promising tool to meet the requirements of modern data analysis. It enables the modeling of both short-range and long-range serial correlation structures by selecting the kernel set accordingly and accommodates various marginal distributions within the class of infinitely divisible laws. We propose an extension of the basic stationary framework to capture additional marginal properties, such as heavy-tailedness, in both short-term and long-term dependencies, as well as overdispersion and zero inflation in simultaneous modeling. Statistical inference is based on composite pairwise likelihood. The model’s flexibility is illustrated through applications to rainfall data in Guinea from 2008 to 2023, and the number of NSF funding awarded to academic institutions. The proposed model demonstrates remarkable flexibility and versatility, capable of simultaneously capturing overdispersion, zero inflation, and heavy-tailedness in count time series data.","PeriodicalId":44760,"journal":{"name":"Journal of Probability and Statistics","volume":"17 1","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139198810","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

Exponentially Generated Modified Chen Distribution with Applications to Lifetime Dataset 指数生成的修正陈分布与寿命数据集的应用

IF 1.1

Journal of Probability and Statistics Pub Date : 2023-11-21 DOI: 10.1155/2023/4458562

Awopeju Kabiru Abidemi, A. A. Abiodun

引用次数: 0

Bayesian Estimation of the Stress-Strength Reliability Based on Generalized Order Statistics for Pareto Distribution 基于广义阶统计量的Pareto分布应力-强度可靠度贝叶斯估计

Journal of Probability and Statistics Pub Date : 2023-11-13 DOI: 10.1155/2023/8648261

Zahra Karimi Ezmareh, Gholamhossein Yari

引用次数: 0

Monitoring Changes in Clustering Solutions: A Review of Models and Applications 监测聚类解决方案的变化:模型和应用综述

Journal of Probability and Statistics Pub Date : 2023-11-03 DOI: 10.1155/2023/7493623

Muhammad Atif, Muhammad Shafiq, Muhammad Farooq, Gohar Ayub, Friedrich Leisch, Muhammad Ilyas

{"title":"Monitoring Changes in Clustering Solutions: A Review of Models and Applications","authors":"Muhammad Atif, Muhammad Shafiq, Muhammad Farooq, Gohar Ayub, Friedrich Leisch, Muhammad Ilyas","doi":"10.1155/2023/7493623","DOIUrl":"https://doi.org/10.1155/2023/7493623","url":null,"abstract":"This article comprehensively reviews the applications and algorithms used for monitoring the evolution of clustering solutions in data streams. The clustering technique is an unsupervised learning problem that involves the identification of natural subgroups in a large dataset. In contrast to supervised learning models, clustering is a data mining technique that retrieves the hidden pattern in the input dataset. The clustering solution reflects the mechanism that leads to a high level of similarity between the items. A few applications include pattern recognition, knowledge discovery, and market segmentation. However, many modern-day applications generate streaming or temporal datasets over time, where the pattern is not stationary and may change over time. In the context of this article, change detection is the process of identifying differences in the cluster solutions obtained from streaming datasets at consecutive time points. In this paper, we briefly review the models/algorithms introduced in the literature to monitor clusters’ evolution in data streams. Monitoring the changes in clustering solutions in streaming datasets plays a vital role in policy-making and future prediction. Of course, it has a wide range of applications that cannot be covered in a single study, but some of the most common are highlighted in this article.","PeriodicalId":44760,"journal":{"name":"Journal of Probability and Statistics","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135818826","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

Fitting Time Series Models to Fisheries Data to Ascertain Age 拟合渔业数据的时间序列模型以确定年龄

Journal of Probability and Statistics Pub Date : 2023-10-07 DOI: 10.1155/2023/9991872

Kathleen S. Kirch, Norou Diawara, Cynthia M. Jones

{"title":"Fitting Time Series Models to Fisheries Data to Ascertain Age","authors":"Kathleen S. Kirch, Norou Diawara, Cynthia M. Jones","doi":"10.1155/2023/9991872","DOIUrl":"https://doi.org/10.1155/2023/9991872","url":null,"abstract":"The ability of government agencies to assign accurate ages of fish is important to fisheries management. Accurate ageing allows for most reliable age-based models to be used to support sustainability and maximize economic benefit. Assigning age relies on validating putative annual marks by evaluating accretional material laid down in patterns in fish ear bones, typically by marginal increment analysis. These patterns often take the shape of a sawtooth wave with an abrupt drop in accretion yearly to form an annual band and are typically validated qualitatively. Researchers have shown key interest in modeling marginal increments to verify the marks do, in fact, occur yearly. However, it has been challenging in finding the best model to predict this sawtooth wave pattern. We propose three new applications of time series models to validate the existence of the yearly sawtooth wave patterned data: autoregressive integrated moving average (ARIMA), unobserved component, and copula. These methods are expected to enable the identification of yearly patterns in accretion. ARIMA and unobserved components account for the dependence of observations and error, while copula incorporates a variety of marginal distributions and dependence structures. The unobserved component model produced the best results (AIC: −123.7, MSE 0.00626), followed by the time series model (AIC: −117.292, MSE: 0.0081), and then the copula model (AIC: −96.62, Kendall’s tau: −0.5503). The unobserved component model performed best due to the completeness of the dataset. In conclusion, all three models are effective tools to validate yearly accretional patterns in fish ear bones despite their differences in constraints and assumptions.","PeriodicalId":44760,"journal":{"name":"Journal of Probability and Statistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252244","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

Clustering Analysis of Multivariate Data: A Weighted Spatial Ranks-Based Approach 多变量数据的聚类分析:一种基于加权空间秩的方法

Journal of Probability and Statistics Pub Date : 2023-09-30 DOI: 10.1155/2023/8849404

Mohammed H. Baragilly, Hend Gabr, Brian H. Willis

引用次数: 0

A New Type 1 Alpha Power Family of Distributions and Modeling Data with Correlation, Overdispersion, and Zero-Inflation in the Health Data Sets 一个新的1型阿尔法幂分布族和健康数据集中具有相关性、过度分散和零通货膨胀的建模数据

IF 1.1

Journal of Probability and Statistics Pub Date : 2023-08-31 DOI: 10.1155/2023/6611108

Getachew Tekle, R. Roozegar, Zubair Ahmad

{"title":"A New Type 1 Alpha Power Family of Distributions and Modeling Data with Correlation, Overdispersion, and Zero-Inflation in the Health Data Sets","authors":"Getachew Tekle, R. Roozegar, Zubair Ahmad","doi":"10.1155/2023/6611108","DOIUrl":"https://doi.org/10.1155/2023/6611108","url":null,"abstract":"In the recent era, the introduction of a new family of distributions has gotten great attention due to the curbs of the classical univariate distributions. This study introduces a novel family of distributions called a new type 1 alpha power family of distributions. Based on the novel family, a special model called a new type 1 alpha power Weibull model is studied in depth. The new model has very interesting patterns and it is very flexible. Thus, it can model the real data with the failure rate patterns of increasing, decreasing, parabola-down, and bathtub. Its applicability is studied by applying it to the health sector data, and time-to-recovery of breast cancer patients, and its performance is compared to seven well-known models. Based on the model comparison, it is the best model to fit the health-related data with no exceptional features. Furthermore, the popular models for the data with exceptional features such as correlation, overdispersion, and zero-inflation in aggregate are explored with applications to epileptic seizer data. Sometimes, these features are beyond the probability distribution models. Hence, this study has implemented eight possible models separately to these data and they are compared based on the standard techniques. Accordingly, the zero-inflated Poisson-normal-gamma model which includes the random effects in the linear predictor to handle the three features simultaneously has shown its supremacy over the others and is the best model to fit the health-related data with these features.","PeriodicalId":44760,"journal":{"name":"Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48658235","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

Applications of Robust Methods in Spatial Analysis 鲁棒方法在空间分析中的应用

IF 1.1

Journal of Probability and Statistics Pub Date : 2023-05-10 DOI: 10.1155/2023/1328265

S. Selvaratnam

引用次数: 0

Hybrid Model for Stock Market Volatility 股票市场波动性的混合模型

IF 1.1

Journal of Probability and Statistics Pub Date : 2023-04-25 DOI: 10.1155/2023/6124649

Kofi Agyarko, N. K. Frempong, E. N. Wiah

引用次数: 0

Estimation of Risk Factors Affecting Screening Outcomes of Prostate Cancer Using the Bayesian Ordinal Logistic Model 应用贝叶斯有序逻辑模型估计影响癌症筛查结果的危险因素

IF 1.1

Journal of Probability and Statistics Pub Date : 2023-03-06 DOI: 10.1155/2023/4987764

J. Sirengo, D. Alilah, D. Mbete, R. Keli

{"title":"Estimation of Risk Factors Affecting Screening Outcomes of Prostate Cancer Using the Bayesian Ordinal Logistic Model","authors":"J. Sirengo, D. Alilah, D. Mbete, R. Keli","doi":"10.1155/2023/4987764","DOIUrl":"https://doi.org/10.1155/2023/4987764","url":null,"abstract":"Prostate cancer occurs when cells in the prostate gland grow out of control. Almost all prostate cancers are adenocarcinomas. The survival rate for prostate cancer patients depends on the screening outcome, which can be either no prostate cancer, early detection, and late detection or advanced stage detection. The main objective of this study was to estimate the risk factors affecting the screening outcome of prostate cancer. With ordinal outcomes, a generalized Bayesian ordinal logistic model was considered in the analysis. The generalized Bayesian ordinal logistic model helped in estimation of coefficient parameters of the risk factors affecting each level of prostate cancer-screening outcomes. In the study, positive coefficients, that is, \u0000 \u0000 \u0000 \u0000 β\u0000 \u0000 \u0000 k\u0000 \u0000 \u0000 >\u0000 0\u0000 \u0000 , indicated that the higher values on the explanatory variable increased the chances of the respondent being in a higher category of the dependent variable than the current one, while the negative coefficients, that is, \u0000 \u0000 \u0000 \u0000 β\u0000 \u0000 \u0000 k\u0000 \u0000 \u0000 <\u0000 0\u0000 \u0000 , signified that the higher values on the explanatory variable increased the likelihood of being in the current or lower category of prostate cancer. For instance, from the analysis, positive or negative outcomes of prostate cancer showed that an increase in weight lowered the chances of an individual having the disease.","PeriodicalId":44760,"journal":{"name":"Journal of Probability and Statistics","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46516343","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