Jordan Journal of Mathematics and Statistics最新文献_第8页

Poisson Quasi-Maximum Likelihood Estimator-based CUSUM Test for Integer-Valued Time Series 基于Poisson拟极大似然估计的整数值时间序列CUSUM检验

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Jordan Journal of Mathematics and Statistics Pub Date : 2019-01-01 DOI: 10.3844/jmssp.2019.250.258

Sangyeol Lee

引用次数: 2

Fuzzy Bernstein-Stancu Operator of Rough I-Core of Triple Sequences 三组序列粗糙i核的模糊bernstein - stu算子

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Jordan Journal of Mathematics and Statistics Pub Date : 2019-01-01 DOI: 10.3844/JMSSP.2019.323.332

A. Esi, N. Subramanian, M. Ozdemir

引用次数: 0

Functional Non-Inferiority Hypothesis Testing for Longitudinal Data 纵向资料的功能非劣效假设检验

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Jordan Journal of Mathematics and Statistics Pub Date : 2019-01-01 DOI: 10.3844/JMSSP.2019.208.217

A. Sandie, A. Wanjoya, J. B. Tchatchueng-Mbougua

{"title":"Functional Non-Inferiority Hypothesis Testing for Longitudinal Data","authors":"A. Sandie, A. Wanjoya, J. B. Tchatchueng-Mbougua","doi":"10.3844/JMSSP.2019.208.217","DOIUrl":"https://doi.org/10.3844/JMSSP.2019.208.217","url":null,"abstract":"The study pattern of non-inferiority trials is increasingly used to show the non-inferiority of new health intervention. Although in such studies the data are longitudinally collected (data held over a period of time), the conclusion of these non-inferiority trials is based on data observed at a specific time during the study period (usually at the end of the study period). In this study, we present a method that takes into account all the data observed during the study period to perform non-inferiority test. Thus, we approximate the observed data on a statistical unit by a function of time. This allows to transform the observed data on a time grid into functional data on a continuum domain. Although it could have some relevant applications, the functional data analysis for non-inferiority test has not been addressed. In this study, the functional non-inferiority hypothesis testing has been introduced. The optimal point-wise test and simultaneous confidence bands have been adapted and adopted for the purpose. The assessment of the methods has been done through simulations example. Both methods have good performances for large sample sizes. For small sample sizes, the optimal point-wise test would be too conservative while the simultaneous confidence bands based test would be a bit liberal.","PeriodicalId":41981,"journal":{"name":"Jordan Journal of Mathematics and Statistics","volume":"5 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80744508","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

Explaining the Generalized Cross-Validation on Linear Models 解释线性模型的广义交叉验证

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Jordan Journal of Mathematics and Statistics Pub Date : 2019-01-01 DOI: 10.3844/jmssp.2019.298.307

L. Chaves, Laerte Dias de Carvalho, Carlos José dos Reis, Devanil Jaques de Souza

引用次数: 2

Constructing Gibbs Measure in a Rigorous Way 严谨地构造吉布斯测度

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Jordan Journal of Mathematics and Statistics Pub Date : 2019-01-01 DOI: 10.3844/jmssp.2019.308.322

F. Kachapova, Ilias Kachapov

引用次数: 0

Survival Analysis of Data of HIV Infected Persons Receiving Antiretroviral Therapy Using a Model-Based Binary Tree Approach 使用基于模型的二叉树方法对接受抗逆转录病毒治疗的HIV感染者的生存数据进行分析

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Jordan Journal of Mathematics and Statistics Pub Date : 2019-01-01 DOI: 10.3844/jmssp.2019.354.365

S. Tiendrébéogo, B. Somé, S. Kouanda, S. Dossou-Gbété

{"title":"Survival Analysis of Data of HIV Infected Persons Receiving Antiretroviral Therapy Using a Model-Based Binary Tree Approach","authors":"S. Tiendrébéogo, B. Somé, S. Kouanda, S. Dossou-Gbété","doi":"10.3844/jmssp.2019.354.365","DOIUrl":"https://doi.org/10.3844/jmssp.2019.354.365","url":null,"abstract":"Discrete-time approach is used in survival data analysis when only the time interval in which the event of interest has occurred is known or when this event occurs in a discrete - time scale. The work presented in this paper is motivated by the analysis of HIV/AIDS follow-up data collected in Burkina Faso during the 5-YEAR Global Fund program implemented to fight AIDS, Tuberculosis and Malaria. The research question that motivated the work is the likely existence of different mortality risk profiles of people infected with HIV/AIDS, depending on their characteristics and health status at the beginning of their care. In order to answer these questions, we considered a binary tree regression approach for survival data analysis since such a model owns the ability to handle interaction effects between the outcome covariates without a tight specification of such effects during the model statement step. This helps to prevent specification and interpretation errors. The fitted model resulted in splitting patients into three disjoint subgroups, corresponding each to a specific hazard profile.","PeriodicalId":41981,"journal":{"name":"Jordan Journal of Mathematics and Statistics","volume":"3 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87511742","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

Density Power Downweighting and Robust Inference: Some New Strategies 密度功率降权与鲁棒推理:一些新策略

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Jordan Journal of Mathematics and Statistics Pub Date : 2019-01-01 DOI: 10.3844/JMSSP.2019.333.353

Saptarshi Roy, K. Chakraborty, S. Bhadra, A. Basu

引用次数: 1

Subset ARMA Model Identification for Monthly Electricity Consumption Data 月用电量数据子集ARMA模型辨识

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Jordan Journal of Mathematics and Statistics Pub Date : 2019-01-01 DOI: 10.3844/jmssp.2019.22.29

Amaal El Sayed Abd El Ghany Mubarak

{"title":"Subset ARMA Model Identification for Monthly Electricity Consumption Data","authors":"Amaal El Sayed Abd El Ghany Mubarak","doi":"10.3844/jmssp.2019.22.29","DOIUrl":"https://doi.org/10.3844/jmssp.2019.22.29","url":null,"abstract":"Subset models can always be highly influential in series analysis, particularly when the data demonstrate a sort of form in periodic behavior with miscellaneous natural period's ranges, specifically; days, weeks, months and years. Subset models can also be effective as they let the number of parameters lower allowing only the really needed ones to be present in the model. Though subset autoregressive moving-average (ARMA) models always receive much attention, their identification is computationally cumbersome. This paper aims at the identification of Subset ARMA model through utilizing two methods of identification; innovation regression method and genetic algorithm method. The innovation regression method is a traditional one whilst the genetic algorithm methodologies represent a relatively modern approach for identifying Subset ARMA models in recent decades. After encoding every ARMA model as a binary string in the latter method, the iterative algorithm tries tracing the natural evolution of the population in those strings through letting strings to reproduce, producing newer models for competing for survival within upcoming populations. The aim of this research is to show the procedures for identifying the most appropriate order of subset ARMA models for the monthly electricity consumption data in Damietta governorate.","PeriodicalId":41981,"journal":{"name":"Jordan Journal of Mathematics and Statistics","volume":"90 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74624187","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

On the Sensitivity of a Dynamic Measure of Financial Inequality 金融不平等动态测度的敏感性研究

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Jordan Journal of Mathematics and Statistics Pub Date : 2019-01-01 DOI: 10.3844/jmssp.2019.280.297

G. D’Amico, S. Scocchera, L. Storchi

引用次数: 0

Model Comparison for the Prediction of Stock Prices in the NYSE 纽约证券交易所股票价格预测的模型比较

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Jordan Journal of Mathematics and Statistics Pub Date : 2019-01-01 DOI: 10.3844/JMSSP.2019.233.249

Victoria Switlyk, Junfeng Shang

{"title":"Model Comparison for the Prediction of Stock Prices in the NYSE","authors":"Victoria Switlyk, Junfeng Shang","doi":"10.3844/JMSSP.2019.233.249","DOIUrl":"https://doi.org/10.3844/JMSSP.2019.233.249","url":null,"abstract":"The stock market is an integral part of investments as well as the economy. The prediction of stock prices is an exciting and challenging problem that has been considered by many due to the complexity and noise within the market and to the potential profit that can be yielded from accurate predictions. We aim to construct and compare models used for the prediction of weekly closing prices for some of the top stocks in the New York Stock Exchange (NYSE) and to discuss the relationship between stock prices and the predictor variables. Relationships explored in the study include that with macroeconomic variables such as the Federal Funds Rate and the M1 money supply and market indexes such as the CBOE Volatility Index, the Wilshire 5000 Total Market Full Cap Index, the CBOE interest rate for 10-year T-notes and bonds, and NYSE commodity indexes including XOI and HUI. Models are built using methods of regression analysis and time series analysis. Models are analyzed and compared with one another by considering their predictive ability, accuracy, fit to the underlying model assumptions, and usefulness in application. The final models considered are a pooled regression model involving the median weekly closing price across all stocks, a varying intercept model considering the weekly closing price for each individual stock, and an ARIMA time series model that predicts the median weekly closing stock price based on past prices.","PeriodicalId":41981,"journal":{"name":"Jordan Journal of Mathematics and Statistics","volume":"34 1","pages":""},"PeriodicalIF":0.3,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76521111","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