Stats Pub Date : 2022-11-28 DOI: 10.3390/stats5040073

I. Tsolas

引用次数: 2

On the Relation between Lambert W-Function and Generalized Hypergeometric Functions 关于Lambert W函数与广义超几何函数的关系

Stats Pub Date : 2022-11-23 DOI: 10.3390/stats5040072

P. N. Rathie, L. Ozelim

引用次数: 1

Model Validation of a Single Degree-of-Freedom Oscillator: A Case Study 单自由度振荡器的模型验证：一个实例研究

Stats Pub Date : 2022-11-18 DOI: 10.3390/stats5040071

E. Boone, Jan Hannig, R. Ghanam, Sujit Ghosh, F. Ruggeri, S. Prudhomme

{"title":"Model Validation of a Single Degree-of-Freedom Oscillator: A Case Study","authors":"E. Boone, Jan Hannig, R. Ghanam, Sujit Ghosh, F. Ruggeri, S. Prudhomme","doi":"10.3390/stats5040071","DOIUrl":"https://doi.org/10.3390/stats5040071","url":null,"abstract":"In this paper, we investigate a validation process in order to assess the predictive capabilities of a single degree-of-freedom oscillator. Model validation is understood here as the process of determining the accuracy with which a model can predict observed physical events or important features of the physical system. Therefore, assessment of the model needs to be performed with respect to the conditions under which the model is used in actual simulations of the system and to specific quantities of interest used for decision-making. Model validation also supposes that the model be trained and tested against experimental data. In this work, virtual data are produced from a non-linear single degree-of-freedom oscillator, the so-called oracle model, which is supposed to provide an accurate representation of reality. The mathematical model to be validated is derived from the oracle model by simply neglecting the non-linear term. The model parameters are identified via Bayesian updating. This calibration process also includes a modeling error due to model misspecification and modeled as a normal probability density function with zero mean and standard deviation to be calibrated.","PeriodicalId":93142,"journal":{"name":"Stats","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47929046","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 Weibull-Beta Prime Distribution to Model COVID-19 Data with the Presence of Covariates and Censored Data 包含协变量和删节数据的COVID-19数据建模的Weibull-Beta Prime分布

Stats Pub Date : 2022-11-17 DOI: 10.3390/stats5040069

Elisângela C. Biazatti, G. Cordeiro, Gabriela M. Rodrigues, E. Ortega, L. H. de Santana

引用次数: 3

Closed Form Bayesian Inferences for Binary Logistic Regression with Applications to American Voter Turnout 二元Logistic回归的闭式贝叶斯推断及其在美国选民投票中的应用

Stats Pub Date : 2022-11-17 DOI: 10.3390/stats5040070

Kevin D. Dayaratna, Jesse M. Crosson, Chandler Hubbard

{"title":"Closed Form Bayesian Inferences for Binary Logistic Regression with Applications to American Voter Turnout","authors":"Kevin D. Dayaratna, Jesse M. Crosson, Chandler Hubbard","doi":"10.3390/stats5040070","DOIUrl":"https://doi.org/10.3390/stats5040070","url":null,"abstract":"Understanding the factors that influence voter turnout is a fundamentally important question in public policy and political science research. Bayesian logistic regression models are useful for incorporating individual level heterogeneity to answer these and many other questions. When these questions involve incorporating individual level heterogeneity for large data sets that include many demographic and ethnic subgroups, however, standard Markov Chain Monte Carlo (MCMC) sampling methods to estimate such models can be quite slow and impractical to perform in a reasonable amount of time. We present an innovative closed form Empirical Bayesian approach that is significantly faster than MCMC methods, thus enabling the estimation of voter turnout models that had previously been considered computationally infeasible. Our results shed light on factors impacting voter turnout data in the 2000, 2004, and 2008 presidential elections. We conclude with a discussion of these factors and the associated policy implications. We emphasize, however, that although our application is to the social sciences, our approach is fully generalizable to the myriads of other fields involving statistical models with binary dependent variables and high-dimensional parameter spaces as well.","PeriodicalId":93142,"journal":{"name":"Stats","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43746245","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 New Predictive Algorithm for Time Series Forecasting Based on Machine Learning Techniques: Evidence for Decision Making in Agriculture and Tourism Sectors 基于机器学习技术的时间序列预测新算法:农业和旅游部门决策的证据

Stats Pub Date : 2022-11-16 DOI: 10.3390/stats5040068

Juan Borrero, J. Mariscal, Alfonso Vargas-Sánchez

{"title":"A New Predictive Algorithm for Time Series Forecasting Based on Machine Learning Techniques: Evidence for Decision Making in Agriculture and Tourism Sectors","authors":"Juan Borrero, J. Mariscal, Alfonso Vargas-Sánchez","doi":"10.3390/stats5040068","DOIUrl":"https://doi.org/10.3390/stats5040068","url":null,"abstract":"Accurate time series prediction techniques are becoming fundamental to modern decision support systems. As massive data processing develops in its practicality, machine learning (ML) techniques applied to time series can automate and improve prediction models. The radical novelty of this paper is the development of a hybrid model that combines a new approach to the classical Kalman filter with machine learning techniques, i.e., support vector regression (SVR) and nonlinear autoregressive (NAR) neural networks, to improve the performance of existing predictive models. The proposed hybrid model uses, on the one hand, an improved Kalman filter method that eliminates the convergence problems of time series data with large error variance and, on the other hand, an ML algorithm as a correction factor to predict the model error. The results reveal that our hybrid models obtain accurate predictions, substantially reducing the root mean square and absolute mean errors compared to the classical and alternative Kalman filter models and achieving a goodness of fit greater than 0.95. Furthermore, the generalization of this algorithm was confirmed by its validation in two different scenarios.","PeriodicalId":93142,"journal":{"name":"Stats","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47232726","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: 2

On the Sampling Size for Inverse Sampling 关于反抽样的抽样大小

Stats Pub Date : 2022-11-15 DOI: 10.3390/stats5040067

Daniele Cuntrera, V. Falco, O. Giambalvo

引用次数: 1

Conditional Kaplan–Meier Estimator with Functional Covariates for Time-to-Event Data 时间到事件数据的函数协变量条件Kaplan-Meier估计

Stats Pub Date : 2022-11-10 DOI: 10.3390/stats5040066

Sudaraka Tholkage, Qi Zheng, K. B. Kulasekera

引用次数: 0

Selected Payback Statistical Contributions to Matrix/Linear Algebra: Some Counterflowing Conceptualizations 选择回报统计贡献矩阵/线性代数:一些逆流的概念

Stats Pub Date : 2022-11-09 DOI: 10.3390/stats5040065

D. Griffith

{"title":"Selected Payback Statistical Contributions to Matrix/Linear Algebra: Some Counterflowing Conceptualizations","authors":"D. Griffith","doi":"10.3390/stats5040065","DOIUrl":"https://doi.org/10.3390/stats5040065","url":null,"abstract":"Matrix/linear algebra continues bestowing benefits on theoretical and applied statistics, a practice it began decades ago (re Fisher used the word matrix in a 1941 publication), through a myriad of contributions, from recognition of a suite of matrix properties relevant to statistical concepts, to matrix specifications of linear and nonlinear techniques. Consequently, focused parts of matrix algebra are topics of several statistics books and journal articles. Contributions mostly have been unidirectional, from matrix/linear algebra to statistics. Nevertheless, statistics offers great potential for making this interface a bidirectional exchange point, the theme of this review paper. Not surprisingly, regression, the workhorse of statistics, provides one tool for such historically based recompence. Another prominent one is the mathematical matrix theory eigenfunction abstraction. A third is special matrix operations, such as Kronecker sums and products. A fourth is multivariable calculus linkages, especially arcane matrix/vector operators as well as the Jacobian term associated with variable transformations. A fifth, and the final idea this paper treats, is random matrices/vectors within the context of simulation, particularly for correlated data. These are the five prospectively reviewed discipline of statistics subjects capable of informing, inspiring, or otherwise furnishing insight to the far more general world of linear algebra.","PeriodicalId":93142,"journal":{"name":"Stats","volume":"147 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41311753","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

Bias-Corrected Maximum Likelihood Estimation and Bayesian Inference for the Process Performance Index Using Inverse Gaussian Distribution 基于逆高斯分布的过程性能指标的偏差校正最大似然估计和贝叶斯推断

Stats Pub Date : 2022-11-05 DOI: 10.3390/stats5040064

Tzong-Ru Tsai, Hua Xin, Yanqin Fan, Y. Lio

引用次数: 0

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