Stats Pub Date : 2023-06-19 DOI: 10.3390/stats6020045

B. Oluyede, Thatayaone Moakofi

引用次数: 0

Modeling Model Misspecification in Structural Equation Models 结构方程模型中的建模模型错误

Stats Pub Date : 2023-06-14 DOI: 10.3390/stats6020044

A. Robitzsch

引用次数: 2

Area under the Curve as an Alternative to Latent Growth Curve Modeling When Assessing the Effects of Predictor Variables on Repeated Measures of a Continuous Dependent Variable 当评估预测变量对连续因变量重复测量的影响时，曲线下面积作为潜在增长曲线模型的替代方法

Stats Pub Date : 2023-05-25 DOI: 10.3390/stats6020043

Daniel Rodriguez

{"title":"Area under the Curve as an Alternative to Latent Growth Curve Modeling When Assessing the Effects of Predictor Variables on Repeated Measures of a Continuous Dependent Variable","authors":"Daniel Rodriguez","doi":"10.3390/stats6020043","DOIUrl":"https://doi.org/10.3390/stats6020043","url":null,"abstract":"Researchers conducting longitudinal data analysis in psychology and the behavioral sciences have several statistical methods to choose from, most of which either require specialized software to conduct or advanced knowledge of statistical methods to inform the selection of the correct model options (e.g., correlation structure). One simple alternative to conventional longitudinal data analysis methods is to calculate the area under the curve (AUC) from repeated measures and then use this new variable in one’s model. The present study assessed the relative efficacy of two AUC measures: the AUC with respect to the ground (AUC-g) and the AUC with respect to the increase (AUC-i) in comparison to latent growth curve modeling (LGCM), a popular repeated measures data analysis method. Using data from the ongoing Panel Study of Income Dynamics (PSID), we assessed the effects of four predictor variables on repeated measures of social anxiety, using both the AUC and LGCM. We used the full information maximum likelihood (FIML) method to account for missing data in LGCM and multiple imputation to account for missing data in the calculation of both AUC measures. Extracting parameter estimates from these models, we next conducted Monte Carlo simulations to assess the parameter bias and power (two estimates of performance) of both methods in the same models, with sample sizes ranging from 741 to 50. The results using both AUC measures in the initial models paralleled those of LGCM, particularly with respect to the LGCM baseline. With respect to the simulations, both AUC measures preformed as well or even better than LGCM in all sample sizes assessed. These results suggest that the AUC may be a viable alternative to LGCM, especially for researchers with less access to the specialized software necessary to conduct LGCM.","PeriodicalId":93142,"journal":{"name":"Stats","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43201393","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 Extended Weibull Distribution with Application to Influenza and Hepatitis Data 一个新的扩展威布尔分布及其在流感和肝炎数据中的应用

Stats Pub Date : 2023-05-19 DOI: 10.3390/stats6020042

G. Cordeiro, Elisângela C. Biazatti, L. H. de Santana

引用次数: 0

Interval-Censored Regression with Non-Proportional Hazards with Applications 非比例风险区间截尾回归及其应用

Stats Pub Date : 2023-05-17 DOI: 10.3390/stats6020041

F. Prataviera, E. M. Hashimoto, E. M. Ortega, T. Savian, G. Cordeiro

引用次数: 0

Climate Change: Linear and Nonlinear Causality Analysis 气候变化:线性与非线性因果分析

Stats Pub Date : 2023-05-15 DOI: 10.3390/stats6020040

Jiecheng Song, Merry H. Ma

{"title":"Climate Change: Linear and Nonlinear Causality Analysis","authors":"Jiecheng Song, Merry H. Ma","doi":"10.3390/stats6020040","DOIUrl":"https://doi.org/10.3390/stats6020040","url":null,"abstract":"The goal of this study is to detect linear and nonlinear causal pathways toward climate change as measured by changes in global mean surface temperature and global mean sea level over time using a data-based approach in contrast to the traditional physics-based models. Monthly data on potential climate change causal factors, including greenhouse gas concentrations, sunspot numbers, humidity, ice sheets mass, and sea ice coverage, from January 2003 to December 2021, have been utilized in the analysis. We first applied the vector autoregressive model (VAR) and Granger causality test to gauge the linear Granger causal relationships among climate factors. We then adopted the vector error correction model (VECM) as well as the autoregressive distributed lag model (ARDL) to quantify the linear long-run equilibrium and the linear short-term dynamics. Cointegration analysis has also been adopted to examine the dual directional Granger causalities. Furthermore, in this work, we have presented a novel pipeline based on the artificial neural network (ANN) and the VAR and ARDL models to detect nonlinear causal relationships embedded in the data. The results in this study indicate that the global sea level rise is affected by changes in ice sheet mass (both linearly and nonlinearly), global mean temperature (nonlinearly), and the extent of sea ice coverage (nonlinearly and weakly); whereas the global mean temperature is affected by the global surface mean specific humidity (both linearly and nonlinearly), greenhouse gas concentration as measured by the global warming potential (both linearly and nonlinearly) and the sunspot number (only nonlinearly and weakly). Furthermore, the nonlinear neural network models tend to fit the data closer than the linear models as expected due to the increased parameter dimension of the neural network models. Given that the information criteria are not generally applicable to the comparison of neural network models and statistical time series models, our next step is to examine the robustness and compare the forecast accuracy of these two models using the soon-available 2022 monthly data.","PeriodicalId":93142,"journal":{"name":"Stats","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43183566","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

Big Data Analytics and Machine Learning in Supply Chain 4.0: A Literature Review 供应链4.0中的大数据分析和机器学习:文献综述

Stats Pub Date : 2023-05-05 DOI: 10.3390/stats6020038

Elena Barzizza, Nicolò Biasetton, R. Ceccato, L. Salmaso

{"title":"Big Data Analytics and Machine Learning in Supply Chain 4.0: A Literature Review","authors":"Elena Barzizza, Nicolò Biasetton, R. Ceccato, L. Salmaso","doi":"10.3390/stats6020038","DOIUrl":"https://doi.org/10.3390/stats6020038","url":null,"abstract":"Owing to the development of the technologies of Industry 4.0, recent years have witnessed the emergence of a new concept of supply chain management, namely Supply Chain 4.0 (SC 4.0). Huge investments in information technology have enabled manufacturers to trace the intangible flow of information, but instruments are required to take advantage of the available data sources: big data analytics (BDA) and machine learning (ML) represent important tools for this task. Use of advanced technologies can improve supply chain performances and support reaching strategic goals, but their implementation is challenging in supply chain management. The aim of this study was to understand the main benefits, challenges, and areas of application of BDA and ML in SC 4.0 as well as to understand the BDA and ML techniques most commonly used in the field, with a particular focus on nonparametric techniques. To this end, we carried out a literature review. From our analysis, we identified three main gaps, namely, the need for appropriate analytical tools to manage challenging data configurations; the need for a more reliable link with practice; the need for instruments to select the most suitable BDA or ML techniques. As a solution, we suggest and comment on two viable solutions: nonparametric statistics, and sentiment analysis and clustering.","PeriodicalId":93142,"journal":{"name":"Stats","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43796258","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

Game-Theoretic Models of Coopetition in Cournot Oligopoly 古诺寡头垄断中合作的博弈论模型

Stats Pub Date : 2023-05-04 DOI: 10.3390/stats6020037

G. Ougolnitsky, A. Korolev

引用次数: 0

Causal Inference in Threshold Regression and the Neural Network Extension (TRNN) 阈值回归与神经网络扩展(TRNN)中的因果推理

Stats Pub Date : 2023-04-28 DOI: 10.3390/stats6020036

Yiming Chen, P. Smith, Mei-Ling Ting Lee

{"title":"Causal Inference in Threshold Regression and the Neural Network Extension (TRNN)","authors":"Yiming Chen, P. Smith, Mei-Ling Ting Lee","doi":"10.3390/stats6020036","DOIUrl":"https://doi.org/10.3390/stats6020036","url":null,"abstract":"The first-hitting-time based model conceptualizes a random process for subjects’ latent health status. The time-to-event outcome is modeled as the first hitting time of the random process to a pre-specified threshold. Threshold regression with linear predictors has numerous benefits in causal survival analysis, such as the estimators’ collapsibility. We propose a neural network extension of the first-hitting-time based threshold regression model. With the flexibility of neural networks, the extended threshold regression model can efficiently capture complex relationships among predictors and underlying health processes while providing clinically meaningful interpretations, and also tackle the challenge of high-dimensional inputs. The proposed neural network extended threshold regression model can further be applied in causal survival analysis, such as performing as the Q-model in G-computation. More efficient causal estimations are expected given the algorithm’s robustness. Simulations were conducted to validate estimator collapsibility and threshold regression G-computation. The performance of the neural network extended threshold regression model is also illustrated by using simulated and real high-dimensional data from an observational study.","PeriodicalId":93142,"journal":{"name":"Stats","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44349710","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

Adaptations on the Use of p-Values for Statistical Inference: An Interpretation of Messages from Recent Public Discussions p值在统计推断中的应用:对近期公开讨论信息的解读

Stats Pub Date : 2023-04-25 DOI: 10.3390/stats6020035

E. Verykouki, Chris Nakas

引用次数: 0

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