Stats最新文献 - Book学术

An Optimal Design through a Compound Criterion for Integrating Extra Preference Information in a Choice Experiment: A Case Study on Moka Ground Coffee 通过复合标准在选择实验中整合额外偏好信息的最佳设计：摩卡研磨咖啡案例研究

Stats Pub Date : 2024-06-08 DOI: 10.3390/stats7020032

R. Berni, N. D. Nikiforova, Patrizia Pinelli

{"title":"An Optimal Design through a Compound Criterion for Integrating Extra Preference Information in a Choice Experiment: A Case Study on Moka Ground Coffee","authors":"R. Berni, N. D. Nikiforova, Patrizia Pinelli","doi":"10.3390/stats7020032","DOIUrl":"https://doi.org/10.3390/stats7020032","url":null,"abstract":"In this manuscript, we propose an innovative approach to studying consumers’ preferences for coffee, which integrates a choice experiment with consumer sensory tests and chemical analyses (caffeine contents obtained through a High-Performance Liquid Chromatography (HPLC) method). The same choice experiment is administered on two consecutive occasions, i.e., before and after the guided tasting session, to analyze the role of tasting and awareness about coffee composition in the consumers’ preferences. To this end, a Bayesian optimal design, based on a compound design criterion, is applied in order to build the choice experiment; the compound criterion allows for addressing two main issues related to the efficient estimation of the attributes and the evaluation of the sensorial part, e.g., the HPLC effects and the scores obtained through the consumer sensory test. All these elements, e.g., the attributes involved in the choice experiment, the scores obtained for each coffee through the sensory tests, and the HPLC quantitative evaluation of caffeine, are analyzed through suitable Random Utility Models. The initial results are promising, confirming the validity of the proposed approach.","PeriodicalId":503278,"journal":{"name":"Stats","volume":" 12","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141368325","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 Spatial Gaussian-Process Boosting Analysis of Socioeconomic Disparities in Wait-Listing of End-Stage Kidney Disease Patients across the United States 对全美终末期肾病患者候诊名单中社会经济差异的空间高斯过程提升分析

Stats Pub Date : 2024-06-07 DOI: 10.3390/stats7020031

Sounak Chakraborty, Tanujit Dey, Lingwei Xiang, Joel T. Adler

{"title":"A Spatial Gaussian-Process Boosting Analysis of Socioeconomic Disparities in Wait-Listing of End-Stage Kidney Disease Patients across the United States","authors":"Sounak Chakraborty, Tanujit Dey, Lingwei Xiang, Joel T. Adler","doi":"10.3390/stats7020031","DOIUrl":"https://doi.org/10.3390/stats7020031","url":null,"abstract":"In this study, we employed a novel approach of combining Gaussian processes (GPs) with boosting techniques to model the spatial variability inherent in End-Stage Kidney Disease (ESKD) data. Our use of the Gaussian processes boosting, or GPBoost, methodology underscores the efficacy of this hybrid method in capturing intricate spatial dynamics and enhancing predictive accuracy. Specifically, our analysis demonstrates a notable improvement in out-of-sample prediction accuracy regarding the percentage of the population remaining on the wait list within geographic regions. Furthermore, our investigation unveils race and gender-based factors that significantly influence patient wait-listing. By leveraging the GPBoost approach, we identify these pertinent factors, shedding light on the complex interplay between demographic variables and access to kidney transplantation services. Our findings underscore the imperative for a multifaceted strategy aimed at reducing spatial disparities in kidney transplant wait-listing. Key components of such an approach include mitigating gender disparities, bolstering access to healthcare services, fostering greater awareness of transplantation options, and dismantling structural barriers to care. By addressing these multifactorial challenges, we can strive towards a more equitable and inclusive landscape in kidney transplantation.","PeriodicalId":503278,"journal":{"name":"Stats","volume":" 14","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141371206","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

Combined Permutation Tests for Pairwise Comparison of Scale Parameters Using Deviances 使用偏差对标度参数进行成对比较的组合置换检验

Stats Pub Date : 2024-03-28 DOI: 10.3390/stats7020021

Scott J. Richter, Melinda H. McCann

引用次数: 0

Comments on the Bernoulli Distribution and Hilbe’s Implicit Extra-Dispersion 对伯努利分布和希尔贝隐式外扩散的评论

Stats Pub Date : 2024-03-05 DOI: 10.3390/stats7010016

Daniel A. Griffith

{"title":"Comments on the Bernoulli Distribution and Hilbe’s Implicit Extra-Dispersion","authors":"Daniel A. Griffith","doi":"10.3390/stats7010016","DOIUrl":"https://doi.org/10.3390/stats7010016","url":null,"abstract":"For decades, conventional wisdom maintained that binary 0–1 Bernoulli random variables cannot contain extra-binomial variation. Taking an unorthodox stance, Hilbe actively disagreed, especially for correlated observation instances, arguing that the universally adopted diagnostic Pearson or deviance dispersion statistics are insensitive to a variance anomaly in a binary context, and hence simply fail to detect it. However, having the intuition and insight to sense the existence of this departure from standard mathematical statistical theory, but being unable to effectively isolate it, he classified this particular over-/under-dispersion phenomenon as implicit. This paper explicitly exposes his hidden quantity by demonstrating that the variance in/deflation it represents occurs in an underlying predicted beta random variable whose real number values are rounded to their nearest integers to convert to a Bernoulli random variable, with this discretization masking any materialized extra-Bernoulli variation. In doing so, asymptotics linking the beta-binomial and Bernoulli distributions show another conventional wisdom misconception, namely a mislabeling substitution involving the quasi-Bernoulli random variable; this undeniably is not a quasi-likelihood situation. A public bell pepper disease dataset exhibiting conspicuous spatial autocorrelation furnishes empirical examples illustrating various features of this advocated proposition.","PeriodicalId":503278,"journal":{"name":"Stats","volume":"107 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140079554","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

Two-Stage Limited-Information Estimation for Structural Equation Models of Round-Robin Variables 循环变量结构方程模型的两阶段有限信息估计

Stats Pub Date : 2024-02-28 DOI: 10.3390/stats7010015

Terrence D. Jorgensen, Aditi M. Bhangale, Yves Rosseel

{"title":"Two-Stage Limited-Information Estimation for Structural Equation Models of Round-Robin Variables","authors":"Terrence D. Jorgensen, Aditi M. Bhangale, Yves Rosseel","doi":"10.3390/stats7010015","DOIUrl":"https://doi.org/10.3390/stats7010015","url":null,"abstract":"We propose and demonstrate a new two-stage maximum likelihood estimator for parameters of a social relations structural equation model (SR-SEM) using estimated summary statistics (Σ^) as data, as well as uncertainty about Σ^ to obtain robust inferential statistics. The SR-SEM is a generalization of a traditional SEM for round-robin data, which have a dyadic network structure (i.e., each group member responds to or interacts with each other member). Our two-stage estimator is developed using similar logic as previous two-stage estimators for SEM, developed for application to multilevel data and multiple imputations of missing data. We demonstrate out estimator on a publicly available data set from a 2018 publication about social mimicry. We employ Markov chain Monte Carlo estimation of Σ^ in Stage 1, implemented using the R package rstan. In Stage 2, the posterior mean estimates of Σ^ are used as input data to estimate SEM parameters with the R package lavaan. The posterior covariance matrix of estimated Σ^ is also calculated so that lavaan can use it to calculate robust standard errors and test statistics. Results are compared to full-information maximum likelihood (FIML) estimation of SR-SEM parameters using the R package srm. We discuss how differences between estimators highlight the need for future research to establish best practices under realistic conditions (e.g., how to specify empirical Bayes priors in Stage 1), as well as extensions that would make 2-stage estimation particularly advantageous over single-stage FIML.","PeriodicalId":503278,"journal":{"name":"Stats","volume":"69 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140421435","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

New Vessel Extraction Method by Using Skew Normal Distribution for MRA Images 利用偏斜正态分布提取 MRA 图像中血管的新方法

Stats Pub Date : 2024-02-23 DOI: 10.3390/stats7010013

Tohid Bahrami, H. J. Khamnei, M. Lakestani, B. G. Kibria

{"title":"New Vessel Extraction Method by Using Skew Normal Distribution for MRA Images","authors":"Tohid Bahrami, H. J. Khamnei, M. Lakestani, B. G. Kibria","doi":"10.3390/stats7010013","DOIUrl":"https://doi.org/10.3390/stats7010013","url":null,"abstract":"Vascular-related diseases pose significant public health challenges and are a leading cause of mortality and disability. Understanding the complex structure of the vascular system and its processes is crucial for addressing these issues. Recent advancements in medical imaging technology have enabled the generation of high-resolution 3D images of vascular structures, leading to a diverse array of methods for vascular extraction. While previous research has often assumed a normal distribution of image data, this paper introduces a novel vessel extraction method that utilizes the skew normal distribution for more accurate probability distribution modeling. The proposed method begins with a preprocessing step to enhance vessel structures and reduce noise in Magnetic Resonance Angiography (MRA) images. The skew normal distribution, known for its ability to model skewed data, is then employed to characterize the intensity distribution of vessels. By estimating the parameters of the skew normal distribution using the Expectation-Maximization (EM) algorithm, the method effectively separates vessel pixels from the background and non-vessel regions. To extract vessels, a thresholding technique is applied based on the estimated skew normal distribution parameters. This segmentation process enables accurate vessel extraction, particularly in detecting thin vessels and enhancing the delineation of vascular edges with low contrast. Experimental evaluations on a diverse set of MRA images demonstrate the superior performance of the proposed method compared to previous approaches in terms of accuracy and computational efficiency. The presented vessel extraction method holds promise for improving the diagnosis and treatment of vascular-related diseases. By leveraging the skew normal distribution, it provides accurate and efficient vessel segmentation, contributing to the advancement of vascular imaging in the field of medical image analysis.","PeriodicalId":503278,"journal":{"name":"Stats","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140435559","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

Utility in Time Description in Priority Best–Worst Discrete Choice Models: An Empirical Evaluation Using Flynn’s Data 优先权最佳-最差离散选择模型中时间描述的效用：利用弗林数据进行实证评估

Stats Pub Date : 2024-02-19 DOI: 10.3390/stats7010012

Sasanka Adikari, N. Diawara

{"title":"Utility in Time Description in Priority Best–Worst Discrete Choice Models: An Empirical Evaluation Using Flynn’s Data","authors":"Sasanka Adikari, N. Diawara","doi":"10.3390/stats7010012","DOIUrl":"https://doi.org/10.3390/stats7010012","url":null,"abstract":"Discrete choice models (DCMs) are applied in many fields and in the statistical modelling of consumer behavior. This paper focuses on a form of choice experiment, best–worst scaling in discrete choice experiments (DCEs), and the transition probability of a choice of a consumer over time. The analysis was conducted by using simulated data (choice pairs) based on data from Flynn’s (2007) ‘Quality of Life Experiment’. Most of the traditional approaches assume the choice alternatives are mutually exclusive over time, which is a questionable assumption. We introduced a new copula-based model (CO-CUB) for the transition probability, which can handle the dependent structure of best–worst choices while applying a very practical constraint. We used a conditional logit model to calculate the utility at consecutive time points and spread it to future time points under dynamic programming. We suggest that the CO-CUB transition probability algorithm is a novel way to analyze and predict choices in future time points by expressing human choice behavior. The numerical results inform decision making, help formulate strategy and learning algorithms under dynamic utility in time for best–worst DCEs.","PeriodicalId":503278,"journal":{"name":"Stats","volume":"21 20","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140451012","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