Stats Pub Date : 2023-11-01 DOI: 10.3390/stats6040074

Gebrenegus Ghilagaber, Rolf Larsson

引用次数: 0

Implementation Aspects in Invariance Alignment 不变性对齐中的实现方面

Stats Pub Date : 2023-10-25 DOI: 10.3390/stats6040073

Alexander Robitzsch

{"title":"Implementation Aspects in Invariance Alignment","authors":"Alexander Robitzsch","doi":"10.3390/stats6040073","DOIUrl":"https://doi.org/10.3390/stats6040073","url":null,"abstract":"In social sciences, multiple groups, such as countries, are frequently compared regarding a construct that is assessed using a number of items administered in a questionnaire. The corresponding scale is assessed with a unidimensional factor model involving a latent factor variable. To enable a comparison of the mean and standard deviation of the factor variable across groups, identification constraints on item intercepts and factor loadings must be imposed. Invariance alignment (IA) provides such a group comparison in the presence of partial invariance (i.e., a minority of item intercepts and factor loadings are allowed to differ across groups). IA is a linking procedure that separately fits a factor model in each group in the first step. In the second step, a linking of estimated item intercepts and factor loadings is conducted using a robust loss function L0.5. The present article discusses implementation alternatives in IA. It compares the default L0.5 loss function with Lp with other values of the power p between 0 and 1. Moreover, the nondifferentiable Lp loss functions are replaced with differentiable approximations in the estimation of IA that depend on a tuning parameter ε (such as, e.g., ε=0.01). The consequences of choosing different values of ε are discussed. Moreover, this article proposes the L0 loss function with a differentiable approximation for IA. Finally, it is demonstrated that the default linking function in IA introduces bias in estimated means and standard deviations if there is noninvariance in factor loadings. Therefore, an alternative linking function based on logarithmized factor loadings is examined for estimating factor means and standard deviations. The implementation alternatives are compared through three simulation studies. It turned out that the linking function for factor loadings in IA should be replaced by the alternative involving logarithmized factor loadings. Furthermore, the default L0.5 loss function is inferior to the newly proposed L0 loss function regarding the bias and root mean square error of factor means and standard deviations.","PeriodicalId":93142,"journal":{"name":"Stats","volume":"64 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135168392","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

Asymptotic Relative Efficiency of Parametric and Nonparametric Survival Estimators 参数和非参数生存估计量的渐近相对效率

Stats Pub Date : 2023-10-25 DOI: 10.3390/stats6040072

Szilárd Nemes

引用次数: 0

The Semi-Hyperbolic Distribution and Its Applications 半双曲分布及其应用

Stats Pub Date : 2023-10-21 DOI: 10.3390/stats6040071

Roman V. Ivanov

引用次数: 0

An Archimedean Copulas-Based Approach for m-Consecutive-k-Out-of-n: F Systems with Exchangeable Components 具有可交换分量的m-连续-k- of-n: F系统的阿基米德copulas方法

Stats Pub Date : 2023-10-20 DOI: 10.3390/stats6040070

Ioannis S. Triantafyllou

引用次数: 0

Exploring the Consumer Acceptance of Nano Clothing Using a PLS-SEM Analysis 利用PLS-SEM分析探索消费者对纳米服装的接受程度

Stats Pub Date : 2023-10-19 DOI: 10.3390/stats6040069

Andreea-Ionela Puiu, Rodica Ianole-Călin, Elena Druică

引用次数: 0

Comparison between Two Algorithms for Computing the Weighted Generalized Affinity Coefficient in the Case of Interval Data 区间数据加权广义关联系数两种计算算法的比较

Stats Pub Date : 2023-10-13 DOI: 10.3390/stats6040068

Áurea Sousa, Osvaldo Silva, Leonor Bacelar-Nicolau, João Cabral, Helena Bacelar-Nicolau

{"title":"Comparison between Two Algorithms for Computing the Weighted Generalized Affinity Coefficient in the Case of Interval Data","authors":"Áurea Sousa, Osvaldo Silva, Leonor Bacelar-Nicolau, João Cabral, Helena Bacelar-Nicolau","doi":"10.3390/stats6040068","DOIUrl":"https://doi.org/10.3390/stats6040068","url":null,"abstract":"From the affinity coefficient between two discrete probability distributions proposed by Matusita, Bacelar-Nicolau introduced the affinity coefficient in a cluster analysis context and extended it to different types of data, including for the case of complex and heterogeneous data within the scope of symbolic data analysis (SDA). In this study, we refer to the most significant partitions obtained using the hierarchical cluster analysis (h.c.a.) of two well-known datasets that were taken from the literature on complex (symbolic) data analysis. h.c.a. is based on the weighted generalized affinity coefficient for the case of interval data and on probabilistic aggregation criteria from a VL parametric family. To calculate the values of this coefficient, two alternative algorithms were used and compared. Both algorithms were able to detect clusters of macrodata (aggregated data into groups of interest) that were consistent and consonant with those reported in the literature, but one performed better than the other in some specific cases. Moreover, both approaches allow for the treatment of large microdatabases (non-aggregated data) after their transformation into macrodata from the huge microdata.","PeriodicalId":93142,"journal":{"name":"Stats","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135917806","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

On the Vector Representation of Characteristic Functions 关于特征函数的向量表示

Stats Pub Date : 2023-10-10 DOI: 10.3390/stats6040067

Wolf-Dieter Richter

引用次数: 0

Newcomb–Benford’s Law in Neuromuscular Transmission: Validation in Hyperkalemic Conditions 神经肌肉传递中的Newcomb-Benford定律:在高钾血症条件下的验证

Stats Pub Date : 2023-10-09 DOI: 10.3390/stats6040066

Adriano Silva, Sergio Floquet, Ricardo Lima

{"title":"Newcomb–Benford’s Law in Neuromuscular Transmission: Validation in Hyperkalemic Conditions","authors":"Adriano Silva, Sergio Floquet, Ricardo Lima","doi":"10.3390/stats6040066","DOIUrl":"https://doi.org/10.3390/stats6040066","url":null,"abstract":"Recently, we demonstrated the validity of the anomalous numbers law, known as Newcomb–Benford’s law, in mammalian neuromuscular transmission, considering different extracellular calcium. The present work continues to examine how changes in extracellular physiological artificial solution can modulate the first digit law in the context of spontaneous acetylcholine release at the neuromuscular junction. Using intracellular measurements, we investigated if the intervals of miniature potentials collected at the neuromuscular junction obey the law in a hyperkalemic environment. When bathed in standard Ringer’s solution, the experiments provided 22,582 intervals extracted from 14 recordings. On the other hand, 690,385 intervals were obtained from 12 experiments in a modified Ringer’s solution containing a high potassium concentration. The analysis showed that the intervals, harvested from recordings at high potassium, satisfactorily obeyed Newcomb–Benford’s law. Furthermore, our data allowed us to uncover a conformity fluctuation as a function of the number of intervals of the miniature potentials. Finally, we discuss the biophysical implications of the present findings.","PeriodicalId":93142,"journal":{"name":"Stats","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135094154","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

Computationally Efficient Poisson Time-Varying Autoregressive Models through Bayesian Lattice Filters 基于贝叶斯格滤波器的计算高效泊松时变自回归模型

Stats Pub Date : 2023-10-09 DOI: 10.3390/stats6040065

Yuelei Sui, Scott H. Holan, Wen-Hsi Yang

引用次数: 0

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