Stats Pub Date : 2023-11-11 DOI: 10.3390/stats6040077

Javier Linkolk López-Gonzales, Ana María Gómez Lamus, Romina Torres, Paulo Canas Rodrigues, Rodrigo Salas

引用次数: 0

On Underdispersed Count Kernels for Smoothing Probability Mass Functions 光滑概率质量函数的欠分散计数核

Stats Pub Date : 2023-11-04 DOI: 10.3390/stats6040076

Célestin C. Kokonendji, Sobom M. Somé, Youssef Esstafa, Marcelo Bourguignon

{"title":"On Underdispersed Count Kernels for Smoothing Probability Mass Functions","authors":"Célestin C. Kokonendji, Sobom M. Somé, Youssef Esstafa, Marcelo Bourguignon","doi":"10.3390/stats6040076","DOIUrl":"https://doi.org/10.3390/stats6040076","url":null,"abstract":"Only a few count smoothers are available for the widespread use of discrete associated kernel estimators, and their constructions lack systematic approaches. This paper proposes the mean dispersion technique for building count kernels. It is only applicable to count distributions that exhibit the underdispersion property, which ensures the convergence of the corresponding estimators. In addition to the well-known binomial and recent CoM-Poisson kernels, we introduce two new ones such the double Poisson and gamma-count kernels. Despite the challenging problem of obtaining explicit expressions, these kernels effectively smooth densities. Their good performances are pointed out from both numerical and comparative analyses, particularly for small and moderate sample sizes. The optimal tuning parameter is here investigated by integrated squared errors. Also, the added advantage of faster computation times is really very interesting. Thus, the overall accuracy of two newly suggested kernels appears to be between the two old ones. Finally, an application including a tail probability estimation on a real count data and some concluding remarks are given.","PeriodicalId":93142,"journal":{"name":"Stats","volume":"39 26","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135773638","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

Pivot Clustering to Minimize Error in Forecasting Aggregated Demand Streams Each Following an Autoregressive Moving Average Model 用自回归移动平均模型预测总需求流误差最小化的枢轴聚类

Stats Pub Date : 2023-11-02 DOI: 10.3390/stats6040075

Vladimir Kovtun, Avi Giloni, Clifford Hurvich, Sridhar Seshadri

引用次数: 0

Adjustment of Anticipatory Covariates in Retrospective Surveys: An Expected Likelihood Approach 回顾性调查中预期协变量的调整:一种预期似然方法

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

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