Statistical Analysis and Data Mining: The ASA Data Science Journal最新文献_第2页

A deep learning factor analysis model based on importance‐weighted variational inference and normalizing flow priors: Evaluation within a set of multidimensional performance assessments in youth elite soccer players 基于重要性加权变分推理和归一化流先验的深度学习因素分析模型:青少年精英足球运动员多维表现评估中的评价

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2023-06-30 DOI: 10.1002/sam.11632

P. Kilian, Daniel Leyhr, Christopher J. Urban, O. Höner, A. Kelava

{"title":"A deep learning factor analysis model based on importance‐weighted variational inference and normalizing flow priors: Evaluation within a set of multidimensional performance assessments in youth elite soccer players","authors":"P. Kilian, Daniel Leyhr, Christopher J. Urban, O. Höner, A. Kelava","doi":"10.1002/sam.11632","DOIUrl":"https://doi.org/10.1002/sam.11632","url":null,"abstract":"Exploratory factor analysis is a widely used framework in the social and behavioral sciences. Since measurement errors are always present in human behavior data, latent factors, generating the observed data, are important to identify. While most factor analysis methods rely on linear relationships in the data‐generating process, deep learning models can provide more flexible modeling approaches. However, two problems need to be addressed. First, for interpretation, scaling assumptions are required, which can be (at least) cumbersome for deep generative models. Second, deep generative models are typically not identifiable, which is required in order to identify the underlying latent constructs. We developed a model that uses a variational autoencoder as an estimator for a complex factor analysis model based on importance‐weighted variational inference. In order to receive interpretable results and an identified model, we use a linear factor model with identification constraints in the measurement model. To maintain the flexibility of the model, we use normalizing flow latent priors. Within the evaluation of performance measures in a talent development program in soccer, we found more clarity in the separation of the identified underlying latent dimensions with our models compared to traditional PCA analyses.","PeriodicalId":342679,"journal":{"name":"Statistical Analysis and Data Mining: The ASA Data Science Journal","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114061712","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

Categorical classifiers in multiclass classification with imbalanced datasets 不平衡数据集下多类分类的分类器

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2023-05-18 DOI: 10.1002/sam.11624

M. Carpita, Silvia Golia

引用次数: 0

A new parametric approach to gender gap with application to EUSILC data in Poland and Italy 对性别差距的新参数化方法及其在波兰和意大利的应用

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2023-05-15 DOI: 10.1002/sam.11623

F. Greselin, Alina Jȩdrzejczak, Kamila Trzcińska

{"title":"A new parametric approach to gender gap with application to EUSILC data in Poland and Italy","authors":"F. Greselin, Alina Jȩdrzejczak, Kamila Trzcińska","doi":"10.1002/sam.11623","DOIUrl":"https://doi.org/10.1002/sam.11623","url":null,"abstract":"Real income distribution comparisons are of interest to policy makers across European countries. Nowadays, a crucial component of income inequality remains the discrepancy between men and women, often called the gender gap. Since the gender gap is related to the whole distribution of incomes in a population, popular single metrics are not adequate, and previous studies applied the relative distribution method, a non‐parametric approach to the comparison of distributions. Here, we propose a parametric approach for estimating the relative distribution. Then we extend it to assess the impact of selected covariates—related to the personal characteristics of the samples—on the existing gender gap in both countries. In more detail, models for income were fitted to empirical data from Poland and Italy, from the European Survey of Income and Living Conditions (wave 2018). Afterwards, their parameters were employed to obtain the estimates of relative distribution characteristics. The methods applied in the study turned out to be relevant to describe the gender gap over the entire income range. Finally, the results of the empirical analysis are discussed to reveal similarities and substantial differences between the countries.","PeriodicalId":342679,"journal":{"name":"Statistical Analysis and Data Mining: The ASA Data Science Journal","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122848542","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

Semiparametric detection of changepoints in location, scale, and copula 半参数检测的变化点在位置，规模，和copula

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2023-04-29 DOI: 10.1002/sam.11622

Gaurav Agarwal, I. Eckley, P. Fearnhead

引用次数: 0

A new formulation of sparse multiple kernel k$$ k $$ ‐means clustering and its applications 稀疏多核k $$ k $$ -均值聚类的新公式及其应用

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2023-04-18 DOI: 10.1002/sam.11621

Wentao Qu, Xianchao Xiu, Jun Sun, Lingchen Kong

引用次数: 0

Association rules and decision rules 关联规则和决策规则

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2023-04-14 DOI: 10.1002/sam.11620

A. Mokkadem, M. Pelletier, Louis Raimbault

引用次数: 0

Simplicial depth: Characterization and reconstruction 单纯深度:表征与重构

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2023-04-12 DOI: 10.1002/sam.11618

P. Laketa, Stanislav Nagy

{"title":"Simplicial depth: Characterization and reconstruction","authors":"P. Laketa, Stanislav Nagy","doi":"10.1002/sam.11618","DOIUrl":"https://doi.org/10.1002/sam.11618","url":null,"abstract":"Statistical depth functions have been designed with the intention of extending nonparametric inference toward multivariate setups. As such, the depths should serve as multivariate analogues of the quantile functions known from the analysis of real‐valued data. The so‐called characterization and reconstruction questions are among the fundamental open problems of the contemporary depth research. Roughly speaking, they ask: (a) Is it is possible that two different datasets, or more generally, two different probability distributions, correspond to identical depths, or does the depth function uniquely characterize the underlying distribution? (b) Knowing a depth function, can we reconstruct the corresponding distribution? For any given depth to constitute a fully‐fledged alternative to the quantile function, the depth must characterize wide classes of probability measures, and these measures must be simple to recover from their depths. We investigate these characterization/reconstruction questions for the classical simplicial depth for multivariate data. We show that, under mild conditions, datasets (represented by measures putting equal mass 1/n$$ 1/n $$ to each datum in a dataset of size n$$ n $$ ) and atomic measures are characterized by, and can be easily reconstructed from, their simplicial depth.","PeriodicalId":342679,"journal":{"name":"Statistical Analysis and Data Mining: The ASA Data Science Journal","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125919102","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

Share density‐based clustering of income data 基于份额密度的收入数据聚类

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2023-04-03 DOI: 10.1002/sam.11619

Francesca Condino

引用次数: 0

Lq regularization for fair artificial intelligence robust to covariate shift 对协变量移位具有鲁棒性的公平人工智能的Lq正则化

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2023-02-22 DOI: 10.1002/sam.11616

Seonghyeon Kim, Sara Kim, Kunwoong Kim, Yongdai Kim

{"title":"Lq regularization for fair artificial intelligence robust to covariate shift","authors":"Seonghyeon Kim, Sara Kim, Kunwoong Kim, Yongdai Kim","doi":"10.1002/sam.11616","DOIUrl":"https://doi.org/10.1002/sam.11616","url":null,"abstract":"It is well recognized that historical biases exist in training data against a certain sensitive group (e.g., non‐White, women) which are socially unacceptable, and these unfair biases are inherited in trained artificial intelligence (AI) models. Various learning algorithms have been proposed to remove or alleviate unfair biases in trained AI models. In this paper, we consider another type of bias in training data so‐called covariate shift in view of fair AI. Here, covariate shift means that training data do not represent the population of interest well. Covariate shift occurs when special sampling designs (e.g., stratified sampling) are used when collecting training data, or the population where training data are collected is different from the population of interest. When covariate shift exists, fair AI models on training data may not be fair in test data. To ensure fairness on test data, we develop computationally efficient learning algorithms robust to covariate shifts. In particular, we propose a robust fairness constraint based on the Lq norm which is a generic algorithm to be applied to various fairness AI problems without much hampering. By analyzing multiple benchmark datasets, we show that our proposed robust fairness AI algorithm improves existing fair AI algorithms much in terms of the fairness‐accuracy tradeoff to covariate shift and has significant computational advantages compared to other robust fair AI algorithms.","PeriodicalId":342679,"journal":{"name":"Statistical Analysis and Data Mining: The ASA Data Science Journal","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134429513","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

Buckley–James estimation of generalized additive accelerated lifetime model with ultrahigh‐dimensional data 超高维数据下广义加性加速寿命模型的Buckley-James估计

Statistical Analysis and Data Mining: The ASA Data Science Journal Pub Date : 2023-02-22 DOI: 10.1002/sam.11615

Zichang Li, Xuejing Zhao

{"title":"Buckley–James estimation of generalized additive accelerated lifetime model with ultrahigh‐dimensional data","authors":"Zichang Li, Xuejing Zhao","doi":"10.1002/sam.11615","DOIUrl":"https://doi.org/10.1002/sam.11615","url":null,"abstract":"High‐dimensional covariates in lifetime data is a challenge in survival analysis, especially in gene expression profile. The objective of this paper is to propose an efficient algorithm to extend the generalized additive model to survival data with high‐dimensional covariates. The algorithm is combined of generalized additive (GAM) model and Buckley–James estimation, which makes a nonparametric extension to the nonlinear model, where the GAM is exploited to illustrate the nonlinear effect of the covariates and the Buckley–James estimation is used to address the regression model with right‐censored response. In addition, we use maximal‐information‐coefficient (MIC)‐type variable screening and weighted p‐value to reduce dimension in high‐dimensional situations. The performance of the proposed algorithm is compared with the three benchmark models: Cox proportional hazards regression model, random survival forest, and BJ‐AFT on a simulated dataset and two real survival datasets. The results, evaluated by concordance index (C‐index) as well as modified mean squared error (mMSE), illustrated the superiority of the proposed algorithm.","PeriodicalId":342679,"journal":{"name":"Statistical Analysis and Data Mining: The ASA Data Science Journal","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133964222","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