REP
发表于 2025-3-25 05:14:07
Statistical Models and Methods for Data Science978-3-031-30164-3Series ISSN 1431-8814 Series E-ISSN 2198-3321
Baffle
发表于 2025-3-25 08:49:01
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LATHE
发表于 2025-3-25 15:15:39
https://doi.org/10.1007/978-3-031-30164-3Classification; Data Analysis; Data Science; Statistical Learning; Machine Learning; Statistical Models; M
四牛在弯曲
发表于 2025-3-25 17:41:40
,Optimal Coding of High-Cardinality Categorical Data in Machine Learning,d several approaches have been suggested in the literature. This article proposes a method for analyzing categorical variables with neural networks. Both a supervised and unsupervised approaches were considered, in which the variables can have high cardinality. Some simulated data applications illustrate the interest in the proposal.
acrimony
发表于 2025-3-25 22:00:11
,Hierarchical Clustering of Income Data Based on Share Densities,proposed to evaluate the discrepancy across share densities, and a hierarchical clustering algorithm is employed to find the family of partitions. Results regarding data from the Survey on Households Income and Wealth (SHIW) by Bank of Italy are shown.
掺假
发表于 2025-3-26 03:49:29
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META
发表于 2025-3-26 06:11:34
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性学院
发表于 2025-3-26 11:58:27
,Robust Response Transformations for Generalized Additive Models via Additivity and Variance StabiliAM). We describe and illustrate robust methods for the non-parametric transformation of the response and for estimation of the terms in the model and report the results of a simulation study comparing our robust procedure with AVAS. We illustrate the efficacy of our procedure through a simulation study and the analysis of real data.
难取悦
发表于 2025-3-26 14:47:21
A Random-Coefficients Analysis with a Multivariate Random-Coefficients Linear Model,ace by a weighted least-squares closed-form solution, starting from the standardized multivariate best linear predictors. The application shows the effect of the linear dependence of the random effects in the space of the model covariates.
connoisseur
发表于 2025-3-26 17:39:39
Conference proceedings 2023Numerous topics are covered, ranging from statistical inference and modelling to clustering and factorial methods, and from directional data analysis to time series analysis and small area estimation. The applications deal with new developments in a variety of fields, including medicine, finance, en