Results 61 to 70 of about 103,329 (151)
A dimension reduction assisted credit scoring method for big data with categorical features
Financial InnovationIn the past decade, financial institutions have invested significant efforts in the development of accurate analytical credit scoring models. The evidence suggests that even small improvements in the accuracy of existing credit-scoring models may ...
Tatjana Miljkovic, Pei Wang
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Discussion on ‘Review of sparse sufficient dimension reduction’
Statistical Theory and Related Fields, 2020 Xin Chen
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BuildingsAsphalt mixtures exhibit complex mechanical behaviors due to their multiphase internal structures. To provide better characterizations of asphalt pavements under various forms of potential distress, a two-dimensional (2D) finite element simulation based ...
Kai Li +4 more
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An Adaptive Domain Partitioning Technique for Meshfree-Type Methods
Journal of Applied Mathematics, 2012 An overlapping domain partitioning based on adapting nodes is presented for the meshless-type methods. The decomposition of the domain is carried out based on the distribution of the nodes produced rather than the geometry of the problem.
Kamal Shanazari
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Information preserving sufficient summaries for dimension reduction
Journal of Multivariate Analysis, 2013 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
David Nelson, Siamak Noorbaloochi
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FUNCTIONAL SUFFICIENT DIMENSION REDUCTION THROUGH AVERAGE FRÉCHET DERIVATIVES. [PDF]
Ann Stat, 2022 Lee KY, Li L.
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Pseudo likelihood and dimension reduction for data with nonignorable nonresponse
Statistical Theory and Related Fields, 2018 Tang et al. (2003. Analysis of multivariate missing data with nonignorable nonresponse. Biometrika, 90(4), 747–764) and Zhao & Shao (2015. Semiparametric pseudo-likelihoods in generalized linear models with nonignorable missing data.
Ji Chen, Bingying Xie, Jun Shao
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Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect. [PDF]
Stat Sin, 2021 Ghosh T, Ma Y, de Luna X.
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Markov Boundary Discovery with Ridge Regularized Linear Models
Journal of Causal Inference, 2016 Ridge regularized linear models (RRLMs), such as ridge regression and the SVM, are a popular group of methods that are used in conjunction with coefficient hypothesis testing to discover explanatory variables with a significant multivariate association ...
Strobl Eric V., Visweswaran Shyam
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Testing predictor contributions in sufficient dimension reduction
The Annals of Statistics, 2004 We develop tests of the hypothesis of no effect for selected predictors in regression, without assuming a model for the conditional distribution of the response given the predictors. Predictor effects need not be limited to the mean function and smoothing is not required.
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