Permuted Inclusion Criterion- A Variable Selection Technique
➤ Gửi thông báo lỗi ⚠️ Báo cáo tài liệu vi phạmNội dung chi tiết: Permuted Inclusion Criterion- A Variable Selection Technique
Permuted Inclusion Criterion- A Variable Selection Technique
University of PennsylvaniaScholarlyCommonsPublicly Accessible Penn DissertationsSummer 2009Permuted Inclusion Criterion: A Variable Selection Techniqu Permuted Inclusion Criterion- A Variable Selection TechniqueueShaun LysenUniversity of Pennsylvania ■ Wharton School. slysen(8>gmail.comFollow this and additional works at: https://repository.upenn.edu/edissertationsÔ* Part of the Applied Statistics Commons, Multivariate Analysis Commons, Statistical MethodologyCommons, and the Statistical Models CommonsReco Permuted Inclusion Criterion- A Variable Selection Techniquemmended CitationLysen, Shaun, ’Permuted Inclusion Criterion: A Variable Selection Technique' (2009). Publicly AccessiblePenn Dissertations. 28.https:/Permuted Inclusion Criterion- A Variable Selection Technique
/repository.upenn.edu/edissertations/28This paper IS posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/28 For more information, University of PennsylvaniaScholarlyCommonsPublicly Accessible Penn DissertationsSummer 2009Permuted Inclusion Criterion: A Variable Selection Techniqu Permuted Inclusion Criterion- A Variable Selection Techniquechnique called the Permuted Inclusion Criterion (PIC) based on augmenting the predictor space X with a row-permuted version denoted xpi. We adopt the linear regression setup with n observations on p variables. Thus, our augmented space has p real predictors and p permuted predictors. This has many d Permuted Inclusion Criterion- A Variable Selection Techniqueesirable properties for variable selection. For example, this preserves relations between variables, e.g. squares and interactions and equates the momPermuted Inclusion Criterion- A Variable Selection Technique
ents and covariance structure of X and Xpi. More importantly, Xpi scales with the size of X. We motivate the idea with forward selection. The first tiUniversity of PennsylvaniaScholarlyCommonsPublicly Accessible Penn DissertationsSummer 2009Permuted Inclusion Criterion: A Variable Selection Techniqu Permuted Inclusion Criterion- A Variable Selection Techniqueg points. This has the added benefit of quantifying how certain we are about stopping Variable selection typically penalizes each additional variable by a prespecified amount. Our method uses a data adaptive penalty. We apply this method to simulated data and compare its predictive performance to ot Permuted Inclusion Criterion- A Variable Selection Techniqueher widely used criteria such as Cp. RIC. and the Lasso. Viewing PIC as a selection scheme for greedy algorithms, we extend the PIC to generalized linPermuted Inclusion Criterion- A Variable Selection Technique
ear regression (GLM) and classification and regression trees (CART).Degree TypeDissertationDegree NameDoctor of Philosophy (PhD)Graduate GroupStatistiUniversity of PennsylvaniaScholarlyCommonsPublicly Accessible Penn DissertationsSummer 2009Permuted Inclusion Criterion: A Variable Selection Techniqu Permuted Inclusion Criterion- A Variable Selection Techniquetivariate Analysis I Statistical Methodology I Statistical ModelsThis dissertation is available at ScholariyCommons’ httpsV/repository upenn edu/edissertations/28Permuted Inclusion Criterion: A Variable Selection TechniqueShaun LysenA DissertationinStatistics Permuted Inclusion Criterion- A Variable Selection TechniqueUniversity of PennsylvaniaScholarlyCommonsPublicly Accessible Penn DissertationsSummer 2009Permuted Inclusion Criterion: A Variable Selection TechniquGọi ngay
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