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Comparison of nonlinear curves and surfaces.
Zhao, Shi; Bakoyannis, Giorgos; Lourens, Spencer; Tu, Wanzhu.
Afiliación
  • Zhao S; Department of Biostatistics, Indiana University Fairbanks School of Public Health and Indiana University School of Medicine, Indianapolis, Indiana 46202, U.S.A.
  • Bakoyannis G; Department of Biostatistics, Indiana University Fairbanks School of Public Health and Indiana University School of Medicine, Indianapolis, Indiana 46202, U.S.A.
  • Lourens S; CliftonLarsonAllen LLP.
  • Tu W; Department of Biostatistics, Indiana University Fairbanks School of Public Health and Indiana University School of Medicine, Indianapolis, Indiana 46202, U.S.A.
Article en En | MEDLINE | ID: mdl-36688204
ABSTRACT
Estimation of nonlinear curves and surfaces has long been the focus of semiparametric and nonparametric regression analysis. What has been less studied is the comparison of nonlinear functions. In lower-dimensional situations, inference typically involves comparisons of curves and surfaces. The existing comparative procedures are subject to various limitations, and few computational tools have been made available for off-the-shelf use. To address these limitations, two modified testing procedures for nonlinear curve and surface comparisons are proposed. The proposed computational tools are implemented in an R package, with a syntax similar to that of the commonly used model fitting packages. An R Shiny application is provided with an interactive interface for analysts who do not use R. The new tests are consistent against fixed alternative hypotheses. Theoretical details are presented in an appendix. Operating characteristics of the proposed tests are assessed against the existing methods. Applications of the methods are illustrated through real data examples.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Comput Stat Data Anal Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: Comput Stat Data Anal Año: 2020 Tipo del documento: Article País de afiliación: Estados Unidos