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Bootstrap based goodness‑of‑fit tests for binary multivariate regression models

  • We consider a binary multivariate regression model where the conditional expectation of a binary variable given a higher-dimensional input variable belongs to a parametric family. Based on this, we introduce a model-based bootstrap (MBB) for higher-dimensional input variables. This test can be used to check whether a sequence of independent and identically distributed observations belongs to such a parametric family. The approach is based on the empirical residual process introduced by Stute (Ann Statist 25:613–641, 1997). In contrast to Stute and Zhu’s approach (2002) Stute & Zhu (Scandinavian J Statist 29:535–545, 2002), a transformation is not required. Thus, any problems associated with non-parametric regression estimation are avoided. As a result, the MBB method is much easier for users to implement. To illustrate the power of the MBB based tests, a small simulation study is performed. Compared to the approach of Stute & Zhu (Scandinavian J Statist 29:535–545, 2002), the simulations indicate a slightly improved power of the MBB based method. Finally, both methods are applied to a real data set.

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Metadaten
Author:Mareike van Heel, Gerhard DiktaORCiD, Roel Braekers
DOI:https://doi.org/10.1007/s42952-021-00142-4
ISSN:2005-2863 (Online)
ISSN:1226-3192 (Print)
Parent Title (English):Journal of the Korean Statistical Society
Publisher:Springer Nature
Place of publication:Singapur
Document Type:Article
Language:English
Year of Completion:2021
Date of the Publication (Server):2021/09/21
Volume:51
Length:28 Seiten
Note:
Corresponding author: Mareike van Heel
Link:https://doi.org/10.1007/s42952-021-00142-4
Zugriffsart:weltweit
Institutes:FH Aachen / Fachbereich Medizintechnik und Technomathematik
FH Aachen / Fachbereich Energietechnik
collections:Verlag / Springer Nature
Open Access / Hybrid
Licence (German):License LogoCreative Commons - Namensnennung