Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287618 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of the Korean Statistical Society [ISSN:] 2005-2863 [Volume:] 51 [Issue:] 1 [Publisher:] Springer [Place:] Singapore [Year:] 2021 [Pages:] 308-335
Publisher: 
Springer, Singapore
Abstract: 
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.
Subjects: 
Binary regression model
Bootstrap based test
Goodness-of-fit test
Marked empirical process
JEL: 
F17
E20
F40
J12
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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