Generalized Partially Double-Index Model: Bootstrapping and Distinguishing Values | Zendy

Jae Keun Yoo | Zendy

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Generalized Partially Double-Index Model: Bootstrapping and Distinguishing Values

Author(s) -

Jae Keun Yoo

Publication year - 2015

Publication title -

communications for statistical applications and methods

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.326

H-Index - 6

eISSN - 2383-4757

pISSN - 2287-7843

DOI - 10.5351/csam.2015.22.3.305

Subject(s) - bootstrapping (finance) , linear subspace , mathematics , dimension (graph theory) , index (typography) , generalized linear model , dimensionality reduction , statistics , population , computer science , econometrics , artificial intelligence , combinatorics , geometry , demography , sociology , world wide web

We extend a generalized partially linear single-index model and newly define a generalized partially double-index model (GPDIM). The philosophy of sufficient dimension reduction is adopted in GPDIM to estimate unknown coefficient vectors in the model. Subsequently, various combinations of popular sufficient dimension reduction methods are constructed with the best combination among many candidates determined through a bootstrapping procedure that measures distances between subspaces. Distinguishing values are newly defined to match the estimates to the corresponding population coefficient vectors. One of the strengths of the proposed model is that it can investigate the appropriateness of GPDIM over a single-index model. Various numerical studies confirm the proposed approach, and real data application are presented for illustration purposes.

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