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A Novel Estimation Method in Generalized Single Index Models

The single index and generalized single index models have been demonstrated to be a powerful tool for studying nonlinear interaction effects of variables in the low-dimensional case. In this article, we propose a new estimation approach for generalized single index models E(Y ∣∣ θ⊤X)=ψ(g(θ⊤X))
with ψ(⋅)
known but g(⋅)
unknown. Specifically, we first obtain a consistent estimator of the regression function by using a local linear smoother, and then estimate the parametric components by treating ψ(gˆ(θ⊤Xi))
as our continuous response. The resulting estimators of θ are asymptotically normal. The proposed procedure can substantially overcome convergence problems encountered in generalized linear models with discrete response variables when sparseness occurs and misspecification. We conduct simulation experiments to evaluate the numerical performance of the proposed methods and analyze a financial dataset from a peer-to-peer lending platform of China as an illustration.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF BUSINESS & ECONOMIC STATISTICS; Vol. 41, No. 2, April 2023

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 399-413

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

LOGISTIC REGRESSION
SINGLE INDEX MODEL
EMPIRICAL PROCESSES
MAVE
PLSIM
TAIL PROBABILITY INEQUALITY

Info Detail Spesifik

https://doi.org/10.1080/07350015.2022.2027777

Pernyataan Tanggungjawab

Dixin Zhang, Yulin Wang, Hua Liang

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