Asymptotic theory for partly linear models

Jiti Gao

doi:10.1080/03610929508831598

Asymptotic theory for partly linear models

Jiti Gao

Research output: Contribution to journal › Article › Research › peer-review

61 Citations (Scopus)

Abstract

Consider the model Y_i= x_iβ + g(t_i) + V_i, 1≤ i ≤n. Here x_i= (x_il,…,x_ip) and t_iare known and nonrandom design points, β = (β_l,…,β_p) is an unknown parameter, g(·) is an unknown function over R¹, and V_iis a class of linear processes. Based on g(·) estimated by nonparametric kernel estimation or approximated by a finite series expansion, the asymptotic normalities and the strong consistencies of the LS estimator of β and an estimator of σ ² ₀= EV² ₁are investigated.

Original language	English
Pages (from-to)	1985-2009
Number of pages	25
Journal	Communications in Statistics - Theory and Methods
Volume	24
Issue number	8
DOIs	https://doi.org/10.1080/03610929508831598
Publication status	Published - 1 Jan 1995
Externally published	Yes

Keywords

Asymptotic normality
Linear process
Partly linear model
Strong convergence

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10.1080/03610929508831598

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AB - Consider the model Yi= xiβ + g(ti) + Vi, 1≤ i ≤n. Here xi= (xil,…,xip) and tiare known and nonrandom design points, β = (βl,…,βp) is an unknown parameter, g(·) is an unknown function over R1, and Viis a class of linear processes. Based on g(·) estimated by nonparametric kernel estimation or approximated by a finite series expansion, the asymptotic normalities and the strong consistencies of the LS estimator of β and an estimator of σ 2 0= EV2 1are investigated.

KW - Asymptotic normality

KW - Linear process

KW - Partly linear model

KW - Strong convergence

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JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

IS - 8

ER -

Asymptotic theory for partly linear models

Abstract

Keywords

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