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Citation(s) in
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For:
Liu R
, Härdle WK, Zhang G.
Statistical inference for generalized additive partially linear models.
J MULTIVARIATE ANAL
2017. [DOI:
10.1016/j.jmva.2017.07.011
]
[
Citation(s) in
RCA
: 2
]
[
Impact Index Per Article: 0.3
]
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[Indexed: 11/29/2022]
Number
Cited by Other Article(s)
1
Cheng S
, Chen J. GMM estimation of partially linear additive spatial autoregressive model.
Comput Stat Data Anal
2023. [DOI:
10.1016/j.csda.2023.107712
]
[
Citation(s) in
RCA
: 0
]
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Impact Index Per Article: 0
]
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[Indexed: 02/05/2023]
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2
Liu R
, Zhao Y. Empirical likelihood inference for generalized additive partially linear models.
TEST-SPAIN
2021. [DOI:
10.1007/s11749-020-00731-1
]
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Citation(s) in
RCA
: 0
]
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Impact Index Per Article: 0
]
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[Indexed: 11/28/2022]
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3
GMM Estimation of a Partially Linear Additive Spatial Error Model.
MATHEMATICS
2021. [DOI:
10.3390/math9060622
]
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Citation(s) in
RCA
: 4
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Impact Index Per Article: 1.3
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[Indexed: 11/17/2022]
Abstract
This article presents a partially linear additive spatial error model (PLASEM) specification and its corresponding generalized method of moments (GMM). It also derives consistency and asymptotic normality of estimators for the case with a single nonparametric term and an arbitrary number of nonparametric additive terms under some regular conditions. In addition, the finite sample performance for our estimates is assessed by Monte Carlo simulations. Lastly, the proposed method is illustrated by analyzing Boston housing data.
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