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Jump-preserving profiled local linear estimation for partial linear models
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DOI：10.1080/03610926.2025.2572491.png)
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Abstract
En 中文
The partial linear model is a very important class of semiparametric model in applied quantitative sciences. This article considers the estimation of a partial linear model with a discontinuous unknown non parametric function. We embed the jump-preserving techniques in the profiled local linear kernel smoothing method, then propose an adaptive jump-preserving profiled local linear estimation procedure to estimate the parametric coefficients and non parametric function. This method can automatically accommodate possible jumps of the non parametric function without knowing the number and locations of jump points. The resulting estimators can preserve the jumps well and also give smooth estimates of the continuity part. The asymptotical properties of the resulting estimators are demonstrated under some mild conditions. Several numerical simulations are conducted to evaluate the finite sample performance of the proposed methodologies.
Keywords:
Semiparametric model
partial linear model
local linear smoothing
jump-preserving estimation
weighted residual mean square
Journal
C
IF:
0.8
Papers:
211
Citations:
0