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TESTING FOR LINEARITY IN A SEMIPARAMETRIC REGRESSION-MODEL
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AI
DOI：10.1016/0304-4076(94)90058-2.png)
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摘要
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We propose a new exact test for nonlinearity in a regression model with one possibly nonlinear component. The test is based on the stochastic interpretation of spline smoothing given in Wahba (1978) and is a point optimal invariant test (as defined in King, 1988) based on this stochastic model. We show that the power of the exact test compares favourably with several other exact tests for nonlinearity proposed in the literature, and that its level and power are robust to long- and short-tailed symmetric error distributions. We also present an O(n) algorithm based on a state space approach to compute exact p-values for the point optimal invariant test, whereas previous implementations required O(n3) operations. An example is presented to illustrate the theory.
Keyword:
COMPUTATIONALLY EFFICIENT
KALMAN FILTER
LOCALLY MOST POWERFUL INVARIANT TEST
POINT OPTIMAL INVARIANT TEST
ROBUSTNESS
STATE SPACE MODEL
期刊
IF:
4
论文数:
5.3K
被引数:
3.0W
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