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Lopez, O.; Patilea, V.

Nonparametric lack-of-fit tests for parametric mean-regression models with censored data. (English) Zbl 1151.62036

J. Multivariate Anal. 100, No. 1, 210-230 (2009).
Summary: We developed two kernel smoothing based tests of a parametric mean-regression model against a nonparametric alternative when the response variable is right-censored. The new test statistics are inspired by synthetic data and the weighted least squares approaches for estimating the parameters of a (non)linear regression model under censoring. The asymptotic critical values of our tests are given by the quantiles of the standard normal law. The tests are consistent against fixed alternatives, local Pitman alternatives and uniformly over alternatives in Hölder classes of functions of known regularity.

Cited in 12 Documents

MSC:

62G10 Nonparametric hypothesis testing
62G08 Nonparametric regression and quantile regression
62N01 Censored data models
62F03 Parametric hypothesis testing

Keywords:

hypothesis testing; censored data; Kaplan-Meier integral; local alternatives
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References:

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