Heteroscedasticity diagnostics in two-phase linear regression models. (English) Zbl 1183.62116
Summary: In two-phase linear regression models, it is a standard assumption that the random errors of two phases have constant variances. However, this assumption is not necessarily appropriate. This paper is devoted to the tests for variance heterogeneity in these models. We initially discuss a simultaneous test for variance heterogeneity of the two phases. When the simultaneous test shows that significant heteroscedasticity occurs in the whole model, we construct two individual tests to investigate whether or not both phases or one of them have/has significant heteroscedasticity. Several score statistics and their adjustments based on D. R. Cox and N. Reid, J. R. Stat. Soc., Ser. B 49, 1–39 (1987; Zbl 0616.62006), are obtained and illustrated with Australian onion data. The simulated powers of test statistics are investigated through Monte Carlo methods.
MSC:
| 62J05 | Linear regression; mixed models |
| 62H15 | Hypothesis testing in multivariate analysis |
| 65C05 | Monte Carlo methods |
| 62J20 | Diagnostics, and linear inference and regression |
Keywords:
heteroscedasticity; individual test; score test; Monte Carlo simulation; simultaneous test; two-phase linear regression modelsCitations:
Zbl 0616.62006References:
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