103,458 research outputs found
A Simple Test for the Absence of Covariate Dependence in Hazard Regression Models
This paper extends commonly used tests for equality of hazard rates in a two-sample or k-sample setup to a situation where the covariate under study is continuous. In other words, we test the hypothesis that the conditional hazard rate is the same for all covariate values, against the omnibus alternative as well as more specific alternatives, when the covariate is continuous. The tests developed are particularly useful for
detecting trend in the underlying conditional hazard rates or changepoint trend alternatives. Asymptotic distribution of the test statistics are established and small sample properties of the tests are studied. An application to the e¤ect of aggregate Q on corporate failure in the UK shows evidence of trend in the covariate e¤ect, whereas a Cox regression model failed to detect evidence of any covariate effect. Finally, we discuss an
important extension to testing for proportionality of hazards in the presence of individual level frailty with arbitrary distribution
M-estimation of linear models with dependent errors
We study asymptotic properties of -estimates of regression parameters in
linear models in which errors are dependent. Weak and strong Bahadur
representations of the -estimates are derived and a central limit theorem is
established. The results are applied to linear models with errors being
short-range dependent linear processes, heavy-tailed linear processes and some
widely used nonlinear time series.Comment: Published at http://dx.doi.org/10.1214/009053606000001406 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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