This paper proposes an asymptotically efficient method for estimating models with conditional moment restrictions. Our estimator generalizes the maximum empirical likelihood estimator (MELE) of Qin and Lawless (1994). Using a kernel smoothing method, we efficiently incorporate the information implied by the conditional moment restrictions into our empirical likelihood-based procedure. This yields a one-step estimator which avoids estimating optimal instruments. Our likelihood ratio-type statistic for parametric restrictions does not require the estimation of variance, and achieves asymptotic pivotalness implicitly. The estimation and testing procedures we propose are normalization invariant. Simulation results suggest that our new estimator works remarkably well in finite samples.

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As a marker of nocturnal hypoxemia, ODI rather than AHI might better explain the relationship between OSAS and FMD. Because body mass index and waist-to-hip ratio were identified as risk factors of high serum CRP in OSAS, obesity should be considered when predicting cardiovascular complications in OSAS.

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Although all markers demonstrated good diagnostic performance, they varied depending on the pathologic types of benign diseases and ovarian cancer. For accurate diagnosis of ovarian cancer, CA 125, HE4, and ROMA should be used complementarily.