Yu, Wen Empirical likelihood method for general additive-multiplicative hazard models. (English) Zbl 1201.62045 Commun. Stat., Theory Methods 39, No. 16, 2977-2990 (2010). Summary: An empirical likelihood-based inferential procedure is developed for a class of general additive-multiplicative hazard models. The proposed log-empirical likelihood ratio test statistic for the parameter vector is shown to have a chi-squared limiting distribution. The result can be used to make inference about the entire parameter vector as well as any linear combination of it. The asymptotic power of the proposed test statistic under contiguous alternatives is discussed. The method is illustrated by extensive simulation studies and a real example. Cited in 3 Documents MSC: 62G05 Nonparametric estimation 62N01 Censored data models 62G10 Nonparametric hypothesis testing 62E20 Asymptotic distribution theory in statistics 62G20 Asymptotic properties of nonparametric inference 65C60 Computational problems in statistics (MSC2010) Keywords:additive-multiplicative hazard; chi-squared distribution; contiguous alternative; counting process; empirical likelihood; likelihood ratio test; right-censored data Software:emplik PDFBibTeX XMLCite \textit{W. Yu}, Commun. Stat., Theory Methods 39, No. 16, 2977--2990 (2010; Zbl 1201.62045) Full Text: DOI References: [1] Andersen P. K., Statistical Models Based on Counting Processes (1993) · Zbl 0769.62061 [2] DOI: 10.1006/jmva.1994.1011 · Zbl 0796.62040 · doi:10.1006/jmva.1994.1011 [3] DOI: 10.1093/biomet/83.2.329 · Zbl 0864.62017 · doi:10.1093/biomet/83.2.329 [4] Chen S. X., Ann. Statist. 21 pp 621– (1993) [5] Cox D. R., J. Roy. Statist. Soc. B. 34 pp 187– (1972) [6] DOI: 10.1093/biomet/62.2.269 · Zbl 0312.62002 · doi:10.1093/biomet/62.2.269 [7] Cox D. R., Analysis of Survival Data (1984) [8] DOI: 10.2307/2289721 · doi:10.2307/2289721 [9] Li G., Statist. Sinica 13 pp 51– (2003) [10] DOI: 10.1093/biomet/81.1.61 · Zbl 0796.62099 · doi:10.1093/biomet/81.1.61 [11] DOI: 10.1214/aos/1176324320 · Zbl 0844.62082 · doi:10.1214/aos/1176324320 [12] DOI: 10.1016/j.jmva.2005.09.007 · Zbl 1102.62108 · doi:10.1016/j.jmva.2005.09.007 [13] DOI: 10.1093/biomet/75.2.237 · Zbl 0641.62032 · doi:10.1093/biomet/75.2.237 [14] DOI: 10.1214/aos/1176347494 · Zbl 0712.62040 · doi:10.1214/aos/1176347494 [15] DOI: 10.1214/aos/1176348368 · Zbl 0799.62048 · doi:10.1214/aos/1176348368 [16] DOI: 10.1201/9781420036152 · doi:10.1201/9781420036152 [17] DOI: 10.1111/1467-9469.00261 · Zbl 1010.62060 · doi:10.1111/1467-9469.00261 [18] DOI: 10.1081/SAC-100001859 · Zbl 1008.62692 · doi:10.1081/SAC-100001859 [19] DOI: 10.1214/aos/1176325370 · Zbl 0799.62049 · doi:10.1214/aos/1176325370 [20] DOI: 10.2307/3315441 · Zbl 0839.62059 · doi:10.2307/3315441 [21] DOI: 10.2307/2285449 · Zbl 0331.62028 · doi:10.2307/2285449 [22] DOI: 10.1080/03610920802379177 · Zbl 1167.62038 · doi:10.1080/03610920802379177 [23] DOI: 10.1081/SAC-200047114 · Zbl 1061.62161 · doi:10.1081/SAC-200047114 [24] DOI: 10.1093/biomet/92.2.492 · Zbl 1094.62127 · doi:10.1093/biomet/92.2.492 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.