Electronic Journal of Statistics

The Electronic Journal of Statistics (EJS) publishes research articles and short notes in theoretical, computational and applied statistics. The journal is open access. Articles are refereed and are held to the same standard as articles in other IMS journals. Articles become publicly available shortly after they are accepted.

Electronic Journal of Statistics is sponsored by the Institute of Mathematical Statistics and by the Bernoulli Society.

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Model selection in semiparametric expectile regressionElmar Spiegel, Fabian Sobotka, and Thomas KneibVolume 11, Number 2 (2017)
Error bounds for the convex loss Lasso in linear modelsMark Hannay and Pierre-Yves DeléamontVolume 11, Number 2 (2017)
Variable selection for partially linear models via learning gradientsLei Yang, Yixin Fang, Junhui Wang, and Yongzhao ShaoVolume 11, Number 2 (2017)
Kernel estimates of nonparametric functional autoregression models and their bootstrap approximationTingyi Zhu and Dimitris N. PolitisVolume 11, Number 2 (2017)
Asymptotic properties of quasi-maximum likelihood estimators in observation-driven time series modelsRandal Douc, Konstantinos Fokianos, and Eric MoulinesVolume 11, Number 2 (2017)
  • ISSN: 1935-7524 (electronic)
  • Publisher: The Institute of Mathematical Statistics and the Bernoulli Society
  • Discipline(s): Statistics and Probability
  • Full text available in Euclid: 2007--
  • Access: Open access
  • Euclid URL: https://projecteuclid.org/ejs