Free Access
Volume 20, 2016
Page(s) 510 - 526
Published online 07 December 2016
  1. N. Balakrishnan and B. Xingqiu Zhao, New multi-sample nonparametric tests for panel count data. Ann. Stat. 37 (2009) 1112–1149. [Google Scholar]
  2. N. Balakrishnan and B. Xingqiu Zhao, A nonparametric test for the equality of counting processes with panel count data. Comput. Stat. Data Anal. 54 (2010) 135–142. [Google Scholar]
  3. M. Boutahar, B. Ghattas and D. Pommeret, Nonparametric comparison of several transformations of distribution functions. J. Nonparametric Stat. 25 (2013) 619–633. [CrossRef] [Google Scholar]
  4. R.J. Carroll, D. Ruppert, L.A. Stefanski and C. Crainiceanu, Measurement Error in Nonlinear Models: A Modern Perspective, 2nd edition. Chapman Hall, New York (2006). [Google Scholar]
  5. L. Cavalier and N. Hengartner, Adaptive estimation for inverse problems with noisy operators. Inverse Probl. 21 (2005) 1345–1361. [Google Scholar]
  6. S. Efromovich and V. Koltchinskii, On inverse problems with unknown operators. IEEE Trans. Inf. Theory 47 (2001) 2876–2893. [Google Scholar]
  7. R. Friden, Probability, Statistical Optics and Data Testing: A Problem Solving Approach. Springer Series in Information Science (2001). [Google Scholar]
  8. L. Jin, A data-driven test to compare two or multiple time series. Comput. Stat. Data Anal. 55 (2011) 2183–2196. [Google Scholar]
  9. H. Jin and J. Zhang, Subsampling tests for variance changes in the presence of autoregressive parameter shifts. J. Multivariate Anal. 101 (2010) 2255–2265 [CrossRef] [MathSciNet] [Google Scholar]
  10. J. Meyer, Stochastic Dominance and Transformations of Random Variables, in Studies in the Economics of Uncertainty: In Honor of Josef Hadar, edited by T.B. Fomby and T.K. Seo. Springer Science and Business Media (2012) 45–58. [Google Scholar]
  11. J. Meyer and M.B. Ormiston, Deterministic transformations of random variables and the comparative statics of risk. J. Risk Uncertain. 2 (1989) 179–188. [Google Scholar]
  12. M.H. Neumann and E. Paparoditis, On bootstrapping L2-type statistics in density testing. Stat. Probab. Lett. 50 (2000) 137–147 [Google Scholar]
  13. P. Sarda and P. Vieu,Kernel regression, in Smoothing and Regression Approaches, computation and application, edited by M.G. Schimek. Wiley Series in Probability and Statistics (2000) 43–70. [Google Scholar]
  14. S.L. Sclove and J.V. Ryzin, Estimating the Parameters of a Linear Function of a Random Variable. J. R. Stat. Soc. Ser. B 32 (1970) 362–368. [Google Scholar]
  15. P.K. Sen, Limiting behavior of regular functionals of empirical distributions for stationary mixing processes. Probab. Theory Relat. Fields 25 (1972) 71–82. [Google Scholar]
  16. J. Sun and J.D. Kalbfleisch, Estimation of the mean function of point processes based on data panel count data. Stat. Sin. 5 (1995) 279–290. [Google Scholar]
  17. X.F. Wang and B. Wang, Deconvolution Estimation in Measurement Error Models: The R Package decon. J. Stat. Softw. 39 (2011) 8060. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.