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Semiparametric estimation of the accelerated mean model with panel count data under informative examination times
dc.contributor.authorChiou, Sy Han
dc.contributor.authorXu, Gongjun
dc.contributor.authorYan, Jun
dc.contributor.authorHuang, Chiung‐yu
dc.date.accessioned2018-11-20T15:36:04Z
dc.date.available2019-11-01T15:10:33Zen
dc.date.issued2018-09
dc.identifier.citationChiou, Sy Han; Xu, Gongjun; Yan, Jun; Huang, Chiung‐yu (2018). "Semiparametric estimation of the accelerated mean model with panel count data under informative examination times." Biometrics 74(3): 944-953.
dc.identifier.issn0006-341X
dc.identifier.issn1541-0420
dc.identifier.urihttps://hdl.handle.net/2027.42/146494
dc.publisherWiley Periodicals, Inc.
dc.subject.otherScaleâ change model
dc.subject.otherSquared extrapolation method
dc.subject.otherFrailty
dc.subject.otherModelâ based bootstrap
dc.subject.otherPoisson process
dc.subject.otherRecurrent events
dc.titleSemiparametric estimation of the accelerated mean model with panel count data under informative examination times
dc.typeArticleen_US
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelMathematics
dc.subject.hlbtoplevelScience
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/146494/1/biom12840.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/146494/2/biom12840-sup-0001-SuppData.pdf
dc.description.bitstreamurlhttps://deepblue.lib.umich.edu/bitstream/2027.42/146494/3/biom12840_am.pdf
dc.identifier.doi10.1111/biom.12840
dc.identifier.sourceBiometrics
dc.identifier.citedreferenceVaradhan, R. and Gilbert, P. ( 2009 ). BB: An R package for solving a large system of nonlinear equations and for optimizing a highâ dimensional nonlinear objective function. Journal of Statistical Software 32, 1 â 26.
dc.identifier.citedreferenceSen, B. and Xu, G. ( 2015 ). Model based bootstrap methods for interval censored data. Computational Statistics & Data Analysis 81, 121 â 129.
dc.identifier.citedreferenceSong, S. ( 2004 ). Estimation with univariate mixed case interval censored data. Statistics Sinica 14, 269 â 282.
dc.identifier.citedreferenceSun, J., Tong, X., and He, X. ( 2007 ). Regression analysis of panel count data with dependent observation times. Biometrics 63, 1053 â 1059.
dc.identifier.citedreferenceSun, J. and Wei, L. ( 2000 ). Regression analysis of panel count data with covariateâ dependent observation and censoring times. Journal of the Royal Statistical Society, Series B (Statistical Methodology) 62, 293 â 302.
dc.identifier.citedreferenceSun, L. and Su, B. ( 2008 ). A class of accelerated means regression models for recurrent event data. Lifetime Data Analysis 14, 357 â 375.
dc.identifier.citedreferenceThall, P. F. and Lachin, J. M. ( 1988 ). Analysis of recurrent events: Nonparametric methods for randomâ interval count data. Journal of the American Statistical Association 83, 339 â 347.
dc.identifier.citedreferenceTurnbull, B. W. ( 1976 ). The empirical distribution function with arbitrarily grouped, censored and truncated data. Journal of the Royal Statistical Society, Series B (Methodological) 290 â 295.
dc.identifier.citedreferencevan der Vaart, A. and Wellner, J. A. ( 2000 ). Preservation theorems for Glivenkoâ Cantelli and uniform Glivenkoâ Cantelli classes.In High Dimensional Probability, II (Seattle, WA, 1999), volume 47 of Program Probability, 115â 133.Boston, MA: Birkhäuser Boston.
dc.identifier.citedreferenceVaradhan, R. ( 2014 ). SQUAREM: Squared extrapolation methods for accelerating fixedâ point iterations. R Package Version 2014.8â 1.
dc.identifier.citedreferenceVaradhan, R. and Roland, C. ( 2008 ). Simple and globally convergent methods for accelerating the convergence of any EM algorithm. Scandinavian Journal of Statistics 35, 335 â 353.
dc.identifier.citedreferenceWang, M.â C., Qin, J., and Chiang, C.â T. ( 2001 ). Analyzing recurrent event data with informative censoring. Journal of the American Statistical Association 96, 1057 â 1065.
dc.identifier.citedreferenceWang, X., Ma, S., and Yan, J. ( 2013 ). Augmented estimating equations for semiparametric panel count regression with informative observation times and censoring time. Statistica Sinica 23, 359 â 381.
dc.identifier.citedreferenceWellner, J. A. ( 1995 ). Interval censoring, case 2: Alternative hypotheses.In Analysis of Censored Data (Pune, 1994/1995), volume 27 of IMS Lecture Notes Monograph Series, 271â 291. Hayward, CA: Institute of Mathematical Statistics.
dc.identifier.citedreferenceWellner, J. A. and Zhang, Y. ( 2007 ). Two likelihoodâ based semiparametric estimation methods for panel count data with covariates. The Annals of Statistics 35, 2106 â 2142.
dc.identifier.citedreferenceXu, G., Chiou, S. H., Huang, C.â Y., Wang, M.â C., and Yan, J. ( 2017 ). Joint scaleâ change models for recurrent events and failure time. Journal of the American Statistical Association 112, 794 â 805.
dc.identifier.citedreferenceZeng, D. and Cai, J. ( 2010 ). A semiparametric additive rate model for recurrent events with an informative terminal event. Biometrika 97, 699 â 712.
dc.identifier.citedreferenceZeng, D., Mao, L., and Lin, D. ( 2016 ). Maximum likelihood estimation for semiparametric transformation models with intervalâ censored data. Biometrika 103, 253 â 271.
dc.identifier.citedreferenceZhang, Y. ( 2002 ). A semiparametric pseudolikelihood estimation method for panel count data. Biometrika 89, 39 â 48.
dc.identifier.citedreferenceZhao, X. and Tong, X. ( 2011 ). Semiparametric regression analysis of panel count data with informative observation times. Computational Statistics & Data Analysis 55, 291 â 300.
dc.identifier.citedreferenceZhao, X., Tong, X., and Sun, J. ( 2013 ). Robust estimation for panel count data with informative observation times. Computational Statistics & Data Analysis 57, 33 â 40.
dc.identifier.citedreferenceZhou, J., Zhang, H., Sun, L., and Sun, J. ( 2017 ). Joint analysis of panel count data with an informative observation process and a dependent terminal event. Lifetime Data Analysis 23, 560 â 584.
dc.identifier.citedreferenceAbrevaya, J. and Huang, J. ( 2005 ). On the bootstrap of the maximum score estimator. Econometrica 73, 1175 â 1204.
dc.identifier.citedreferenceSun, J. and Zhao, X. ( 2013 ). Statistical Analysis of Panel Count Data. New York: Springer.
dc.identifier.citedreferenceBailey, H. H., Kim, K., Verma, A. K., Sielaff, K., Larson, P. O., Snow, S., et al. ( 2010 ). A randomized, doubleâ blind, placeboâ controlled phase 3 skin cancer prevention study of α â difluoromethylornithine in subjects with previous history of skin cancer. Cancer Prevention Research 3, 35 â 47.
dc.identifier.citedreferenceBarzilai, J. and Borwein, J. M. ( 1988 ). Twoâ point step size gradient methods. IMA Journal of Numerical Analysis 8, 141 â 148.
dc.identifier.citedreferenceBuzkova, P. ( 2010 ). Panel count data regression with informative observation times. The International Journal of Biostatistics 6, Article 30.
dc.identifier.citedreferenceBuzkova, P. and Lumley, T. ( 2007 ). Longitudinal data analysis for generalized linear models with followâ up dependent on outcomeâ related variables. Canadian Journal of Statistics 35, 485 â 500.
dc.identifier.citedreferenceChen, Y. Q. and Jewell, N. P. (2001). On a general class of semiparametric hazards regression models. Biometrika 88, 687 â 702.
dc.identifier.citedreferenceDing, Y. and Nan, B. ( 2011 ). A sieve Mâ theorem for bundled parameters in semiparametric models, with application to the efficient estimation in a linear model for censored data. The Annals of Statistics 39, 3032 â 3061.
dc.identifier.citedreferenceGhosh, D. and Lin, D. Y. ( 2003 ). Semiparametric analysis of recurrent events data in the presence of dependent censoring. Biometrics 59, 877 â 885.
dc.identifier.citedreferenceGroeneboom, P. and Wellner, J. A. ( 1992 ). Information Bounds and Nonparametric Maximum Likelihood Estimation, volume 19 of DMV Seminar. Basel: Birkhäuser Verlag.
dc.identifier.citedreferenceHe, X., Tong, X., and Sun, J. ( 2009 ). Semiparametric analysis of panel count data with correlated observation and followâ up times. Lifetime Data Analysis 15, 177 â 196.
dc.identifier.citedreferenceHu, X., Sun, J., and Wei, L.â J. ( 2003 ). Regression parameter estimation from panel counts. Scandinavian Journal of Statistics 30, 25 â 43.
dc.identifier.citedreferenceHua, L. and Zhang, Y. ( 2012 ). Splineâ based semiparametric projected generalized estimating equation method for panel count data. Biostatistics 13, 440 â 454.
dc.identifier.citedreferenceHuang, C.â Y., Wang, M.â C., and Zhang, Y. ( 2006 ). Analysing panel count data with informative observation times. Biometrika 93, 763 â 775.
dc.identifier.citedreferenceKalbfleisch, J. and Lawless, J. F. ( 1985 ). The analysis of panel data under a markov assumption. Journal of the American Statistical Association 80, 863 â 871.
dc.identifier.citedreferenceKim, Y.â J. ( 2006 ). Analysis of panel count data with dependent observation times. Communications in Statistics Simulation and Computation® 35, 983 â 990.
dc.identifier.citedreferenceLa Cruz, W., Martí nez, J., and Raydan, M. ( 2006 ). Spectral residual method without gradient information for solving largeâ scale nonlinear systems of equations. Mathematics of Computation 75, 1429 â 1448.
dc.identifier.citedreferenceLi, N., Park, D.â H., Sun, J., and Kim, K. ( 2011 ). Semiparametric transformation models for multivariate panel count data with dependent observation process. Canadian Journal of Statistics 39, 458 â 474.
dc.identifier.citedreferenceLi, N., Sun, L., and Sun, J. ( 2010 ). Semiparametric transformation models for panel count data with dependent observation process. Statistics in Biosciences 2, 191 â 210.
dc.identifier.citedreferenceLi, N., Zhao, H., and Sun, J. ( 2013 ). Semiparametric transformation models for panel count data with correlated observation and followâ up times. Statistics in Medicine 32, 3039 â 3054.
dc.identifier.citedreferenceLin, D. Y., Wei, L. J., and Ying, Z. ( 1998 ). Accelerated failure time models for counting processes. Biometrika 85, 605 â 618.
dc.identifier.citedreferenceLu, M., Zhang, Y., and Huang, J. ( 2009 ). Semiparametric estimation methods for panel count data using monotone bâ splines. Journal of the American Statistical Association 104, 1060 â 1070.
dc.identifier.citedreferenceMa, L. and Sundaram, R. ( 2016 ). Analysis of gap times based on panel count data with informative observation times and unknown start time. Journal of the American Statistical Association, https://doi.org/10.1080/01621459.2016.1246369
dc.identifier.citedreferenceR Core Team ( 2007 ). R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
dc.identifier.citedreferenceSchick, A. and Yu, Q. ( 2000 ). Consistency of the GMLE with mixed case intervalâ censored data. Scandinavian Journal of Statistics 27, 45 â 55.
dc.identifier.citedreferenceSen, B., Banerjee, M., and Woodroofe, M. ( 2010 ). Inconsistency of bootstrap: The Grenander estimator. Annals of Statistics 38, 1953 â 1977.
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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