bccp: an R package for life-testing and survival analysis

The progressive type-I interval censoring and progressive type-II censoring plans are among the most commonly used censoring mechanisms in life-testing and survival analysis. In this work, we introduce an R package bccp, that is a useful tool for users in the field of reliability and survival analysis. The package bccp has been developed for simulating, computing maximum likelihood (ML) estimator, computing the expected and observed Fisher information matrices, computing goodness-of-fit measures, and correcting bias of the ML estimator for a wide range of distributions fitted to subjects under above two plans. The performance of the bccp is checked by two real examples. The bccp has been uploaded to comprehensive R archive network (CRAN) at https://cran.r-project.org/web/packages/bccp/index.html.

[1]  Hon Keung Tony Ng,et al.  Statistical estimation for the parameters of Weibull distribution based on progressively type-I interval censored sample , 2009 .

[2]  N. Balakrishnan,et al.  Progressive Censoring: Theory, Methods, and Applications , 2000 .

[3]  Francisco Cribari-Neto,et al.  Improved statistical inference for the two-parameter Birnbaum-Saunders distribution , 2007, Comput. Stat. Data Anal..

[4]  Ryan T. Godwin,et al.  Bias -corrected Maximum Likelihood Estimation of the Parameters of the Generalized Pareto Distribution , 2010 .

[5]  N. Balakrishnan,et al.  Point and interval estimation for Gaussian distribution, based on progressively Type-II censored samples , 2003, IEEE Trans. Reliab..

[6]  Wentao Wang,et al.  Bias-Corrected maximum likelihood estimation of the parameters of the weighted Lindley distribution , 2017, Commun. Stat. Simul. Comput..

[7]  Sudhir Paul,et al.  Bias-corrected maximum likelihood estimator of the negative binomial dispersion parameter. , 2005, Biometrics.

[8]  N. Balakrishnan,et al.  Inference for the extreme value distribution under progressive Type-II censoring , 2004 .

[9]  Gauss M. Cordeiro,et al.  Bias correction in ARMA models , 1994 .

[10]  D. Giles,et al.  Bias Reduction for the Maximum Likelihood Estimator of the Parameters of the Generalized Rayleigh Family of Distributions , 2014 .

[11]  Narayanaswamy Balakrishnan,et al.  Progressive censoring methodology: an appraisal , 2007 .

[12]  Saralees Nadarajah,et al.  mle.tools: An R Package for Maximum Likelihood Bias Correction , 2017, R J..

[13]  M. Teimouri.  Bias corrected maximum likelihood estimators under progressive type-I interval censoring scheme , 2020, Commun. Stat. Simul. Comput..

[14]  D. Giles,et al.  Bias-reduced maximum likelihood estimation of the zero-inflated Poisson distribution , 2016 .

[15]  Narayanaswamy Balakrishnan,et al.  Planning life tests with progressively Type-I interval censored data from the lognormal distribution , 2009 .

[16]  S. Nadarajah,et al.  Bias corrected MLEs under progressive type-II censoring scheme , 2016 .

[17]  Debasis Kundu,et al.  Bayesian Inference and Life Testing Plan for the Weibull Distribution in Presence of Progressive Censoring , 2008, Technometrics.

[18]  Francisco Cribari-Neto,et al.  Bias-corrected maximum likelihood estimation for the beta distribution , 1997 .

[19]  N. Balakrishnan,et al.  Reliability sampling plans for lognormal distribution, based on progressively-censored samples , 2000, IEEE Trans. Reliab..

[20]  Sanku Dey,et al.  Bias-corrected maximum likelihood estimators of the parameters of the inverse Weibull distribution , 2019, Commun. Stat. Simul. Comput..

[21]  E. Gehan,et al.  Plasmacytic myeloma. A study of the relationship of survival to various clinical manifestations and anomalous protein type in 112 patients. , 1967, The American journal of medicine.

[22]  Debasis Kundu,et al.  On Progressively Type-II Censored Two-parameter Rayleigh Distribution , 2016, Commun. Stat. Simul. Comput..

[23]  Arjun K. Gupta,et al.  Estimation Methods for the Gompertz–Makeham Distribution Under Progressively Type-I Interval Censoring Scheme , 2012 .

[24]  Min Wang,et al.  Improved parameter estimation of the log-logistic distribution with applications , 2018, Comput. Stat..

[25]  Ryan T. Godwin,et al.  On the Bias of the Maximum Likelihood Estimator for the Two-Parameter Lomax Distribution , 2013 .

[26]  M. H. Quenouille.  Approximate tests of correlation in time-series 3 , 1949, Mathematical Proceedings of the Cambridge Philosophical Society.

[27]  D. Chen,et al.  Lower Confidence Limits on the Generalized Exponential Distribution Percentiles Under Progressive Type-I Interval Censoring , 2013, Commun. Stat. Simul. Comput..

[28]  Ryan T. Godwin,et al.  Improved maximum-likelihood estimation of the shape parameter in the Nakagami distribution , 2013 .

[29]  Sukhdev Singh,et al.  Estimating the parameters of an inverse Weibull distribution under progressive type-I interval censoring , 2018 .

[30]  Saralees Nadarajah,et al.  Bias corrected MLEs for the Weibull distribution based on records , 2013 .

[31]  Narayanaswamy Balakrishnan,et al.  A Simple Simulational Algorithm for Generating Progressive Type-II Censored Samples , 1995 .

[32]  Narayanaswamy Balakrishnan,et al.  Inference for the Type II generalized logistic distribution under progressive Type II censoring , 2007 .

[33]  Francisco Cribari-Neto,et al.  Nearly Unbiased Maximum Likelihood Estimation for the Beta Distribution , 2002 .

[34]  Tzong-Ru Tsai,et al.  Parameter Estimations for Generalized Rayleigh Distribution under Progressively Type-I Interval Censored Data , 2011 .

[35]  Yuhlong Lio,et al.  Parameter estimations for generalized exponential distribution under progressive type-I interval censoring , 2010, Comput. Stat. Data Anal..

[36]  Veeresh Gadag,et al.  Progressively Censored Reliability Sampling Plans for the Weibull Distribution , 2000, Technometrics.

[37]  N. Balakrishnan,et al.  A General Purpose Approximate Goodness-of-Fit Test for Progressively Type-II Censored Data , 2012, IEEE Transactions on Reliability.

[38]  Bias-corrected estimators of scalar skew normal , 2017 .

[39]  Yu Guo,et al.  Statistical Inference for the Information Entropy of the Log-Logistic Distribution under Progressive Type-I Interval Censoring Schemes , 2018, Symmetry.

[40]  Sukhdev Singh,et al.  On estimating the parameters of the Burr XII model under progressive type-I interval censoring , 2017 .

[41]  Rita Aggarwala,et al.  PROGRESSIVE INTERVAL CENSORING: SOME MATHEMATICAL RESULTS WITH APPLICATIONS TO INFERENCE , 2001 .

[42]  D. Giles.  Bias Reduction for the Maximum Likelihood Estimators of the Parameters in the Half-Logistic Distribution , 2012 .

[43]  María-Dolores Jiménez-Gamero,et al.  Bias Correction in the Type I Generalized Logistic Distribution , 2011, Commun. Stat. Simul. Comput..

[44]  D. Cox,et al.  A General Definition of Residuals , 1968 .

[45]  Debasis Kundu,et al.  Inference and optimal censoring schemes for progressively censored Birnbaum–Saunders distribution , 2013 .

[46]  Peng Xiuyun,et al.  Parameter estimations with gamma distribution based on progressive type- I interval censoring , 2011, 2011 IEEE International Conference on Computer Science and Automation Engineering.

[47]  Rong Liu,et al.  Bias-corrected estimators of scalar skew normal , 2017, Commun. Stat. Simul. Comput..

[48]  L. LioY.,et al.  Parameter Estimations for Generalized RayleighDistribution under Progressively Type-I IntervalCensored Data , 2011 .

[49]  Sanku Dey,et al.  Bias-corrected maximum likelihood estimation of the parameters of the generalized half-normal distribution , 2018 .

[50]  Artur J. Lemonte.  Improved point estimation for the Kumaraswamy distribution , 2011 .

[51]  Narayanaswamy Balakrishnan,et al.  Exact linear inference and prediction for exponential distributions based on general progressively type-ii censored samples , 2002 .