New classes of tests for the Weibull distribution using Stein’s method in the presence of random right censoring

Allison J, Milošević B, Obradović M, Smuts M (2021) Distribution-free goodness-of-fit tests for the pareto distribution based on a characterization. Comput Stat pp 1–16

Allison J, Santana L (2015) On a data-dependent choice of the tuning parameter appearing in certain goodness-of-fit tests. J Stat Comput Simul 85(16):3276–3288

Allison JS, Betsch S, Ebner B, Visagie IJH (2019) New weighted L$$^2$$-type tests for the inverse Gaussian distribution. arXiv preprint arXiv:1910.14119

Allison JS, Huskova M, Meintanis SG (2017) Testing the adequacy of semiparametric transformation models. Test 27:1–25

Allison JS, Santana L, Smit N, Visagie IJH (2017) An “apples-to-apples” comparison of various tests for exponentiality. Comput Stat 32(4):1241–1283

Balakrishnan N, Chimitova E, Vedernikova M (2015) An empirical analysis of some nonparametric goodness-of-fit tests for censored data. Commun Stat Simul Comput 44(4):1101–1115

Baringhaus L, Ebner B, Henze N (2017) The limit distribution of weighted L2-goodness-of-fit statistics under fixed alternatives, with applications. Ann Inst Stat Math 69(5):969–995

Baringhaus L, Henze N (1988) A consistent test for multivariate normality based on the empirical characteristic function. Metrika 35(1):339–348

Baringhaus L, Henze N (1991) A class of consistent tests for exponentiality based on the empirical laplace transform. Ann Inst Stat Math 43(3):551–564

Betsch S, Ebner B (2018) Testing normality via a distributional fixed point property in the Stein characterization. TEST pp. 1–34. https://doi.org/10.1007/s11749-019-00630-0

Betsch S, Ebner B (2019) A new characterization of the gamma distribution and associated goodness-of-fit tests. Metrika 82(7):779–806

Bothma E, Allison JS, Cockeran M, Visagie IJH (2020) Kaplan-Meier based tests for exponentiality in the presence of censoring. arXiv preprint arXiv:2011.04519

Breslow N, Crowley J (1974) A large sample study of the life table and product limit estimates under random censorship. Ann Stat pp. 437–453

Cabaña A, Quiroz AJ (2005) Using the empirical moment generating function in testing for the Weibull and the type I extreme value distributions. Test 14(2):417–431

Cox DR, Oakes D (1984) Analysis of survival data, vol 21. CRC Press, Boca Raton

Cuparić M, Milošević B (2021) New characterization-based exponentiality tests for randomly censored data. TEST. https://doi.org/10.1007/s11749-021-00787-7

D’Agostino RB, Stephens MA (1986) Goodness-of-fit techniques, vol 68. CRC Press, Boca Raton

Döring M, Cramer E (2019) On the power of goodness-of-fit tests for the exponential distribution under progressive Type-II censoring. J Stat Comput Simul 89:2997–3034

Efron B (1967) The two sample problem with censored data. In: Proceedings of the fifth berkeley symposium on mathematical statistics and probability, vol 4, pp 831–853

Fernández T, Rivera N (2020) Kaplan-Meier V-and U-statistics. Electron J Stat 14(1):1872–1916

Fernández VA, Jiménez Gamero MD, García M (2008) A test for the two-sample problem based on empirical characteristic functions. Comput Stat Data Anal 52:3730–3748

Feuerverger A, Mureika RA (1977) The empirical characteristic function and its applications. Ann Stat 5(1):88–97

Giacomini R, Politis DN, White H (2013) A warp-speed method for conducting Monte Carlo experiments involving bootstrap estimators. Econom Theory 29(3):567–589

Gupta RD, Richards DSP (1997) Invariance properties of some classical tests for exponentiality. J Stat Plann Inference 63(2):203–213

Henze N, Visagie IJH (2020) Testing for normality in any dimension based on a partial differential equation involving the moment generating function. Ann Inst Stat Math 72:1109–1136

Hlavac M (2018) stargazer: well-formatted regression and summary statistics tables. https://CRAN.R-project.org/package=stargazer

Jacobson A, Wilson V, Pileggi S (2018) parmsurvfit: Parametric Models for Survival Data. https://CRAN.R-project.org/package=parmsurvfit. R package version 0.1.0

Jiang R, Murthy D (2011) A study of Weibull shape parameter: properties and significance. Reliab Eng Syst Saf 96(12):1619–1626

Kalbfleisch JD, Prentice RL (2011) The statistical analysis of failure time data, vol 360. Wiley, New York

Kaplan EL, Meier P (1958) Nonparametric estimation from incomplete observations. J Am Stat Assoc 53(282):457–481

Kim N (2017) Goodness-of-fit tests for randomly censored Weibull distributions with estimated parameters. Commun Stat Appl Methods 24(5):519–531

Klar B, Meintanis SG (2005) Tests for normal mixtures based on the empirical characteristic function. Comput Stat Data Anal 49(1):227–242

Kotz S, Nadarajah S (2000) Extreme value distributions: theory and applications. World Scientific, Singapore

Kotze S, Johnson N (1983) Encyclopedia of statistical sciences, vol 3. Wiley, New York

Koziol JA, Green SB (1976) A Cramér-von Mises statistic for randomly censored data. Biometrika 63(3):465–474

Krit M (2014) Goodness-of-fit tests for the Weibull distribution based on the Laplace transform. Journal de la Société Française de Statistique 155(3):135–151

Lee ET, Wang J (2003) Statistical methods for survival data analysis, vol 476. Wiley, New York

Liao M, Shimokawa T (1999) A new goodness-of-fit test for type-i extreme-value and 2-parameter Weibull distributions with estimated parameters. Optimization 64(1):23–48

Mann NR, Scneuer EM, Fertig KW (1973) A new goodness-of-fit test for the two-parameter Weibull or extreme-value distribution with unknown parameters. Commun Stat Theory Methods 2(5):383–400

Mazucheli J, Fernandes LB, de Oliveira RP (2016) LindleyR: The Lindley Distribution and Its Modifications. https://CRAN.R-project.org/package=LindleyR. R package version 1.1.0

Meintanis SG, Iliopoulos G (2003) Tests of fit for the Rayleigh distribution based on the empirical Laplace transform. Ann Inst Stat Math 55(1):137–151

Meintanis SG, Ngatchou-Wandji J, Allison JS (2018) Testing for serial independence in vector autoregressive models. Stat Papers 59(4):1379–1410

Mijburgh PA, Visagie IJH (2020) An overview of goodness-of-fit tests for the Poisson distribution. South African Stat J 54(2):207–230

R Core Team (2019) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/

Tiku ML, Singh M (1981) Testing the two parameter Weibull distribution. Commun Stat Theory Methods 10(9):907–918