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Publikationen

Buchkapitel (Sortieren nach: Erscheinungsdatum | Titel)
Cramer, E. & Kamps, U. (1998). Maximum likelihood estimation with different sequential k-out-of-n systems. In: W. Kahle, E. von Collani, J. Franz & U. Jensen (Hrsg.), Advances in Stochastic Models for Reliability, Quality and Safety (S.101-111). Basel: Birkhäuser.
Cramer, E. & Kamps, U. (2001). Sequential k-out-of-n systems. In: N. Balakrishnan & C.R. Rao (Hrsg.), Handbook of Statistics Vol. 20, Advances in Reliability (S.301-372). Amsterdam: Elsevier.
Cramer, E. & Kamps, U. (2005). Characterization of the exponential distribution by conditional expectations of generalized spacings. In: N. Balakrishnan, I.G. Bairamov & O.L. Gebizlioglu (Hrsg.), Advances on Models, Characterizations, and Applications (S.83-96). Boca Raton, FL: Taylor&Francis.
Burkschat, M., Cramer, E. & Kamps, U. (2006). Linear estimation of location and scale parameters based on generalized order statistics from generalized Pareto distributions. In: M. Ahsanullah & M. Raqab (Hrsg.), Recent Developments in Ordered Random Variables (S..). New York: Nova Science Publishers.
Cramer, E. & Iliopoulos, G. (2015). Adaptive progressive censoring. In: P. Choudhary, Ch. Nagaraja & H. K. T. Ng (Hrsg.), Ordered Data Analysis, Modeling and Health Research Methods — In Honor of H.N. Nagaraja’s 60th Birthday (Springer Proceedings in Mathematics & Statistics Volume 149) (S.73-86). New York: Springer.
Bücher (Sortieren nach: Erscheinungsdatum | Titel)
Cramer, K., Cramer, E., Kamps, U. & Zuckschwerdt, Ch. (2004). Beschreibende Statistik - Interaktive Grafiken. Berlin: Springer.
Kamps, U., Cramer, E., Strauer, D. & Herff, W. (2005). Prüfungsvorbereitung Wirtschaftsmathematik - Analysis. München: Oldenburg.
Kamps, U., Cramer, E. & Oltmanns, H. (2009). Wirtschaftsmathematik - Einführendes Lehr- und Arbeitsbuch (3. Aufl). Oldenbourg: München.. Verfügbar unter http://www.oldenbourg-wissenschaftsverlag.de/olb/de/1.c.1694442.de
Cramer, E., Herff, W. & Kamps, U. (2010). Übungen zur Mathematik (2. Aufl.). Aachen: ISW.
Cramer, E. & Kamps, U. (2011). Klausurtraining Statistik. Aachen: ISW.
Burkschat, M., Cramer, E. & Kamps, U. (2012). Beschreibende Statistik - Grundlegende Methoden der Datenanalyse (2.Aufl.). Berlin: Springer.. Verfügbar unter http://www.springer.com/statistics/statistical+theory+and+methods/book/978-3-642-30012-7
Clermont, S., Cramer, E., Jochems, B. & Kamps, U. (2012). Wirtschaftsmathematik - Mathematik-Training zum Studienstart (4. Aufl.). München: Oldenburg.. Verfügbar unter http://www.oldenbourg-verlag.de/wissenschaftsverlag/wirtschaftsmathematik/9783486715064
Cramer, E. & Kamps, U. (2013). Statistik griffbereit (Formelsammlung zur Wahrscheinlichkeitsrechnung und Statistik) [5. Aufl.]. Aachen: ISW.
Balakrishnan, N. & Cramer, E. (2014). The Art of Progressive Censoring. Applications to Reliability and Quality. New York: Birkhäuser.. Verfügbar unter http://www.springer.com/birkhauser/applied+probability+and+statistics/book/978-0-8176-4806-0
Cramer, E., Kamps, U., Kateri, M. & Burkschat, M. (2015). Mathematik für Ökonomen (Kompakter Einstieg für Bachelorstudierende). Berlin: de Gruyter Oldenburg.. Verfügbar unter http://www.degruyter.com/view/product/455559?rskey=IJKTGk&result=1
Cramer, E., Kamps, U., Lehmann, J. & Walcher, S. (2017). Toolbox Mathematik für MINT-Studiengänge . Heidelberg: Springer Spektrum.
Cramer, E. & Kamps, U. (2017). Grundlagen der Wahrscheinlichkeitsrechnung und Statistik (Ein Skript für Studierende der Informatik, der Ingenieur- und Wirtschaftswissenschaften) [4. Aufl.]. Berlin: Springer.. Verfügbar unter http://www.springer.com/springer+spektrum/statistik/statistik+f%C3%BCr+naturwissenschaft+medizin+%26+technik/book/978-3-662-43972-2
Cramer, E. & Neslehova, J. (2018). Vorkurs Mathematik - Arbeitsbuch zum Studienbeginn in Bachelor-Studiengängen (7. Aufl.). Berlin: Springer.. Verfügbar unter http://www.springer.com/de/book/9783662574935
Contribution in Discussion of Papers (Sortieren nach: Erscheinungsdatum | Titel)
Kamps, U. & Cramer, E. (2007). Comments on: 'Progressive censoring methodology - an appraisal'. TEST, 16, 271-275
Cramer, E. (2013). Comments on “Hybrid Censoring: Models, Inferential Results and Applications” by N. Balakrishnan and D. Kundu. Comp. Statist. Data Anal., 57, 201-205
Encyclopedia Entries (Sortieren nach: Erscheinungsdatum | Titel)
Cramer, E. (2006). Sequential Order Statistics. In: S. Kotz, N. Balakrishnan, Campbell B. Read, B. Vidakovic & N. L. Johnson (Hrsg.), Encyclopedia of Statistical Sciences (2. Aufl.) (S.Vol. 12, 7629-7634). Hoboken, NJ: Wiley.
Cramer, E. (2016). Sequential order statistics. In: N. Balakrishnan, P. Brandimarte, B. Everitt, G. Molenberghs, W. Piegorsch & F. Riggeri (Hrsg.), Wiley StatsRef: Statistics Reference Online (S.to appear). New York: Wiley.. Verfügbar unter
Cramer, E. (2016). Progressive Censoring Schemes. Wiley StatsRef - Statistics Reference Online (S.to appear). New York: Wiley.. Verfügbar unter
Neue Medien (Sortieren nach: Erscheinungsdatum | Titel)
Cramer, E., Cramer, K. & Kamps, U. (2002). e-stat: A web-based learning environment in applied statistics. In: W. Härdle & B. Rönz (Hrsg.), COMPSTAT 2002 - Proceedings in Computational Statistics (S.309-314). Heidelberg: Physica.
Cramer, E., Cramer, K. & Kamps, U. (2002). Neuen Medien für den schulischen Statistikunterricht. Stochastik in der Schule, 22, 23-30
Cramer, E., Härdle, W., Kamps, U. & Witzel, R. (2003). e-stat: views, methods, applications. Bulletin of the International Statistical Institute (Vol. LX Book 2) (S.82-85). Berlin: ISI.
Cramer, E. & Neslehova, J. (2003). (e)learning the basics of probability. Bulletin of the International Statistical Institute (Vol. LX Book 2 (Contributed Papers)) (S.153-154). Berlin: ISI.. Verfügbar unter
Cramer, E., Cramer, K., Janzing, P. & Kamps, U. (2003). EMILeA-stat: A web-based learning environment in applied statistics with a focus on learning and teaching in secondary schools. Proceedings of the IASE Satellite Conference on Statistics Education and the Internet [CDROM] (S.-). Voorborg: ISI.
Cramer, E., Cramer, K., Janzing, P., Kamps, U. & Pahl, C. (2004). EMILeA-stat: Multimediales und interaktives Statistiklernen. In: R. Biehler, J. Engel & J. Meyer (Hrsg.), Neue Medien und innermathematische Vernetzungen in der Stochastik (S.107-126). Hildesheim: Franzbecker.
Cramer, E., Cramer, K. & Kamps, U. (2004). EMILeA-stat: Statistik multimedial und interaktiv. Softwaretechnik-Trends, 24, 46-53
Cramer, E., Cramer, K. & Kamps, U. (2004). Die elementar-modulare Struktur der Lehr- und Lernumgebung EMILeA-stat. In: U. Rinn & D.M. Meister (Hrsg.), Didaktik und Neue Medien (S.175-191). Münster: Waxmann.
Cramer, E. & Walcher, S. (2010). Schulmathematik und Studierfähigkeit. Mitteilungen der DMV, 18 (2), 110-114
Au, R. & Cramer, E. (2010). (A)Symmetrie in der Stochastik: Binomialverteilung und Grenzwertsatz von de Moivre-Laplace. mathematik lehren, 161, 55-58
Cramer, E., Heitzer, J., Hürtgen, H., Polaczek, Ch. & Walcher, S. (2011). Fit fürs Studium ... – Weiterführende Argumentationsanlässe in der Oberstufe. mathematik lehren, 168, 58-61
Cramer, E., Walcher, S. & Wittich, O. (2014). Studierfähigkeit im Fach Mathematik: Anmerkungen zu einem vernachlässigten Thema (In: S. Lin-Klitzing, D. DI Fuccia, R. Stengl-Jörns: Abitur und Studierfähigkeit). Bad Heilbrunn: Julius Klinkhardt.
Cramer, E., Walcher, S. & Wittich, O. (2015). Mathematik und die INT-Fächer. In: J. Roth, Th. Bauer, H. Koch & S. Prediger (Hrsg.), Übergänge konstruktiv gestalten ‐ Ansätze für eine zielgruppenspezifische Hochschuldidaktik Mathematik (Konzepte und Studien zur Hochschuldidaktik und Lehrerbildung) (S.51-68). Berlin: Springer.
Cramer, E., Walcher, S. & Wittich, O. (2016). Mathematik(-Didaktik) für WiMINT. In: R. Dürr, K. Dürrschnabel, F. Loose & R. Wurth (Hrsg.), Mathematik zwischen Schule und Hochschule (S.99-115). Berlin: Springer.. Verfügbar unter
Tagungsbeiträge (Sortieren nach: Erscheinungsdatum | Titel)
Cramer, E., Kamps, U. & Rychlik, T. (2002). Moments of generalized order statistics. In: H. Langseth & B. Lindqvist (Hrsg.), Proceedings of the Third International Conference on Mathematical Methods in Reliability MMR2002 in Trondheim (S.165-168). Trondheim: NTNU.
Zeitschriften (Sortieren nach: Erscheinungsdatum | Titel)
Gorny, J. & Cramer, E. (2019). A volume based approach to establish B-spline based expressions for density functions and its application to progressive hybrid censoring. J. Korean Statist. Soc., to appear
Gorny, J. & Cramer, E. (2019). On exact inferential results for a simple step-stress model under a time constraint. Sankhya B, to appear
Jablonka, A., Cramer, E. & Hermanns, M. (2019). Statistical inference for coherent systems with Weibull distributed component lifetimes under complete and incomplete information. Appl. Stoch. Models Bus. Ind., to appear
Laumen, B. & Cramer, E. (2019). Progressive Censoring with Fixed Censoring Times. Statistics, to appear
van Bentum, Th. & Cramer, E. (2019). Stochastic monotonicity of MLEs of the mean for exponentially distributed lifetimes under hybrid censoring. Statist. Probab. Letters (148), 1-8
Gorny, J. & Cramer, E. (2018). Exact inference for a new flexible hybrid censoring scheme. Journal of the Indian Society for Probability and Statistics, 19, 169-199
Ottermanns, R., Cramer, E., Daniels, B., Lehmann, R. & Roß-Nickoll, M. (2018). Uncertainty in site classification and its sensitivity to sample size and indicator quality - Bayesian misclassification rate in ecological risk assessment. Ecological Indicators, 94, 348-356
Cramer, E. & Davies, K. (2018). Restricted optimal progressive censoring. Comm. Stat. Sim. Comp., 47, 1216-1239
Gorny, J. & Cramer, E. (2018). Type-I hybrid censoring of uniformly distributed lifetimes. Commun. Statist. Theory Methods, to appear
Gorny, J. & Cramer, E. (2018). Modularization of hybrid censoring schemes and its application to unified progressive hybrid censoring. Metrika, 81, 173-210
Hermanns, M. & Cramer, E. (2018). Inference with progressively censored k-out-of-n system lifetime data. TEST, 27, 787-810
Gorny, J. & Cramer, E. (2017). From B-spline representations to gamma representations in hybrid censoring. Stat. Papers, to appear
Hermanns, M. & Cramer, E. (2017). Likelihood inference for the component lifetime distribution based on progressively censored parallel systems data. J. Stat. Comp. Simul., 87, 607-630
Alimohammadi, M., Alamatsaz, M. H. & Cramer, E. (2016). Convolutions and Generalization of Logconcavity: Implications and Applications. Nav. Res. Logistics, 63, 109–123
Burkschat, M., Cramer, E. & Gorny, J. (2016). Type-I censored sequential k-out-of-n systems. Appl. Math. Model., 40, 8156–8174
Cramer, E. & Navarro, J. (2016). The progressive censoring signature of coherent systems. Appl. Stoch. Models Bus. Ind. , 32, 697–710
Cramer, E., Burkschat, M. & Gorny, J. (2016). On the exact distribution of the MLEs based on Type-II progressively hybrid censored data from exponential distributions. J. Statist. Comp. Simul., 86, 2036-2052
Abbasi, S., Alamatsaz, H. M. & Cramer, E. (2016). Preservation properties of stochastic orderings by transformation to Harris family with different tilt parameters. Lat. Am. J. Probab. Math. Stat., 13, 465-479
Gorny, J. & Cramer, E. (2016). Exact Likelihood Inference for Exponential Distributions under Generalized Progressive Hybrid Censoring Schemes . Statist. Methodology, 29, 70-94
Cramer, E. & Navarro, J. (2015). Progressive Type-II censoring and coherent systems. Nav. Res. Logist., 62, 512-530
Alimohammadi, M., Alamatsaz, M. H. & Cramer, E. (2015). Discrete strong unimodality of order statistics. Statist. Probab. Letters, 103, 176-185
Laumen, B. & Cramer, E. (2015). Likelihood inference for the lifetime performance index under progressive Type-II censoring. Econ. Quality Control, 30, 59-73
Cramer, E. (2014). Extreme value analysis for progressively Type-II censored order statistics. Commun. Statist. Theory Methods, 43, 2135-2155
Beutner, E. & Cramer, E. (2014). Using linear interpolation to reduce the order of the coverage error of nonparametric prediction intervals based on right-censored data. J. Multivariate Anal., 129, 95-109
Volterman, W., Balakrishnan, N. & Cramer, E. (2014). Exact meta-analysis of several independent progressively Type-II censored data. Appl. Math, Model., 38, 949–960
Balakrishnan, N., Cramer, E. & Iliopoulos, G. (2014). On the method of pivoting the CDF for exact confidence intervals with illustration for exponential mean under life-test with time constraints. Statist. Probab. Letters, 89, 124-130
Alimohammadi, M., Alamatsaz, M. H. & Cramer, E. (2014). Some convexity properties of the distribution of lower k-record values with extensions. Probab. Eng. Inf. Sci., 28, 389-399
Volterman, W., Cramer, E., Davies, K. & Balakrishnan, N. (2014). Further results on order statistics generated by two simulation methods. Commun. Statist. Simul. Comput., 43, 2732-2743
Cramer, E. & Tamm, M. (2014). On a correction of the scale MLE for a two-parameter exponential distribution under progressive Type-I censoring. Commun. Statist. Theory Methods, 43, 4401-4414
Cramer, E. & Balakrishnan, N. (2013). On some exact distributional results based on Type-I progressively hybrid censored data from exponential distributions. Statist. Methodology, 10, 128-150
Rezapoor, M., Alamatsaz, M.H., Balakrishnan, N. & Cramer, E. (2013). On properties of progressively Type-II censored order statistics arising from dependent and non-identical random variables. Statist. Methodology, 10, 58-71
Balakrishnan, N., Beutner, E. & Cramer, E. (2013). Computational aspects of statistical intervals based on two Type-II censored samples. Comp. Statist., 28 (3), 893-917
Volterman, W., Balakrishnan, N. & Cramer, E. (2012). Exact nonparametric meta-analysis for multiple Independent doubly Type-II censored samples. Comput. Statist. Data Anal., 56, 1243-1255
Cramer, E. & Naehrig, G. (2012). Laplace record data. J. Statist. Plann. Inference, 142, 2179-2189
Tamm, M., Cramer, E., Kennes, L.N. & Heussen, N. (2012). Influence of selection bias on the test decision: A simulation study. Methods of Information in Medicine, 51, 138-143
Burkschat, M. & Cramer, E. (2012). Fisher information in generalized order statistics. Statistics (46), 719-743
Burkschat, M., Cramer, E. & Dahmen, K. (2012). A- and D-optimal progressive Type-II censoring designs based on Fisher information. J. Statist. Comput. Simul., 82, 879-905
Cramer, E. & Ensenbach, M. (2011). Asymptotically optimal progressive censoring plans based on Fisher information. J. Statist. Plann. Inference, 141, 1968-1980
Schenk, N., Burkschat, M., Cramer, E. & Kamps, U. (2011). Bayesian estimation and prediction with multiply Type-II censored samples of sequential order statistics from one- and two-parameter exponential distributions. J. Statist. Plann. Inference , 141, 1575-1587
Kennes, L., Cramer, E., Hilgers, R.-D. & Heussen, N. (2011). The impact of selection bias on test decisions in randomized clinical trials. Statistics in Medicine, 30, 2573–2581
Cramer, E. & Bagh, C. (2011). Minimum and maximum information censoring plans in progressive censoring. Commun. Statist. Theory Methods, 40, 2511-2527
Beutner, E. & Cramer, E. (2011). Confidence intervals for quantiles in a minimal repair set-up [Special Issue on Statistical Methods in Biostatistics and Reliability]. Int. J. Appl. Math. Statist., 24, 86-97
Balakrishnan, N., Cramer, E. & Dembinska, A. (2011). Characterizations of geometric distribution through progressively Type-II right censored order statistics. Statistics, 59, 559-573
Cramer, E. & Schmiedt, A. B. (2011). Progressively Type-II censored competing risks data from Lomax distributions. Comp. Statist. Data Anal., 55, 1285-1303
Balakrishnan, N., Beutner, E. & Cramer, E. (2010). Exact two-sample non-parametric confidence, prediction, and tolerance intervals based on ordinary and progressively Type-II right censored data. TEST, 19, 68-91
Cramer, E. & Lenz, U. (2010). Association of progressively Type-II censored order statistics. J. Statist. Plann. Inference, 140, 576-583
Beutner, E. & Cramer, E. (2010). Nonparametric meta-analysis for minimal repair systems. Aust. N. Z. J. Statist., 52, 383-401
Cramer, E. & Iliopoulos, G. (2010). Adaptive progressive Type-II censoring. TEST, 19, 342-358
Balakrishnan, N., Cramer, E. & Davies, K. (2009). Some results on order statistics generated by two simulation methods. Statist. Probab. Letters, 79, 1847-1857
Cramer, E. (2009). Hermite interpolation polynomials and distributions of ordered data. Statist. Methodology, 6, 337-343
Cramer, E., Herle, K. & Balakrishnan, N. (2009). Permanent expansions and distributions of order statistics in the INID case. Commun. Statist. Theory Methods, 38, 2078-2088
Cramer, E. & Tran, T.T.H. (2009). Generalized order statistics from arbitrary distributions and the Markov chain property. J. Statist. Plann. Inference (139), 4064--4071
Balakrishnan, N., Burkschat, M., Cramer, E. & Hofmann, G. (2008). Fisher information based progressive censoring plans. Comp. Statist. Data Analysis, 53, 366-380
Balakrishnan, N., Burkschat, M. & Cramer, E. (2008). Best linear equivariant estimation and prediction in location-scale families. Sankhya B, 70, 229-247
Fischer, T., Balakrishnan, N. & Cramer, E. (2008). Mixture representation for order ptatistics from INID progressive censoring and its applications. J. Multivariate Analysis, 99, 1999-2015
Balakrishnan, N. & Cramer, E. (2008). Progressive Censoring from Heterogeneous Distributions with Applications to Robustness. Ann. Inst. Statist. Math., 60, 151-171
Burkschat, M., Cramer, E. & Kamps, U. (2007). Optimality criteria and optimal schemes in progressive censoring. Commun. Statist. Theory Methods, 36, 1419-1431
Cramer, E. (2006). Dependence Structure of Generalized Order Statistics. Statistics, 40, 409-413
Burkschat, M., Cramer, E. & Kamps, U. (2006). On optimal schemes in progressive censoring. Statist. Probab. Letters, 76, 1032-1036
Hofmann, G., Cramer, E., Balakrishnan, N. & Kunert, G. (2005). An asymptotic approach to progressive censoring. J. Stat. Plann. Inference, 130, 207-227
Balakrishnan, N., Cramer, E. & Kamps, U. (2005). Relation for joint densities of progressively censored order statistics. Statistics, 39, 529-536
Cramer, E., Kamps, U. & Keseling, C. (2004). Characterizations via linear regression of ordered random variables: A unifying approach. Commun. Statist.Theory Methods, 33, 2885-2911
Cramer, E., Kamps, U. & Rychlik, T. (2004). Unimodality of uniform generalized order statistics, with applications to mean bounds. Ann. Inst. Statist. Math., 56, 183-192
Cramer, E. (2004). Logconcavity and unimodality of progressively censored order statistics. Stat. Probab. Lett., 68, 83-90
Cramer, E., Kamps, U. & Raqab, M. (2003). Characterizations of exponential distributions by spacings of generalized order statistics. Appl. Math., 30, 257-265
Burkschat, M., Cramer, E. & Kamps, U. (2003). Dual generalized order statistics. Metron, 61, 13-26
Cramer, E. & Kamps, U. (2003). Marginal distributions of sequential and generalized order statistics. Metrika, 58, 293-310
Cramer, E., Kamps, U. & Rychlik, T. (2002). Evaluations of expected generalized order statistics in various scale units. Appl. Math., 29, 285-295
Cramer, E., Kamps, U. & Schenk, N. (2002). On the joint completeness of independent statistics. Statist. Decisions, 20, 269-277
Cramer, E., Kamps, U. & Rychlik, T. (2002). On the existence of moments of generalized order statistics. Statist. Probab. Letters, 59, 397-404
Cramer, E. (2002). A note on moments of progressively type II censored order statistics. Commun. Statist. Theory Methods, 31, 1301-1307
Balakrishnan, N., Cramer, E., Kamps, U. & Schenk, N. (2001). Progressive type II censored order statistics from exponential distributions. Statistics, 35, 537-556
Kamps, U. & Cramer, E. (2001). On distributions of generalized order statistics. Statistics, 35, 269-280
Balakrishnan, N., Cramer, E. & Kamps, U. (2001). Bounds for means and variances of progressive type II censored order statistics. Statist. Probab. Letters, 54, 301-315
Cramer, E. (2001). Inference for stress-strength models based on Weinman multivariate exponential samples. Commun. Statist. Theory Methods, 30, 331-346
Cramer, E. & Kamps, U. (2001). Estimation with sequential order statistics from exponential distributions. Ann. Inst. Statist. Math., 53, 307-324
Cramer, E. (2000). Probability measures with given marginals and conditionals: I-projections and conditional iterative proportional fitting. Statist. & Decisions, 18, 311-329
Cramer, E. (2000). Asymptotic properties of estimators of the sample size in a record model. Statistical Papers, 41, 159-171
Cramer, E. & Kamps, U. (2000). Relations for expectations of functions of generalized order statistics. J. Statist. Planning Inference, 89, 79-89
Cramer, E. & Nasri-Roudsari, D. (1999). On the convergence rates of extreme generalized order statistics. Extremes, 2, 421-447
Cramer, E. (1999). Estimation of the mean and the covariance matrix under a marginal independence assumption - an application of matrix differential calculus. Linear Algebra Appl., 288, 219-228
Cramer, E. (1998). Conditional iterative proportional fitting for Gaussian distributions. J. Multivariate Anal., 65, 261-276
Cramer, E. & Kamps, U. (1998). Sequential k-out-of-n systems with Weibull components. Economic Quality Control, 13, 227-239
Cramer, E. & Kamps, U. (1997). A note on the UMVUE of Pr(X<Y) in the exponential case. Commun. Statist. Theory Methods, 26, 1051-1055
Cramer, E. & Kamps, U. (1997). The UMVUE of P(X<Y) based on Type-II censored samples from Weinman multivariate exponential distributions. Metrika, 46, 93-121
Cramer, E. & Bock, H.-H. (1996). Continuous-time Markov chains and compound Poisson processes with circulant intensity matrices. Optimization, 37, 385-392
Cramer, E. & Kamps, U. (1996). Sequential order statistics and k-out-of-n systems with sequentially adjusted failure rates. Ann. Inst. Statist. Math., 48, 535-549
Cramer, E. & Nasri-Roudsari, D. (1995). Die Siebformel von Poincaré-Sylvester und "Runs", Eine Anwendung in der Informatik. Stochastik in der Schule, 15, 13-22