A finite sample comparison of nonparametric estimates of the effective dose in quantal bioassay

Journal of Statistical Computation and Simulation

Volumn 80, Issue 5, 2010, Pages 527-544

  Dette, Holger ^a   Scheder, Regine ^a  

^a RUHR UNIVERSITY BOCHUM   (Germany)

Author keywords

Binary response model; Effective dose level; Isotonic regression; Local linear regression; Nonparametric regression; Order restricted inference

Indexed keywords

EID: 77951167991     PISSN: 00949655     EISSN: 15635163     Source Type: Journal    
DOI: 10.1080/00949650902737465     Document Type: Article

References (22)

1. J. Berkson, Application of the logistic function to bioassay, J. Amer. Statist. Assoc. 39 (1944), pp. 357-365.
2. C.I. Bliss, The method of probits, Science 79 (1934), pp. 38-39.
3. R.A. Woutersen, D. Jonker, H. Stevenson, J.D. te Biesebeek, and W. Slob, The benchmark approach applied to a 28-day toxicity study with Rhodorsil Silane in rats: The impact of increasing the number of dose groups,Food Chem. Toxicol. 39(7) (2001), pp. 697-707.
4. W. Slob, Dose response modeling of continuous endpoints, Toxicol. Sci. 66 (2001), pp. 298-312.
5. D. Krewski, R. Smythe, and K.Y. Fung, Optimal designs for estimating the effective dose in development toxicity experiments, Risk Anal. 22 (2002), pp. 1195-1205.
6. F. Bretz, J.C. Pinheiro, and M. Branson, Combining multiple comparisons and modeling techniques in dose-response studies, Biometrics, 61(3) (2005), pp. 738-748.
7. 50, J. Amer. Statist. Assoc. 74 (1979), pp. 344-354.
8. H. Dette, N. Neumeyer, and K. Pilz, A note on nonparametric estimation of the effective dose in quantal bioassay, J. Amer. Statist. Assoc. 100(470) (2005), pp. 503-510.
9. H.G. Müller and T. Schmitt, Kernel and probit estimates in quantal bioassay, J.Amer. Statist.Assoc. 83(403) (1988), pp. 750-759.
10. D. Park and S. Park, Parametric and nonparametric estimators of ED100α, J. Stat. Comput. Simul. 76(8) (2006), pp. 661-672.
11. R. Bhattacharya and M. Kong, Consistency and asymptotic normality of the estimated effective doses in bioassay, J. Statist. Plann. Inference 137(3) (2007), pp. 643-658.
12. D.L. Hunt and D. Bowman, A parametric model for detecting hormetic effects in developmental toxicity studies, Risk Anal. 24 (2004), pp. 65-72.
13. J. J. Chen and R. L. Kodell, Quantitative risk assessment for teratological effects,J.Amer. Statist.Assoc. 84 (1989), pp. 966-971.
14. M. Ayer, H.D. Brunk, G.M. Ewing, W.T. Reid, and E. Silverman, An empirical distribution function for sampling with incomplete information, Ann. Math. Stat. 26 (1955), pp. 641-647.
15. R.E. Barlow, D.J. Bartholomew, J.M. Bremner, and H.D. Brunk, Statistical Inference under order Restrictions (The Theory and Application of Isotonic Regression), John Wiley & Sons, London-NewYork-Sydney, 1972.
16. T. Gasser and H.G. Müller, Estimating regression functions and their derivatives by the kernel method, Scand. J. Statist. 11 (1984), pp. 171-185.
17. R. Kappenman, Nonparametric estimation of dose-response curves with application to ED50 Estimation, J. Stat. Comput. Simul. 28 (1987), pp. 1-13.
18. B.W. Silvermann, Using kernel density estimates to investigate multimodulity, J. R. Stat. Soc. Ser. B 43 (1981), pp. 97-99.
19. G.H. Hardy, J.E. Littlewood and G. Pólya, Inequalities, Cambridge University Press, Cambridge, (1952).
20. A. Agresti, Categorical Data Analysis,Wiley, 1990.
21. E. T. Lee, A computer program for linear logistic regression analysis, Comput. Programs Biomed. 4 (1974), pp. 80-92.
22. J. Rice, Bandwidth choice for nonparametric regression, Ann. Statist. 12 (1984), pp. 1215-1230.