High-throughput plant phenotyping-Data acquisition, transformation, and analysis

Bioinformatics: Tools and Applications

Volumn , Issue , 2007, Pages 259-278

  Eberius, Matthias ^a   Lima Guerra, José ^b  

^a LemnaTec GmbH   (Germany)

^b KEYGENE N V   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 78649908781     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-0-387-92738-1_13     Document Type: Chapter

References (29)

1. Agresti A (2002) Categorical data analysis. Wiley, New York
2. Bernardo JM, Smith AFM (2002) Bayesian theory. Wiley, New York
3. Bioconductor (2008) http://www.bioconductor.org/. Accessed 13 July 2008
4. Breslow NR, Clayton DG (1993) Approximate inference in generalized linear mixed models. J Am Stat Assoc 88:9
5. Davies ER (2005) Machine vision. Morgan Kaufmann/Elsevier, San Francisco, CA
6. Devore JL (2000) Probability and statistics for engineering and the sciences. ITP, CA
7. Eberius M, Mennicken G, Reuter I, Vandenhirtz J (2002) Sensitivity of different growth inhibition tests-just a question of mathematical calculation? Ecotoxicology 11:293-297
8. Falconer DS (1989) Introduction to quantitative genetics. Longman, London
9. Gonzalez RC, Woods RE (2008) Digital image processing. Pearson Prentice Hall, Upper Saddle River, NJ
10. Hart GW (1995) Multidimensional analysis: algebras and systems for science and engineering. Springer-Verlag, New York
11. Hosmer DW, Lemeshow S (1989) Applied logistic regression. Wiley, New York
12. Jeffreys H (1961) Theory of probability. Clarendom, Oxford
13. Kline SJ (1986) Similitude and approximation theory. Springer, New York
14. Koch CG, Carr GJ, Strokes ME, Uryniak TJ (1990) Categorical data analysis. In: Berry DA (ed) Statistical methodology in pharmaceutical sciences. Marcel Dekker, New York
15. LemnaTec (2008) http://www.lemnatec.de/Dokumente/2007/The moving field concept.pdf. Accessed 21 May 2008
16. ® systems for mixed models. SAS Institute, Cary NC
17. McCllagh P, Nelder JA (1989) Generalized linear models. Chapman and Hall, London
18. Nyholm N (1985) Response variable in algal growth inhibition test-biomass or growth rate? Water Res 19:273-279
19. Nyholm N (1990) Expression of results from growth inhibition toxicity tests with algae. Arch Environ Contam Toxicol 19:518-522
20. PROC VARCLUS (2008) http://www2.sas.com/proceedings/sugi26/p261-26.pdf. Accessed 19 June 2008
21. R®-Project (2008) http://www.r-project.org. Accessed 19 June 2008
22. Royall R (2000) Statistical evidence: a likelihood paradigm. Chapman & Hall/CRC, London
23. Russ JC (1999) The image processing handbook. CRC, Boca Raton, FL
24. SAS (1995) Logistic regression examples using the SAS system. SAS Institute, Cary, NC
25. ® help and documentation (release 9.1.3). SAS Institute, Cary, NC
26. Sonka M, Hlavac V, Boyle R (2008) Image processing, analysis and machine vision. Thomson, Toronto, Canada
27. Stokes M, Davis CS, Koch GG (2000) Categorical data analysis using the SAS system, 2nd edn. SAS, Cary, NC
28. Wedderburn RWM (1974) Quasilikelihood methods, generalized linear models, and the Gauss-Newton method. Biometrika 61:339
29. Weir BS (1996) Genetic data analysis II. Sinauer, Sunderland, MA