Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Automatic Text Generation in Macedonian Using Recurrent Neural Networks Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Application of Hierarchical Bayesian Model in Ophtalmological Study

Authors : Biljana Tojtovska, Panche Ribarski, Antonela Ljubic

Published in: ICT Innovations 2019. Big Data Processing and Mining

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The problems with statistical results based on p-values, together with multiple comparisons have been criticized often in the literature. Many authors argue that this way of reporting scientific research creates unreliable results. This issue is especially important in the era of Big Data, when many tests are done on the same data sets, which are often openly available. A way to overcome these problems is offered by Bayesian analysis. In our previous research we have used traditional statistical approach to conduct multiple hypothesis tests on our data in ophtalmological study. The goal of this paper is to apply the hierarchical Poisson exponential model on the data and test the dependence of congenital heart disease and Brusfield spots. We give detailed description of the model, analyze the generated Markov chains and the posterior distributions for the simulated parameters and discuss the results from Bayesian perspective. The results are original and have not been published yet.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter A Study of Different Models for Subreddit Recommendation Based on User-Community Interaction
next chapter Friendship Paradox and Hashtag Embedding in the Instagram Social Network
Literature
1.
Afifi, H.H., Abdel Azeem, A.A., El-Bassyouni, H.T., Gheith, M.E., Rizk, A., Bateman, J.B.: Distinct ocular expression in infants and children with down syndrome in cairo, Egypt: myopia and heart disease. JAMA Ophthalmol. 131(8), 1057–1066 (2013)CrossRef
2.
Albert, J.H.: Bayesian Methods for Contingency Tables. American Cancer Society (2005)
3.
Bromham, N.R., Woodhouse, J.M., Cregg, M., Webb, E., Fraser, W.I.: Heart defects and ocular anomalies in children with Down’s syndrome. Br. J. Ophthalmol. 86(12), 1367–1368 (2002)CrossRef
4.
da Cunha, R.P., de Castro Moreira, J.B.: Ocular findings in Down’s Syndrome. Am. J. Ophthalmol. 122(2), 236–244 (1996)CrossRef
5.
Davies, G.E., et al.: Genetic variation in the COL6A1 region is associated with congenital heart defects in trisomy 21 (Down’s Syndrome). Ann. Hum. Genet. 59, 253–269 (1995)CrossRef
6.
Eklund, A., Nichols, T.E., Knutsson, H.: Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proc. Natl. Acad. Sci. 113(28), 7900–7905 (2016)CrossRef
7.
8.
Gelman, A.: Prior distributions for variance parameters in hierarchical models (comment on article by Browne and Draper). Bayesian Anal. 1(3), 515–534 (2006)MathSciNetCrossRef
9.
Gelman, A., Hill, J.: Data Analysis Using Regression and Multilevel/Hierarchical Models. Analytical Methods for Social Research. Cambridge University Press, Cambridge (2007)
10.
Gelman, A., Loken, E.: The statistical crisis in science. Am. Sci. 102, 460 (2014)CrossRef
11.
Head, M.L., Holman, L., Lanfear, R., Kahn, A.T., Jennions, M.D.: The extent and consequences of p-hacking in science. PLoS Biol. 13(3), 1–15 (2015)CrossRef
12.
Ioannidis, J.P.A.: Why most published research findings are false. PLOS Med. 2(8) (2005)
13.
Kruschke, J.: Doing Bayesian Data Analysis, 2nd edn. Academic Press, Boston (2015)MATH
14.
Kruschke, J.K.: Bayesian data analysis. Wiley interdisciplinary reviews. Cogn. Sci. 15, 658–676 (2010)
15.
Kruschke, J.K.: Doing Bayesian Data Analysis: A Tutorial with R and BUGS, 1st edn. Academic Press Inc., Burlington (2010)MATH
16.
Ljubic, A., Trajkovski, V., Tesic, M., Tojtovska, B., Stankovic, B.: Ophthalmic manifestations in children and young adults with Down Syndrome and congenital heart defects. Ophthalmic Epidemiol. 22(2), 123–129 (2015)CrossRef
17.
Nuzzo, R.: Scientific method: statistical errors. Nature 506(7487), 150–152 (2014)CrossRef
18.
OSF: Estimating the reproducibility of psychological science. Science 349(6251) (2015)
19.
20.
Rosenthal, R.: The file drawer problem and tolerance for null results. Psychol. Bull. 86(3), 638–641 (1979)CrossRef
21.
Simmons, J.P., Nelson, L.D., Simonsohn, U.: False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol. Sci. 22(11), 1359–1366 (2011)CrossRef
22.
Stirn Kranjc, B.: Ocular abnormalities and systemic disease in Down Syndrome. Strabismus 20, 74–77 (2012)CrossRef
23.
Szucs, D., Ioannidis, J.P.: Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature. bioRxiv (2016)
24.
Tojtovska, B.: Multiple hypothesis testing: adjustment methods with application. In: Proceedings of CIIT 2019–16th International Conference on Informatics and Information Technologies, Mavrovo, N. Macedonia (2019)
25.
Wagenmakers, E.J.: A practical solution to the pervasive problems of p-values. Psychon. Bull. Rev. 14(5), 779–804 (2007)CrossRef
Metadata
Title
Application of Hierarchical Bayesian Model in Ophtalmological Study
Authors
Biljana Tojtovska
Panche Ribarski
Antonela Ljubic
Copyright Year
2019
Book
ICT Innovations 2019. Big Data Processing and Mining

Print ISBN: 978-3-030-33109-2

Electronic ISBN: 978-3-030-33110-8

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-33110-8_10

Premium Partner

    Image Credits