Microergodicity effects on ebullition of methane modelled by Mixed Poisson process with Pareto mixing variable

Chemometrics and Intelligent Laboratory Systems

Volumn 128, Issue , 2013, Pages 124-134

  Jordanova, P ^a   Dusek J ^c   Stehlik M ^b  

^a SHUMEN UNIVERSITY   (Bulgaria)

^b JOHANNES KEPLER UNIVERSITY LINZ   (Austria)

^c CzechGlobe Global Change Research Centre AS CR   (Czech Republic)

Author keywords

Ebullition of methane; Environmental chemistry; Mixed Poisson processes; Moving average process; Pareto distribution; Renewal process; Robust statistics; Sedge grass marsh

Indexed keywords

METHANE;

ARTICLE; CHEMICAL PARAMETERS; CHEMOMETRICS; CONTROLLED STUDY; CZECH REPUBLIC; EBULLITION; ERLANG PARETO METHOD; EXPONENTIAL PARETO METHOD; MATHEMATICAL PARAMETERS; MICROERGODICITY EFFECT; MIXED POISSON PROCESS; PARETO MIXING VARIABLE; PRIORITY JOURNAL; RANDOMIZATION;

EID: 84883698426     PISSN: 01697439     EISSN: 18733239     Source Type: Journal    
DOI: 10.1016/j.chemolab.2013.08.006     Document Type: Article

References (20)

1. Yamamoto S., Alcauskas J.B., Crozier T.E. Solubility of methane in distilled water and seawater. J. Chem. Eng. Data 1976, 21:78-80.
2. Martens C.S., Klump J.V. Biogeochemical cycling in an organic-rich coastal basin 1.Methane sediment-water exchange processes. Geochim. Cosmochim. Acta 1980, 44:471-490.
3. Jordanova P., Dušek J., Stehlík M. Modeling methane emission by the infinite moving average process. Chemom. Intell. Lab. Syst. 2013, 122:40-49.
4. Stehlík M., Střelec L., Thulin M. On robust testing for normality in chemometrics. Chemom. Intell. Lab. Syst. 2013, (under revision).
5. Davis R., Resnick S. Limit theory for moing averages of random variable with regularly varying tail probabilities. Ann. Probab. 1985, 13(1):179-195.
6. Resnick S.I. Heavy-Tail Phenomena 2007, Springer, New York.
7. Leadbetter M.R. On extreme values in stationary sequences. Z. Wahrsch. Verw. Gebiete 1974, 28:289-303.
8. Resnick S.I. Weak convergence to extremal processes. Ann. Probab. 1975, 3:951-960.
9. Weissman I. Multivariate extremal processes generated by independent non-identically distributed random variable. J. Appl. Probab. 1975, 12:477-487.
10. Addiscott T.M., Mirza N.A. New paradigms for modelling mass transfers in soils. Soil Tillage Res. 1998, 47:105-109.
11. Karlis D., Xekalaki E. Mixed Poisson distributions. Int. Stat. Rev. 2005, 73(1):35-58.
12. Grandell J. Mixed Poisson Processes 1997, Chapman and Hall, London.
13. Olver Fr.W.J., Lozier D.W., Boisvert R.F., Clark Ch.W. NIST Handbook on Mathematical Functions 2010, Cambridge University Press.
14. Nadarajah S., Kotz S. On the Laplace Transform of the Pareto Distribution Queueing Systems 2006, 54(4):243-244.
15. Milgram M.S. The generalized integro-exponential function. Math. Comput. 1985, 44:443-458.
16. Mikosch T. Non-Lide Insurance Mathematics: An Introduction with Stochastic Processes 2004, Springer-Verlag, Berlin Heidelberg.
17. Willmot G.E. On recursive evaluation of mixed Poisson probabilities and related quantities. Scand. Actuar. J. 1993, 2:114-133.
18. Felgueiras M.M. Explaining the seismic moment of large earthquakes by heavy and extremely heavy tailed models. Int. J. Geomath. 2012, 3:209-222.
19. Meijer J.W., Baken N.H.G. The exponential integral distribution. Stat. Probab. Lett. 1987, 5:209-211.
20. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing 2013, R Development Core Team, http://www.R-project.org.