An Empirical, Nonparametric Simulator for Multivariate Random Variables with Differing Marginal Densities and Nonlinear Dependence with Hydroclimatic Applications

Risk Analysis

Volumn 36, Issue 1, 2016, Pages 57-73

  Lall, Upmanu ^a,b   Devineni, Naresh ^c,d   Kaheil, Yasir ^e  

^a Center for International Forestry Research   (United States)

^b Department of Earth and Environmental Sciences   (United States)

^c CITY UNIVERSITY OF NEW YORK   (United States)

^d CITY UNIVERSITY OF NEW YORK   (United States)

^e FM Global Engineering and Research   (United States)

Author keywords

Copula; Correlated risk; Logspline; Multivariate nonparametric simulator; Spatial fields

Indexed keywords

AGRICULTURE; DISTRIBUTION FUNCTIONS; PROBABILITY DENSITY FUNCTION; RANDOM VARIABLES; RISK ASSESSMENT; SIMULATORS; VETERINARY MEDICINE;

COPULA; CORRELATED RISKS; LOGSPLINE; NON-PARAMETRIC; SPATIAL FIELDS;

RISK ANALYSIS;

DATA SET; EMPIRICAL ANALYSIS; FIELD METHOD; HISTORICAL RECORD; HYDROMETEOROLOGY; LIVESTOCK; MULTIVARIATE ANALYSIS; NUMERICAL METHOD; RISK ASSESSMENT; SAMPLING; SIMULATOR;

MONGOLIA;

EID: 84956853716     PISSN: 02724332     EISSN: 15396924     Source Type: Journal    
DOI: 10.1111/risa.12432     Document Type: Article

References (42)

1. Christensen JH, Boberg F, Christensen OB, Lucas-Picher P. On the need for bias correction of regional climate change projections of temperature and precipitation. Geophysical Research Letters, 2008; 35(20):L20709.
2. Ehret U, Zehe E, Wulfmeyer V, Warrach-Sagi K, Liebert J. Should we apply bias correction to global and regional climate model data? Hydrology and Earth System Sciences Discussions, 2012; 16(9):3391-3404.
3. Ines AV, Hansen JW. Bias correction of daily GCM rainfall for crop simulation studies. Agricultural and Forest Meteorology, 2006; 138(1):44-53.
4. Teutschbein C, Seibert J. Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology, 2012; 456:12-29.
5. Johnson F, Sharma A. A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations. Water Resources Research, 2012; 48(1):W01504.
6. Clark M, Gangopadhyay S, Hay L, Rajagopalan B, Wilby R. The Schaake shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields. Journal of Hydrometeorology, 2004; 5:243-262.
7. Schaake J, Demargne J, Hartman R, Mullusky M, Welles E, Wu L, Herr H, Fan X, Seo DJ. Precipitation and temperature ensemble forecasts from single-value forecasts. Hydrology and Earth System Science Discussion, 2007; 4:655-717.
8. Luo L, Wood EF. Use of Bayesian merging techniques in a multi-model seasonal hydrologic ensemble prediction system for the eastern U.S. Journal of Hydrometeorology, 2008; 9:866-884, doi:10.1175/2008JHM980.1.
9. Wilks DS. A gridded multisite weather generator and synchronization to observed weather data. Water Resources Research, 2009; 45:W10419, doi: 10.1029/2009WR007902.
10. Turvey CG, Zhao J. Parametric and non-parametric crop yield distributions and their effects on all-risk crop insurance premiums. Working Paper WP 99/05, Department of Food, Agricultural and Resource Economics, University of Guelph, Guelph, 1999.
11. Ozaki VA, Goodwin BK, Ricardo S. Parametric and nonparametric statistical modelling of crop yield: Implications for pricing crop insurance contracts. Applied Economics, 2008; 40:1151-1164.
12. Sklar A. Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut de statistique de l'Université de Paris, 1959; 8:229-231.
13. Joe H. Multivariate Models and Dependence Concepts. London: Chapman & Hall, 1997.
14. Schölzel C, Friederichs P. Multivariate non-normally distributed random variables in climate research: Introduction to the copula approach. Nonlinear Processes Geophysical, 2008; 15:761-772, doi:10.5194/npg-15-761-2008.
15. Genest C, Favre AC. Everything you always wanted to know about copula modeling but were afraid to ask. Journal of Hydrologic Engineering, 2007; 12:347-368.
16. Chebana F, Ouarda TBMJ. Multivariate quantiles in hydrological frequency analysis. Environmetrics, 2011; 22(1):63-78.
17. Silverman BW. Density Estimation for Statistics and Data Analysis, Vol. 26. Boca Raton, FL: CRC Press, 1986.
18. Scott DW. Multivariate Density Estimation: Theory, Practice, and Visualization, Vol. 383. New York: John Wiley & Sons, 2009.
19. Pagan A, Ullah A. Nonparametric Econometrics. Cambridge, UK: Cambridge University Press, 1999.
20. Lall U. Recent advances in nonparametric function estimations: Hydrologic applications. Review of Geophysics, 1995; 33(S2):1093-1102.
21. Kooperberg C, Stone CJ. A study of logspline density estimation. Computational Statistics and Data Analysis, 1991; 12(3):327-347.
22. Kooperberg C, Stone CJ. Logspline density estimation for censored data. Journal of Computational and Graphical Statistics, 1992; 1(4):301-328.
23. Nelson. An Introduction to Copula. New York: Springer-Verlag, 1999.
24. Genest C, Rémillard B, Beaudoin D. Goodness-of-fit tests for copulas: A review and a power study. Insurance: Mathematics and Economics, 2009; 44 (2):199-213.
25. Cherubini U, Luciano E, Vecchiato W. Copula Methods in Finance. New York: Wiley, 2004.
26. Deheuvels P. A non-parametric test for independence. Publications de l'Institut de Statistique de l'Université de Paris, 1981; 26:29-50.
27. Weibull W. A Statistical Theory of Strength of Materials. Stockholm: Ing. Vet. Ak. Handl., 1939.
28. Cunnane C. Unbiased plotting positions-A review. Journal of Hydrology, 1978; 37(3):205-222.
29. Sen B, Banerjee M, Woodroofe M. Inconsistency of bootstrap: The Grenander estimator. Annals of Statistics, 2010; 38(4):1953-1977.
30. Lall U. Will hydrologists learn from the world around them? Empiricism, models, uncertainty and stationarity. AGU Fall Meeting Abstracts, 2010; 1:01.
31. Loader C. Local Regression and Likelihood. New York: Springer, 1999.
32. Aghakouchak A, Grzegorz C, Habib E. Estimation of tail dependence coefficient in rainfall accumulation fields. Advances in Water Resources, 2010; 33(9):1142-1149.
33. AghaKouchak A, Easterling D, Hsu K, Schubert S, Sorooshian S (eds). Extremes in a Changing Climate: Detection, Analysis and Uncertainty. Dordrecht: Springer, 2012.
34. Schmidt R, Stadtmüller U. Non-parametric estimation of tail dependence. Scandinavian Journal of Statistics, 2006; 33(2):307-335.
35. Draisma G, Drees H, Ferreira A, deHaan L. Bivariate tail estimation: Dependence in asymptotic independence. Bernoulli, 2004; 10:251-280.
36. Hüsler J, Li D. Testing asymptotic independence in bivariate extreme. Journal of Statistical Planning and Inference, 2009; 139(3):990-998, doi:10.1016/j.jspi.2008.06.003.
37. Fischer, MJ, Dörflinger M. A note on a non-parametric tail dependence estimator. Diskussionspapiere//Friedrich-Alexander-Universität Erlangen-Nürnberg, Lehrstuhl für Statistik und Ökonometrie, 2006; No. 76.
38. National Statistics Office of Mongolia. Available at: http://en.nso.mn/.
39. Yadamsuren U. Index based livestock insurance project Mongolian case. Conference on Agriculture, Food Security and Climate Change. The Hague, the Netherlands, 2010.
40. Mahul O, Belete N, Goodland A. Innovations in insuring the poor: Index-based livestock insurance in Mongolia. International Food Policy Research Institute, 2009; Focus 17, Brief 9.
41. Mahul O, Skees J. Managing agricultural risk at the country level: The case of index-based livestock insurance in Mongolia. Policy Research Working Paper 4325, World Bank, 2007.
42. Lorenz EN. Deterministic nonperiodic flow. Journal of the Atmospheric Sciences, 1963; 20(2):130-141.