Regression models for outlier identification (Hurricanes and typhoons) in wave hindcast databases

Journal of Atmospheric and Oceanic Technology

Volumn 29, Issue 2, 2012, Pages 267-285

  Minguez R ^a   Reguero, B G ^a   Luceno A ^b   Mendez F J ^a  

^a ENVIRONMENTAL HYDRAULICS INSTITUTE IH CANTABRIA   (Spain)

^b UNIVERSITY OF CANTABRIA   (Spain)

Author keywords

Error analysis; Ocean models; Regression analysis; Statistical techniques

Indexed keywords

BAD DATA; CARIBBEAN SEA; COASTAL STRUCTURES; CONSTANT TIME; CONTINUOUS TIME; CORRECTION TECHNIQUES; GULF OF MEXICO; HINDCASTS; NORTH ATLANTIC OCEAN; NUMERICAL MODELS; OCEAN MODEL; OUTLIER IDENTIFICATION; REGRESSION MODEL; SPATIAL COVERAGE; SPATIO-TEMPORAL RESOLUTION; STATISTICAL CHARACTERIZATION; STATISTICAL TECHNIQUES; WAVE CLIMATES; WAVE HINDCAST; WAVE PREDICTION MODELS; WIND FIELD;

DATABASE SYSTEMS; ERROR ANALYSIS; MATHEMATICAL MODELS; OCEAN CURRENTS; REGRESSION ANALYSIS; TIME SERIES;

HURRICANES;

DATABASE; ERROR ANALYSIS; HINDCASTING; HURRICANE; NUMERICAL MODEL; REGRESSION ANALYSIS; TYPHOON; WAVE MODELING; WIND FIELD;

ATLANTIC OCEAN; ATLANTIC OCEAN (NORTH); CARIBBEAN SEA; GULF OF MEXICO;

EID: 84857188847     PISSN: 07390572     EISSN: None     Source Type: Journal    
DOI: 10.1175/JTECH-D-11-00059.1     Document Type: Article

References (68)

1. Atkinson, A. C., 1984: Fast very robust methods for the detection of multiple outliers. J. Amer. Stat. Assoc., 89, 1329- 1339.
2. Atkinson, A. C., 1985: Plots, Transformations, and Regression: An Introduction to Graphical Methods of Diagnostic Regression Analysis. Clarendon Press, 282 pp.
3. Barnett, V., and T. Lewis, 1994: Outliers in Statistical Data. 3rd ed. John Wiley & Sons, 604 pp.
4. Barrett, B. E., and J. B. Gray, 1997: Leverage, residual, and interaction diagnostics for subsets of cases in least squares regression. Comput. Stat. Data Anal., 26, 39-52.
5. Bazaraa, M. S., H. D. Sherali, and C. M. Shetty, 1993: Nonlinear Programming: Theory and Algorithms. 2nd ed. John Wiley & Sons, 656 pp.
6. Belsley, D. A., E. Kuh, and R. E. Welsch, 1980: Regression Diagnostics: Identifying Influential Data and Sources of Multicollinearity. John Wiley & Sons, 320 pp.
7. Billor, N., S. Chatterjee, and A. S. Hadi, 2001: Iteratively reweighted least squares method for outlier detection in linear regression. Bull. Int. Stat. Inst., 1, 470-472.
8. Brooke, A., D. Kendrick, A. Meeraus, and R. Raman, 1998: GAMS: A user's guide. GAMS Development Corporation, Washington, DC, 305 pp.
9. Caires, S., and A. Sterl, 2005: A new non-parametric method to correct model data: Application to significant wave height from the ERA-40 Re-Analysis. J. Atmos. Oceanic Technol., 22, 443-459.
10. Caires, S., A. Sterl, J. Bidlot, N. Graham, and V. Swail, 2004: Intercomparison of different wind-wave reanalyses. J. Climate, 17, 1893-1913.
11. Cardone, V. J., E. G. Ward, and W. J. Pierson, 1976: Hindcasting the directional spectra of hurricane-generated waves. J. Pet. Technol., 28 (4), 385-394.
12. Cardone, V. J., R. E. Jensen, D. T. Resio, V. R. Swail, and A. T. Cox, 1996: Evaluation of contemporary ocean wave models in rare extreme events: The " Halloween storm" of October 1991 and the "Storm of the Century" of March 1993. J. Atmos. Oceanic Technol., 13, 198-230.
13. Castillo, E., A. S. Hadi, A. J. Conejo, and A. Fernández-Canteli, 2004: A general method for local sensitivity analysis with application to regression models and other optimization problems. Technometrics, 46 (4), 430-445.
14. Castillo, E., A. S. Hadi, N. Balakrishnan, and J. M. Sarabia, 2005: Extreme Value and Related Models with Applications in Engineering and Science. John Wiley & Sons, 362 pp.
15. Castillo, E., A. J. Conejo, C. Castillo, R. Mínguez, and D. Ortigosa, 2006: Perturbation approach to sensitivity analysis in nonlinear programming. J. Optim. Theory Appl., 128, 49-74.
16. Cavaleri, L., and L. Bertotti, 2004: Accuracy of the modelled wind and wave fields in enclosed seas. Tellus, 56A, 167-175.
17. Cavaleri, L., and M. Sclavo, 2006: The calibration of wind and wave model data in the Mediterranean Sea. Coast. Eng., 53, 613-627.
18. Chatterjee, S., and A. S. Hadi, 1986: Influential observations, high leverage points, and outliers in linear regression. Stat. Sci., 1 (3), 379-393.
19. Chatterjee, S., and A. S. Hadi, 1988: Sensitivity Analysis in Linear Regression. John Wiley & Sons, 315 pp.
20. Chatterjee, S., and M. Mächler, 1997: Robust regression: A weighted least squares approach. Comm. Stat. Theory Methods, 26 (6), 1381- 1394.
21. Cheng, T.-C., 2011: Robust diagnostics for the heteroscedastic regression model. Comput. Stat. Data Anal., 55, 1845-1866, doi: 10.1016/j.csda.2010.11.024.
22. Coleman, T. F., and Y. Li, 1994: On the convergence of reflective Newton methods for large-scale nonlinear minimization subject to bounds. Math. Programm., 67 (2), 189-224.
23. Coleman, T. F., and Y. Li, 1996: An interior, trust region approach for nonlinear minimization subject to bounds. SIAM J. Optim., 6, 418-445.
24. Coles, S., 2001: An Introduction to Statistical Modeling of Extreme Values. Springer Series in Statistics, Springer, 224 pp.
25. Cook, R. D., 1986: Assessment of local influence (with discussion). J. Roy. Stat. Soc., 48B, 133-169.
26. Cook, R. D., and S. Weisberg, 1982: Residuals and Influence in Regression. Springer, 240 pp.
27. Daniel, C., and F. S. Wood, 1980: Fitting Equations to Data: Computer Analysis of Multifactor Data. Wiley Series in Probability and Mathematical Statistics, John Wiley & Sons, Inc., 458 pp.
28. Dodet, G., X. Bertin, and R. Taborda, 2010: Wave climate variability in the North-East Atlantic Ocean over the last six decades. Ocean Modell., 31, 120-131.
29. Draper, N. R., and H. Smith, 1981: Applied Regression Analysis. 2nd ed. Wiley Series in Probability and Statistics, John Wiley & Sons, Inc., 709 pp.
30. Gray, J. B., 1986: A simple graphic for assessing influence in regression. J. Stat. Comput. Simulat., 24, 121-134.
31. Gray, J. B., and R. F. Ling, 1984: K-clustering as a detection tool for influential subsets in regression (with discussion). Technometrics, 26, 305-330.
32. Hadi, A. S., and J. S. Simonoff, 1993: Procedures for the identification of multiple outliers in linear models. J. Amer. Stat. Assoc., 88, 1264-1272.
33. Hawkins, D. M., 1980: Identification of Outliers. Springer, 188 pp.
34. Hoaglin, D. C., and R. E. Welsch, 1978: The hat matrix in regression and ANOVA. Amer. Stat., 32, 17-22.
35. Huber, P. J., 1981: Robust Statistics. Wiley Series in Probability and Mathematical Statistics, John Wiley & Sons, Inc., 320 pp.
36. Jarvinen, B. R., C. J. Neumann, and M. A. S. Davis, 1984: A tropical cyclone data tape for the North Atlantic basin, 1886-1983: Contents, limitations, and uses. NOAA Tech. Memo. NWS NHC 22, National Hurricane Center, 24 pp. [Available online at http://www.nhc.noaa.gov/pdf/NWS-NHC-1988-22.pdf.]
37. Jones, W. D., and R. F. Ling, 1988: A new unifying class of influence measures for regression diagnostics. Proc. Stat. Comput. Sect., American Statistical Association, 305-310.
38. Krogstad, H. E., and S. F. Barstow, 1999: Satellite wave measurements for coastal engineering applications. Coastal Eng., 37, 283-307.
39. Landsea, C. W., and Coauthors, 2004: The Atlantic hurricane database reanalysis project: Documentation for the 1851- 1910 alterations and additions to the HURDAT database. Hurricanes and Typhoons: Past, Present, and Future, R. J. Murnane and K. Liu, Eds., Columbia University Press, 177- 221.
40. Landsea, C. W., and Coauthors, 2008: A reanalysis of the 1911-20 Atlantic hurricane database. J. Climate, 21, 2138-2168.
41. Luceño, A., 1997: Estimation of missing values in possibly partially nonstationary vector time series. Biometrika, 84, 495-499, doi:10.1093/biomet/84.2.495.
42. Luceño, A., 1998a: Detecting possibly non-consecutive outliers in industrial time series. J. Roy. Stat. Soc., 60B, 295-310.
43. Luceño, A., 1998b: Multiple outliers detection through reweighted least deviances. Comput. Stat. Data Anal., 26, 313-326, doi:10.1016/S0167-9473(97)00036-4.
44. Luenberger, D. G., 1984: Linear and Nonlinear Programming. 2nd ed. Addison-Wesley, 491 pp.
45. Mayo, M. S., and J. B. Gray, 1997: Elemental subsets: The building blocks of regression. J. Amer. Stat. Assoc., 51, 122-129.
46. Mínguez, R., A. Espejo, A. Tomás, F. J. Méndez, and I. J. Losada, 2011: Directional calibration of wave reanalysis databases using instrumental data. J. Atmos. Oceanic Technol., 28, 1466-1485.
47. Murtagh, B. A., and M. A. Saunders, 1978: Large-scale linearly constrained optimization. Math. Programm., 14, 41-72.
48. Murtagh, B. A., and M. A. Saunders, 1998: MINOS 5.5 users' guide. Rep. SOL 83-20R, Department of Operations Research, Stanford University, Stanford, CA, 150 pp.
49. Nyquist, H., 1992: Sensitivity analysis in empirical studies. J. Off. Stat., 8, 167-182.
50. Paul, S. R., and K. Y. Fung, 1991: A generalized extreme studentized residual multiple-outlier-detection procedure in linear regression. Technometrics, 33, 339-348.
51. Peña, D., and V. Yohai, 1995: The detection of influential subsets in linear regression by using an influence matrix. J. Roy. Stat. Soc., 57B, 339-348.
52. Pilar, P., S. Guedes Soares, and J. Carretero, 2008: 44-year wave hindcast for the North East Atlantic European coast. Coast. Eng., 55 (11), 861-871.
53. Powell, M. D., S. H. Houston, L. R. Amat, and N. Morisseau-Leroy, 1998: The HRD real-time hurricane wind analysis system. J. Wind Eng. Ind. Aerodyn., 77-78, 53-64, doi:10.1016/S0167-6105(98)00131-7.
54. Pregibon, D., 1981: Logistic regression diagnostics. Ann. Stat., 9, 705-724.
55. Rousseeuw, P. J., and A. M. Leroy, 1987: Robust Regression and Outlier Detection. Wiley Series in Probability and Statistics, John Wiley & Sons, 352 pp.
56. Rousseeuw, P. J., and K. Van Driessen, 1999: A fast algorithm for the minimum covariance determinant estimator. Technometrics, 41, 212- 223.
57. Schwarzmann, B., 1991: A connection between local-influence analysis and residual diagnostics. Technometrics, 33, 103-104.
58. Simonoff, J. S., 1991: Directions in robust statistics and diagnostics: Part II. General Approaches to Stepwise Identification of Unusual Values in Data Analysis, W. Stahel and S. Weisberg, Eds., Springer-Verlag, 223-242.
59. Tolman, H., 1997: User manual and system documentation of WAVEWATCH-III version 1.15. Tech. Note 151, NOAA/NWS/NCEP/OMB, 97 pp.
60. Tolman, H., 1999: User manual and system documentation of WAVEWATCH-III version 1.18. Tech. Note 166, NOAA/NWS/NCEP/OMB, 110 pp.
61. Tolman, H., 2002: User manual and system documentation of WAVEWATCH-III version 2.22. Tech. Note 222, NOAA/NWS/NCEP, 133 pp.
62. Tomás, A., F. J. Méndez, and I. J. Losada, 2008: A method for spatial calibration of wave hindcast data bases. Cont. Shelf Res., 28, 391-398.
63. Verboven, S., and M. Hubert, 2005: Libra: A matlab library for robust analysis. Chemometr. Intell. Lab. Syst., 75 (2), 127-136, doi:10.1016/j.chemolab.2004.06.003.
64. WAMDI Group, 1988: The WAM model-A third generation ocean wave prediction model. J. Phys. Oceanogr., 18, 1775- 1810.
65. Weissfeld, I., and H. Schneider, 1990a: Influence diagnostics for the normal linear model with censored data. Aust. J. Stat., 32, 11-20.
66. Weissfeld, I., and H. Schneider, 1990b: Influence diagnostics for the weibull model fit to censored data. Stat. Probab. Lett., 9, 67-73.
67. Winsnowski, W. J., D. C. Montgomery, and R. S. James, 2001: A comparative analysis of multiple outlier detection procedures in the linear regression model. Comput. Stat. Data Anal., 36, 351-382.
68. Wolfe, P., 1963: Methods of nonlinear programming. Recent Advances in Mathematical Programming, R. L. Graves and P. Wolfe, Eds., McGraw-Hill, 76-77.