Detecting influential observations in nonlinear regression modeling of groundwater flow

Water Resources Research

Volumn 34, Issue 7, 1998, Pages 1623-1633

  Yager, Richard M ^a  

^a U S GEOLOGICAL SURVEY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AQUIFER; GROUNDWATER FLOW; PARAMETERIZATION; REGRESSION MODELING;

AQUIFERS; FLOW RATE; GROUNDWATER; GROUNDWATER FLOW; GROUNDWATER RESOURCES; REGRESSION ANALYSIS;

DISCHARGE OBSERVATIONS; FRACTURED ROCK AQUIFERS; GROUNDWATER FLOW MODEL; HORIZONTAL HYDRAULIC CONDUCTIVITIES; INFLUENTIAL OBSERVATIONS; NON-LINEAR REGRESSION; NONLINEAR REGRESSION MODELS; STATISTICAL PARAMETERS;

PARAMETER ESTIMATION;

EID: 0031811167     PISSN: 00431397     EISSN: None     Source Type: Journal    
DOI: 10.1029/98WR01010     Document Type: Article

References (16)

1. Belsley, D. A., E. Kuh, and R. E. Welsch, Regression Diagnostics: Identifying Influential Data and Source of Collinearity, John Wiley, New York, 1980.
2. Cook, R. D., and S. Weisberg, Residuals and Influence in Regression, Monogr. Stat. Appl. Probability, vol. 18, Chapman and Hall, New York, 1982.
3. Cooley, R. L., Confidence intervals for ground-water models using linearization, likelihood, and bootstrap methods, Ground Water, 35(5), 869-880, 1997.
4. Cooley, R. L., and R. L. Naff, Regression modeling of ground-water flow, U.S. Geol. Surv. Tech. Water Resour. Invest., Book 3, Chap. B4, 232 pp., 1990.
5. Doherty, J., PEST, Watermark Comput., Corinda, Australia, 1994.
6. Donaldson, J. R., and P. V. Tyron, Nonlinear Least-Squares Regression Using STARPAC: The Standards Times Series and Regression Package, Natl. Inst. of Stand. and Technol., Gaithersburg, Md., 1983.
7. Hill, M. C., A computer program (MODFLOWP) for estimating parameters of a transient, three-dimensional, ground-water flow model using nonlinear regression, U.S. Geol. Surv. Open File Rep., 91-484, 1992.
8. Kitanidis, P. K., Statistical estimation of polynomial covariance functions and hydrologic applications, Water Resour. Res., 19(4), 909-921, 1983.
9. Linssen, H. N., Nonlinearity measures: A case study, Stat. Ned., 29, 93-99, 1975.
10. Poeter, E. P., and M. C. Hill, Inverse models: A necessary next step in ground-water modeling, Ground Water, 35(2), 250-260, 1997.
11. Rawlings, J. O., Applied Regression Analysis, Wadsworth and Brooks, Pacific Grove, Calif., 1988.
12. Rochelle, B. P., C. I. Liff, W. G. Campbell, D. L. Cassell, M. R. Church, and R. A. Nusz, Regional relationships between geomorphic/hydrologic parameters and surface water chemistry relative to acidic depostion, J. Hydrol., 112, 103-120, 1989.
13. Seber, G. A. F., and C. J. Wild, Nonlinear Regression, John Wiley, New York, 1989.
14. Tasker, G. D., and J. R. Stedinger, An operational GLS model for hydrologic regression, J. Hydrol., 111, 361-375, 1989.
15. Yager, R. M., Simulated three-dimensional ground-water flow in the Lockport Group, a fractured dolomite aquifer near Niagara Falls, New York, U.S. Geol. Surv. Water Supply Pap., 2487, 1997.
16. Yager, R. M., and W. M. Kappel, Characterization of fractures in the Lockport Dolomite, Niagara County, New York, in Pollution, Risk Assessment and Remediation in Groundwater Systems, edited by R. M. Khanbilvardi and J. Fillos, Washington, D. C., pp. 149-195, Sci. Publ., 1987.