ValidationBiblio.pdf
© 2007, Timothy G. Gregoire, Yale University
Last revised: January 2007
VALIDATION BIBLIOGRAPHY
1930-Present (89 entries)
1. Pearson, E.S. and Neyman, J. (1930). On the problem of two samples. In J. Neyman and
E.S. Pearson, 1967, Joint Statistical Papers, Cambridge: Cambridge University Press,
pp. 99-115.
2. Freese, F. (1960). Testing Accuracy. Forest Science 6(2): 139-145.
3. Hahn, G.J. and Nelson, W. (1970). A problem in the statistical comparison of measuring
devices. Technometrics 12(1): 95-102.
4. Rogerson, T.L. (1970). Half-minute counts for neutron probes. Soil Science 110(5): 359360.
5. Bell, J.F. and Groman, W.A. (1971). A field test of the accuracy of the Barr and Stroud
Type FP-12 optical dendrometer. The Forestry Chronicle 47: 69-74.
6. Hazard, J.W. and Berger, J.M. (1972). Volume tables vs. dendrometers for forest surveys.
Journal of Forestry 70(4): 216-219.
7. Maxwell, E.A. (1974). Estimating variances from one or two measurements on each
sample. The American Statistician 28(3): 96-97.
8. Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions, with
discussion. Journal of the Royal Statistical Society–Series B 2: 111-147.
9. Wiant, H.V., Jr. and Koch, C.B. (1974). Predicting diameters inside bark from outside
bark measurements on some Appalachian hardwoods. Journal of Forestry 72(12): 775775.
10. Perng, S.K. and Littell, R.C. (1976). A test of equality of two normal population means
and variances. Journal of the American Statistical Association 71(356): 968-971.
11. Snee, R.D. (1977). Validation of regression models: methods and examples.
Technometrics 19(4): 415-428.
12. Stone, M. (1977). Asymptotics for and against cross-validation. Biometrika 64(1): 29-35.
13. Hanumara, R.C. (1978). A comparison of estimators of the precision of two instruments.
Journal of Statistical Computation and Simulation 7: 213-223.
14. Rennie, J.C. and Wiant, H.V., Jr. (1978). Modification of Freese’s chi-square test of
accuracy. Resource Inventory Notes BLM 14, October 1978. Denver, Colorado: U.S.
Department of Interior, Bureau of Land Management, 3 pages.
ValidationBiblio.pdf
© 2006, Timothy G. Gregoire, Yale University
15. Silen, R.R. and Dimock, E.J., II (1978). Modeling feeding preferences by hare and deer
among Douglas-fir genotypes. Forest Science 24(1): 57-64.
16. Ek, A.R. and Monserud, R.A. (1979). Performance and comparison of stand growth
models based on individual tree and diameter-class growth. Canadian Journal of
Forest Research 9: 231-244.
17. Goulding, C.J. (1979). Validation of growth models used in forest management. New
Zealand Journal of Forestry 24: 108-124.
18. Lawton, W.H., Sylvestre, E.A. and Young-Ferraro, B.J. (1979). Statistical comparison of
multiple analytic procedures: application to clinical chemistry. Technometrics 21(4):
397-416.
19. Swartzman, G. (1979). Evaluation of ecological simulation models. In P.P. Ganapati &
M.L. Rosenzweig (eds.), Contemporary Quantitative Ecology and Related Ecometrics
(pp. 295-318). Burtonsville, Maryland: International Co-operative Publishing House.
20. Thies, W.G. and Harvey, R.D., Jr. (1979). A photographic method for measuring tree
defect. Canadian Journal of Forest Research 9: 541-543.
21. Balci, O. and Sargent, R.G. (1981). A methodology for cost-risk analysis in the statistical
validation of simulation models. Communications of the ACM 24(11): 190-197.
22. Evert, F. (1981). A model for regular mortality in unthinned white spruce plantations.
The Forest Chronicle 57: 77-79.
23. Loesch, J.G. (1981). Weight relation between paired ovaries of blueback herring.
Progressive Fish-Culturist 43(2): 77-79.
24. Altman, D.G. and Bland, J.M. (1983). Measurement in medicine: the analysis of method
comparison studies. The Statistician 32: 307-317.
25. Breiman, L. and Freedman, D. (1983). How many variables should be entered in a
regression equation? Journal of the American Statistical Association 78(381): 131-136.
26. Copas, J.B. (1983). Regression, prediction and shrinkage. Journal of the Royal Statistical
Society–Series B 45(3): 311-354.
27. Efron, B. (1983). Estimating the error rate of a prediction rule: improvement on crossvalidation. Journal of the American Statistical Association 78(382): 316-331.
28. Hamilton, D.A., Jr. and Brickell, J.E. (1983). Modeling methods for a two-state system
with continuous responses. Canadian Journal of Forest Research 13: 1117-1121.
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© 2006, Timothy G. Gregoire, Yale University
29. Berk, K.N. (1984). Validating regression procedures with new data. Technometrics 26(4):
331-338.
30. Feldman, R.M., Curry, G.L. and Wehrly, T.E. (1984). Statistical procedure for validating
a simple population model. Environmental Entomology 13: 1446-1451.
31. Nautiyal, J.C. and Couto, L. (1984). The nature and uses of the timber production
function: Eucalyptus grandis in Brazil. Forest Science 30(3): 761-773.
32. Picard, R.R. and Cook, R.D. (1984). Cross-validation of regression models. Journal of
the American Statistical Association 79(387): 575-583.
33. Reynolds, M.R., Jr. (1984). Estimating the error in model predictions. Forest Science
30(2): 454-469.
34. Kelly, G.E. (1985). Use of the structural equations model in assessing the reliability of a
new measurement technique. Applied Statistics 34(3): 258-263.
35. Bland, J.M. and Altman, D.G. (1986). Statistical methods for assessing agreement
between two methods of clinical measurement. The Lancet 8476: 307-310.
36. Carroll, R.J. and Spiegelman, C.H. (1986). The effect of ignoring small measurement
errors in precision instrument calibration. Journal of Quality Technology 18(3): 170173.
37. Efron, B. (1986). How biased is the apparent error rate of a prediction rule? Journal of
the American Statistical Association 81(394): 461-470.
38. Gong, G. (1986). Cross-validation, the jackknife and the bootstrap: excess error
estimation in forward logistic regression. 81(393): 108-113.
39. Radford, P.J. and West, J. (1986). Models to minimize monitoring. Water Research
20(8): 1059-1066.
40. Rauscher, H.M. (1986). The microcomputer scientific software series 4: testing
prediction accuracy (General Technical Report No. NC-107, 19 p.). St. Paul, MN: U.S.
Department of Agriculture Forest Service, North Central Forest Experiment Station.
41. Reynolds, M.R., Jr. and Chung, J. (1986). Regression methodology for estimating model
prediction error. Canadian Journal of Forest Research 16: 931-938.
42. Verbyla, D. (1986). Potential prediction bias in regression and discriminant analysis.
Canadian Journal of Forest Research 16: 1255-1257.
43. Altman, D.G. and Bland, J.M. (1987). Letters to the editors: comparing methods of
measurement. Applied Statistics 36: 224-226. With reply from Kelly, G.E., pp.225-227.
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© 2006, Timothy G. Gregoire, Yale University
44. Davis, A.W. and Hayakawa, T. (1987). Some distribution theory relating to confidence
regions in multivariate calibration. Annals of the Institute of Statistical Mathematics
39(A): 141-152.
45. Radford, P.J. and Ruardij, P. (1987). The validation of ecosystem models of turbid
estuaries. Continental Shelf Research 7(11/12): 1483-1487.
46. Brooks, D.G., Carroll, S.S. and Verdini, W.A. (1988). Characterizing the domain of a
regression model. The American Statistician 42(3): 187-190.
47. Burk, T.E. (1988). Prediction error evaluation: preliminary results. In L.C. Wensel and
G.S. Biging (eds.), Forest Simulation Systems, Proceedings of the IUFRO Conference,
1988 November 2-5, Berkeley, California (pp.81-88). California: University of
California, 1990.
48. Gregoire, T.G. and Reynolds, M.R. (1988). Accuracy testing and estimation alternatives.
Forest Science 34(2): 302-320.
49. Radford, P.J., Burkill, P.H., Collins, N.R. and Williams, R. (1988). The validation and
scientific assessment of an ecosystem model of the Bristol Channel. In A. Marani (ed.),
State-of-the-art in Ecological Modelling, Proceedings of the ISEM Conference on
Ecological Modelling (pp.427-442). Oxford: Elsevier.
50. Bradley, E.L. and Blackwood, L.G. (1989). Comparing paired data: a simultaneous test
for means and variances. The American Statistician 43(4): 234-235.
Reynolds, M.R., Jr. and Gregoire, T.G. (1991). Comment on Bradley and Blackwood.
The American Statistician 45(2): 163-164.
51. Freedman, L.S. and Pee, D. (1989). Return to a note on screening regression equations.
The American Statistician 43(4): 279-282.
52. Lin, L.I. (1989). A concordance correlation coefficient to evaluate reproducibility.
Biometrics 45: 255-268.
53. Eubank, R.L. and LaRiccia, V.N. (1990). Components of Pearson’s phi-squared distance
measure for the k-sample problem. Journal of the American Statistical Association
85(410): 441-445.
54. Picard, R.R. and Berk, K.N. (1990). Data splitting. The American Statistician 44(2): 140147.
55. Rousseeuw, P.J. and van Zomeren, B.C. (1990). Unmasking multivariate outliers and
leverage points. Journal of the American Statistical Association 85(411): 633-639.
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© 2006, Timothy G. Gregoire, Yale University
56. Saveland, J.M. and Neuenschwander, L.F. (1990). A signal detection framework to
evaluate models of tree mortality following fire damage. Forest Science 36(1): 66-76.
57. Schnute, J.T., Mulligan, T.J. and Kuhn, B.R. (1990). An errors-in variables bias model
with an application to salmon hatchery data. Canadian Journal of Fisheries and
Aquatic Sciences 47: 1453-1467.
58. Stout, B.B., Botkin, D.B., Dell, T., Ek, A.R. and Burk, T.E. (1990). Minimum
standards/criteria for assessing models of air pollution impact on stand productivity.
NCASI Technical Bulletin No. 593. New York: National Council of the Paper Industry
for Air and Stream Improvement, Inc.
59. Van Houwelingen, J.C. and Le Cessie, S. (1990). Predictive value of statistical models.
Statistics In Medicine 9: 1303-1325.
60. Blackwood, L.G. and Bradley, E.L. (1991). An omnibus test for comparing two
measuring devices. Journal of Quality Technology 23(1): 12-16.
61. Hamilton, M.A. (1991). Model validation: an annotated bibliography. Communications in
Statistics–Theory and Methods 20(7): 2207-2266.
62. Hirsch, R.P. (1991). Validation samples. Biometrics 47(3): 1193-1194.
63. Freedman, L.S., Pee, D. and Midthune, D.N. (1992). The problem of underestimating the
residual error variance in forward stepwise regression. The Statistician 41: 405-412.
64. Griblo, L.S. and Wiant, H.V., Jr. (1992). A SAS template program for the accuracy test.
The Compiler 10(1): 48-51.
65. Christensen, R. and Blackwood, L.G. (1993). Tests for precision and accuracy of multiple
measuring devices. Technometrics 35(4): 411-420.
66. Clutter, M.L. and Gent, J.A. (1993). Validation and comparison of four cut-over site
prepared loblolly pine growth and yield models. In J.C. Brissette (ed.), Proceedings of
the Seventh Biennial Southern Silvicultural Research Conference, 1992 November 1319, Mobile, Alabama (pp. 593-600). Louisiana, New Orleans: U.S. Department of
Agriculture Forest Service, Southern Forest Experiment Station.
67. Johnson, D. (1993). Email correspondence regarding use of t-test to compare two air
pollution monitors.
68. Mayer, D.G. and Butler, D.G (1993). Statistical validation. Ecological Modelling 68: 2132.
69. Power, M. (1993). The predictive validation of ecological and environmental models.
Ecological Modelling 68: 33-50.
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© 2006, Timothy G. Gregoire, Yale University
70. Schnute, J.T. (1993). Ambiguous inferences from fisheries data. In V. Barnett and
Turkman, K.F. (eds.), Statistics for the Environment (pp. 293-309). New York: Wiley.
71. Shao, J. (1993). Linear model selection by cross-validation. Journal of the American
Statistical Association 88(422): 486-494.
72. Draper, D. (1995). Assessment and propagation of model uncertainty, with discussion.
Journal of the Royal Statistical Society–Series B 57(1): 45-97.
73. Hanumara, R.C. (1994). A note on the estimation of imprecisions of two instruments.
Journal of Statistical Computation and Simulation 51: 1-5.
74. McNulty, S.G., Vose, J.M., Swank, W.T., Aber, J.D. and Federer, C.A. (1994). Regionalscale forest ecosystem modeling: database development, model predictions and
validation using a Geographic Information System. Climate Research 4: 223-231.
75. Oreskes, N., Shrader-Frechette, K. and Belitz, K. (1994). Verification, validation, and
confirmation of numerical models in the earth sciences. Science 263: 641-646.
76. Kleijnen, J.P.C. (1995). Verification and validation of simulation models. European
Journal of Operational Research 82: 145-162.
77. Nelson, L.S. (1995). Single test that two samples came from the same normal population.
Journal of Quality Technology 27(1): 84-87.
78. Smith, E.P. and Rose, K.A. (1995). Model goodness-of-fit analysis using regression and
related techniques. Ecological Modelling 77: 49-64.
79. Rykiel, E.J., Jr. (1996). Testing ecological models: the meaning of validation. Ecological
Modelling 90: 229-244.
80. Wang, Y. and LeMay, V.M. (1996). Sequential accuracy testing plans for the
applicability of existing tree volume equations. Canadian Journal of Forest Research
26: 525-536.
81. Vanclay, J.K. and Skovsgaard, J.P. (1997). Evaluating forest growth models. Ecological
Modelling 98: 1-12.
82. Kleijnen, J.P.C., Bettonvil, B. and Groenendaal, W.V. (1998). Validation of trace-driven
simulation models: a novel regression test. Management Science 44(6): 812-819.
83. Kangas, A.S. (1999). Methods for assessing uncertainty of growth and yield predictions.
Canadian Journal of Forest Research 29: 1357-1364.
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84. Reynolds, J.H. and Ford, E.D. (1999). Multi-criteria assessment of ecological process
models. Ecology 80(2): 538-553.
85. Webster, M.D. and Sokolov, A.P. (2000). A methodology for quantifying uncertainty in
climate projections. Climate Change 46(4): 417-446.
86. Lin, L., Hedayat, A.S., Sinha, B. and Yang, M. (2002). Statistical methods in assessing
agreement: models, issues, and tools. Journal of the American Statistical Association
97(457): 257-270.
87. Kozak, A. and Kozak, R. (2003). Does cross validation provide additional information in
the evaluation of regression models? Canadian Journal of Forest Research 33: 976987.
88. Yang, Y., Monserud, R.A. and Huang, S. (2004). An evaluation of diagnostic tests and
their roles in validating forest biometric models. Canadian Journal of Forest Research
34: 619-629.
89. Hoeting, J.A., Davis, R.A., Merton, A.A. and Thompson, S.E. (2006). Model selection
for geostatistical models. Ecological Applications 16(1): 87-98.
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