Bibliography
1998; Wiley; Linguagem: Inglês
10.1002/9781118625590.biblio
ISSN1940-6347
AutoresNorman R. Draper, Harry Smith,
Tópico(s)Leaf Properties and Growth Measurement
ResumoFree Access Bibliography Norman R. Draper, Norman R. DraperSearch for more papers by this authorHarry Smith, Harry SmithSearch for more papers by this author Book Author(s):Norman R. Draper, Norman R. DraperSearch for more papers by this authorHarry Smith, Harry SmithSearch for more papers by this author First published: 09 April 1998 https://doi.org/10.1002/9781118625590.biblioBook Series:Wiley Series in Probability and Statistics AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Bibliography Adcock, R. J. (1878). A problem in least squares. Analyst, 5, 53–54. Adichie, J. N. (1967). Estimates of regression parameters based on rank test. Annals of Mathematical Statistics, 38, 894–904. Aia, M. A., Goldsmith, R. L., and Mooney, R. W. (1961). Predicting stoichiometric CaHP04 · 2H20. Industrial and Engineering Chemistry, 53, January, 55–57. Aitken, M., D. Anderson, B. Francis, and J. Hinds (1989). Statistical Modelling in GLIM. Oxford, UK: Clarendon Press. Albert, J., and M. Berliner (1994). Review of the “Resampling Stats” computer package. The American Statistician, 48, 129–131. Amer, F. A., and W. T. Williams (1957). Leaf-area growth in Pelargonium zonale. Annals of Botany, New Series, 21, 339–342. Anderson, D. A., and R. G. Scott (1974). The application of ridge regression analysis to a hydrologie target-control model. Water Resources Bulletin, 10, 680–690. Anderson-Sprecher, R. (1994). Model comparisons and R2. The American Statistician, 48, 113–117. Andrews, D. F. (1974). A robust method for multiple linear regression. Technometrics, 16, 523–531. Andrews, D. F., P. J. Bickel, F. R. Hampel, P. J. Huber, W. H. Rogers, and J. W. Tukey (1972). Robust Estimates of Location. Princeton, NJ: Princeton University Press. Andrews, D. F., and D. Pregibon (1978). Finding the outliers that matter. Journal of the Royal Statistical Society, Series B, 4, 84–93. Anscombe, F. J., and J. W. Tukey (1963). The examination and analysis of residuals. Technometrics, 5, 141–160. Arthanari, T. S., and Y. Dodge (1981). Mathematical Programming in Statistics. New York: Wiley. Askin, R. G., and D. C. Montgomery (1980). Augmented robust estimators. Technometrics, 22, 333–341. Atkinson, A. C. (1985). Plots, Transformations and Regression. Oxford, UK: Clarendon Press. Atkinson, A. C. (1994). Fast very robust methods for the detection of multiple outliers. Journal of the American Statistical Association, 89, 1329–1339. Barker, F., Y. C. Soh, and R. J. Evans (1988). Properties of the geometric mean functional relationship. Biometrics, 44, 279–281. Barnett, V. D. (1967). A note on linear structural relationships when both residual variances are known. Biometrika, 54, 670–672. Barnett, V. D. (1975). Probability plotting methods and order statistics. Applied Statistics, 24, 95–108. Barnett, V. D., and T. Lewis (1994). Outliers in Statistical Data, 3rd ed. New York: Wiley. Barrodale, I., and F. D. K. Roberts (1974). Algorithm 478: solution of an overdetermined system of equations in the l1 norm. Communications of the Association for Computing Machinery, 17, 319–320. Bartlett, M. S. (1947). The use of transformations. Biometrics, 3, 39–52. Bartlett, M. S. (1949). Fitting a straight line when both variables are subject to error. Biometrics, 5, 207–212. Bates, D. M., and D. G. Watts (1988). Nonlinear Regression Analysis and Its Applications. New York: Wiley. Bauer, F. L. (1971). Elimination with weighted row combinations for solving linear equations and least squares problems. In J. H. Wilkinson and C. Reisch (Eds.). Handbook for Automatic Computation Volume II: Linear Algebra. New York: Springer Verlag. Becker, W., and P. Kennedy (1992). A lesson in least squares and R squared. The American Statistician, 46, 282–283. Beaton, A. E., and J. W. Tukey (1974). The fitting of power series, meaning polynomials, illustrated on band spectroscopic data. Technometrics, 16, 147–185. Belsley, D. A. (1991). Conditioning Diagnostics, Collinearity and Weak Data in Regression, New York: Wiley. (This book is a revision of the following book.) Belsley, D. A., E. Kuh, and R. E. Welsch (1980). Regression Diagnostics, New York: Wiley. Berk, K. N., and D. E. Booth (1995). Seeing a curve in multiple regression. Technometrics, 37, 385–398. Berkson, J. (1950). Are there two regressions? Journal of the American Statistical Association, 45, 164–180. Bing, J. (1994). How to standardize regression coefficients. The American Statistician, 48, 209–213. Birch, J. B., and D. B. Agard (1993). Robust inferences in regression: a comparative study. Communications in Statistics, Simulation and Computation, 22(1), 217–244. Birkes, D., and Y. Dodge (1993). Alternative Methods of Regression. New York: Wiley. Bisgaard, S., and H. T. Fuller (1994–95). Analysis of factorial experiments with defects or defectives as the response. Quality Engineering, 7(2), 429–443. Bloomfield, P.. and W. Steiger (1983). Least Absolute Deviations: Theory, Applications, and Algorithms. Boston: Birkhäuser. Box, G. E. P. (1966). Use and abuse of regression. Technometrics, 8, 625–629. Box, G. E. P., and G. A. Coutie (1956). Application of digital computers in the exploration of functional relationships. Proceeding of the Institution of Electrical Engineers, 103, Part B, Suppl. No. 1, 100–107. Box, G. E. P., and D. R. Cox (1964). An analysis of transformations. Journal of the Royal Statistical Society, Series B, 26, 211–243 (discussion pp. 244–252). Box, G. E. P., and N. R. Draper (1965). The Bayesian estimation of common parameters from several responses. Biometrika, 52, 355–365. Box, G. E. P., and N. R. Draper (1987). Empirical Model-Building and Response Surfaces. New York: Wiley. Box, G. E. P., and J. S. Hunter (1954). A confidence region for the solution of a set of simultaneous equations with an application to experimental design. Biometrika, 41, 190–199. Box, G. E. P., and W. G. Hunter (1962). A useful method for model building. Technometrics, 4, 301–318. Box, G. E. P., and W. G. Hunter (1965). Sequential design of experiment for nonlinear models. Proceedings of the IBM Scientific Computing Symposium on Statistics, October 21–23, 1963, pp. 113–137. Box, G. E. P., W. G. Hunter, and J. S. Hunter (1978). Statistics for Experimenters. New York: Wiley. Box, G. E. P., G. M. Jenkins, and G. C. Reinsel (1994). Time Series Analysis, Forecasting and Control, 3rd ed. Englewood Cliffs, NJ: Prentice Hall. Box, G. E. P., and H. L. Lucas (1959). Design of experiments in non-linear situations. Biometrika, 46, 77–90. Box, G. E. P., and J. Wetz (1973). Criteria for judging adequacy of estimation by an approximating response function. University of Wisconsin Statistics Department Technical Report No. 9. Box, J. F. (1978). R. A. Fisher: The Life of a Scientist. New York: Wiley. Breiman, L. (1995). Better subset regression using the nonnegative garrote. Technometrics, 37, 373–384. Breiman, L. (1996). Heuristics of instability and stabilization in model selection. Annals of Statistics, 24, 2350–2383. Brieman, L., and J. H. Friedman (1997). Predicting multivariate responses in multiple linear regression. Journal of the Royal Statistical Society, Series B, 59(1), 3–37 (discussion pp. 37–54). Bright, J. W.. and G. S. Dawkins (1965). Some aspects of curve fitting using orthogonal polynomials. Industrial and Engineering Chemistry Fundamentals, 4, February, 93–97. Bring, J. (1994). How to standardize regression coefficients. The American Statistician, 48, 209–213. Brown, P. J., and C. Payne (1975). Election night forecasting. Journal of the Royal Statistical Society, Series A, 138, 463–483 (discussion pp. 483–498). Brownlee, K. A. (1965). Statistical Theory and Methodology in Science and Engineering, 2nd ed. New York: Wiley. Bunke, H., and O. Bunke (1989). Nonlinear Regression, Functional Relations and Robust Methods. New York: Wiley. Bunke, O. (1975). Minimax linear, ridge and shrunken estimators for linear parameters. Mathematische Operationsforchimg und Statistiks, 6, 697–701. Carroll, R. J.. and D. Ruppert (1988). Transformations and Weighting in Regression. London: Chapman and Hall. Carroll, R. J., and H. Schneider (1985). A note on Levene's tests for equality of variances. Statistics and Probability Letters, 3, 191–194. Chappell, R. (1994). Presenting the coefficients of the linear quadratic formula for clinical use. International Journal of Radiation Oncology, Biology, Physics, 29(1), 191–193. Chatterjee, S., and A. S. Hadi (1988). Sensitivity Analysis in Linear Regression. New York: Wiley. Chaturvedi, A. (1993). Ridge regression estimators in the linear regression models with non-spherical errors. Communications in Statistics, Theory and Methods, 22(8), 2275–2284. Clayton, D. G. (1971). Gram–Schmidt orthogonalization. Applied Statistics, 20, 335–338 (Fortran algorithm). Cleveland, W. S., and B. Kleiner (1975). A graphical technique for enhancing scatter plots with moving statistics. Technometrics, 17, 447–454. Collett, D. (1991). Modelling Binary Data. London: Chapman and Hall. Coniffe, D., and J. Stone (1973). A critical view of ridge regression. The Statistician, 22, 181–187. Cook, R. D. (1977). Detection of influential observations in linear regression. Technometrics, 19, 15–18. Cook, R. D., and S. Weisberg (1982). Residuals and Influence in Regression. London: Chapman and Hall. Cook, R. D., and S. Weisberg (1994). Transforming a response variable for linearity. Biometrika, 81, 731–737. Cooper, B. E. (1971). The use of orthogonal polynomials with equal x-values. Applied Statistics, 20, 209–213. Copas, J. B. (1983). Regression, prediction and shrinkage. Journal of the Royal Statistical Society, Series B, 45, 311–335 (discussion pp. 335–354). Cornell, J. A. (1990). Experiments with Mixtures, 2nd ed. New York: Wiley. Cox, D. R., and E. J. Snell (1989). Analysis of Binary Data. London: Chapman and Hall. Creasy, M. A. (1956). Confidence limits for the gradient in the linear functional relationship. Journal of the Royal Statistical Society, Series B, 18, 65–69. Crouse, R. H., C. Jin, and R. C. Hanumara (1995). Unbiased ridge estimation with prior information and ridge trace. Communications in Statistics, Theory and Methods, 24(9), 2341–2354. Daniel, C., and F. S. Wood, assisted by J. W. Gorman. (1980) Fitting Equations to Data, 2nd ed. New York: Wiley Davies, P. L. (1987). Asymptotic behaviour of S-estimates of multivariate location parameters and dispersion matrices. Annals of Statistics, 15, 1269–1292. Davies, L. (1990). The asymptotics of S-estimators in the linear regression model. Annals of Statistics, 18, 1651–1675. Davies, L. (1992). The asymptotics of Rousseeuw's minimum volume ellipsoid estimator. Annals of Statistics, 20, 1828–1843. De Lury, D. B. (1960). Values and Integrals of the Orthogonal Polynomials up to n = 26. Toronto: University of Toronto Press. Derringer, G. C. (1974). An empirical model for viscosity of filled and plasticized elastomer products. Journal of Applied Polymer Science, 18, 1083–1101. Dielman, T., and R. Pfaffenberger (1982). LAV (least absolute value) estimation in linear regression: a review. In S. H. Zanakis and J. S. Rustagi (eds.). Optimization in Statistics. New York: North-Holland. Dielman, T., and R. Pfaffenberger (1990). Tests of linear hypotheses and LAV estimation: a Monte Carlo comparison. Communications in Statistics, Simulation and Computation, 19, 1179–1199. Diggle, P. J. (1990). Time Series, A Biostatistical Introduction. Oxford, UK: Clarendon Press/Oxford Science Publications. Ding, C. G., and R. E. Bargmann (1991). Statistical algorithms AS 260 and AS 261, concerning the distribution of R2. Applied Statistics, 40, 195–198. Dixon, W. J. (chief ed.). Biomedical Computer Programs, P-Series. Berkeley: University of California Press. (Seek the most recent update.) Dobson, A. J. (1990). Introduction to Generalized Linear Models. London: Chapman and Hall. Y. Dodge (ed.) (1987a). Statistical Data Analysis Based on the L1-Norm and Related Methods. New York: North-Holland. Dodge, Y. (ed.) (1987b). Special Issue on Statistical Data Analysis Based on the L1, Norm and Related Methods. Computational Statistics & Data Analysis, 5, 237–450. Y. Dodge (ed.) (1992). L1-Statistical Analysis and Related Methods. Amsterdam: North-Holland. Dodge, Y. (1996). The guinea pig of multiple regression. In H. Rieder (ed.). Lecture Notes in Statistics, No. 109, see pp. 91–118. New York: Springer. Dolby, J. L. (1963). A quick method for choosing a transformation. Technometrics, 5, 317–325. Donoho, D. L., and P. J. Huber (1983). The notion of breakdown point. In P. J. Bickel, K. Doksum and J. L. Hodges (eds.). A Festschrift for Erich Lehmann, pp. 157–184. Belmont, CA: Wadsworth. Draper, N. R. (1984). The Box–Wetz criterion versus R2. Journal of the Royal Statistical Society, Series A, 147, 100–103. Also (1985), 148, 357. Draper, N. R. (1992). Straight line regression when both variables are subject to error. Proceedings of the 1991 Kansas State University Conference on Applied Statistics in Agriculture, pp. 1–18. Draper, N. R., P. Prescott, S. M. Lewis, A. M. Dean, P. W. M. John, and M. G. Tuck (1993). Mixture designs for four components in orthogonal blocks. Technometrics, 35, 268–276. Also see (1995), 37, 131–132. Draper, N. R., and I. Guttman (1995). Confidence intervals versus regions. The Statistician, 44, 399–403. Draper, N. R., and A. M. Herzberg (1987). A ridge-regression sidelight. The American Statistician, 41, 282–283. Draper, N. R., and W. G. Hunter (1969). Transformations: some examples revisited. Technometrics, 11, 23–40. Draper, N. R., and R. C. Van Nostrand (1979). Ridge regression and James–Stein estimation: review and comments. Technometrics, 21, 451–466. Draper, N. R., and Y. (Fred) Yang (1997). Generalization of the geometric mean functional relationship. Computational Statistics and Data Analysis, 23, 355–372. Dressler, A. (1984). International kinematics of galaxies in cluster, I. Velocity dispersions for elliptical galaxies in Coma and Virgo. Astrophysical Journal, 281, 512–524. Driscoll, M. F., and D. J. Anderson (1980). Point-of-expansion, structure and selection in multivariate polynomial regression. Communications in Statistics, Theory and Methods, A9, 821–836. Durbin, J. (1969). Tests for serial correlation in regression analysis based on the periodogram of least squares residuals. Biometrika, 56, 1–15. Durbin, J. (1970). An alternative to the bounds test for testing for serial correlation in least squares regression. Econometrica, 38, 422–429. Durbin, J., and G. S. Watson (1950). Testing for serial correlation in least squares regression, I. Biometrika, 37 409–428. Durbin, J., and G. S. Watson (1951). Testing for serial correlation in least squares regression, II. Biometrika, 38 159–178. Durbin, J., and G. S. Watson (1971). Testing for serial correlation in least squares regression, III. Biometrika, 58, 1–19. Dutter, R. (1977). Numerical solution of robust regression problems: computational aspects, a comparison. Journal of Statistical Computing and Simulation, 5, 207–238. Edgington, E. (1980). Randomization Tests, 2nd ed. New York: Marcel Dekker. Efron, B. (1982). The Jackknife, the Bootstrap, and Other Resampling Procedures. Philadelphia: SI AM Publications. Efron, B., and R. Tibshirani (1993). Introduction to the Bootstrap. London: Chapman and Hall. Egerton, G. M., and P. J. Laycock (1981). Some criticisms of stochastic shrinkage and ridge regression, with counterexamples. Technometrics, 23, 155–159. (Also see letter, 25, 1983, 303–304 and correction, 304.) Eisenhart, C. (1964). The meaning of “least” in least squares. Journal of the Washington Academy of Sciences, 54, 24–33. Ellerton, R. R. W. (1978). Is the regression equation adequate—a generalization. Technometrics, 20, 313–315. Erjavec, J., G. E. P. Box, W. G. Hunter, and J. F. MacGregor (1973). Some problems associated with the analysis of multiresponse data. Technometrics, 15, 33–51. Fahrmeir, L., and G. Tutz (1994). Multivariate Statistical Modelling Based on Generalized Linear Models. New York: Springer-Verlag. Farebrother, R. W. (1974). Gram–Schmidt regression. Applied Statistics, 23, 470–476 (Algol 60 algorithm). Farebrother, R. W. (1988). Algorithm AS 238: a simple recursive procedure for the L1 norm fitting of a straight line. Applied Statistics, 37, 457–465. Feller, W. (1957). An Introduction to Probability Theory and Its Applications, Volume 7, 2nd ed. New York: Wiley. Fisher, R. A., and F. Yates (1964). Statistical Tables for Biological, Agricultural and Medical Research, 6th ed. New York: Hafner Publishing. Forsythe, A. B. (1972). Robust estimation of straight line regression coefficients by minimizing pth power deviations. Technometrics, 14, 159–166. Francis, B., M. Green, and C. Payne (1993). The GLIM System: Release 4 Manual. New York: Oxford University Press. Frank, I., and J. Friedman (1993). A statistical view of some chemometrics regression tools. Technometrics, 35, 109–135 (discussion pp. 136–148). See p. 124. Freeman, M. F., and J. W. Tukey (1950). Transformations related to the angular and the square root. Annals of Mathematical Statistics, 21(4), 607–611. Freund, R. J. (1988). When is $$Rˆ2 > r_{yx_1 }ˆ2 + r_{yx_2 }ˆ2 $$? (Revisited). The American Statistician, 42, 89–90. Freund, R., and R. Littell (1991). SAS System for Regression, 2nd ed. Cary, NC: SAS Institute. Fiile, E. (1995). On ecological regression and ridge estimation. Communications in Statistics, Simulation and Computation, 24(2), 385–398. Furnival, G. M., and R. W. Wilson (1974). Regression by leaps and bounds. Technometrics, 16, 499–511. Gallant, A. R. (1987). Nonlinear Statistical Models. New York: Wiley. Galton, F. (1877). Typical laws of heredity in man. Address to the Royal Institution, England. Galton, F. (1885). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute, 15, 246–263. Galton, F. (1885). Presidential address to Section H of the British Association, printed in Nature, September, 507–510. Gentle, J. E., S. C. Narula, and V. A. Sposito (1987). Algorithms for unconstrained L1 linear regression. In Y. Dodge (ed.). Statistical Data Analysis Based on the L1-Norm and Related Methods. New York: North-Holland. Gibbons, D. G. (1981). A simulation study of some ridge estimators. Journal of the American Statistical Association, 76, 131–139. Gibson, W. M., and G. H. Jowett (1957). Three-group regression analysis, Part I. Applied Statistics, 6, 114–122. Gilmour, S. G. (1996). The interpretation of Mallows Cp-statistic. The Statistician, 45, 49–56. Goldstein, M., and A. F. M. Smith (1974). Ridge-type estimators for regression analysis. Journal of the Royal Statistical Society, Series B, 36, 284–291. Good, P. (1994). Permutation Tests: A Practical Guide to Resampling Methods for Testing Hypotheses. New York: Springer-Verlag. Gorman, J. W., and R. J. Toman (1966). Selection of variables for fitting equations to data. Technometrics, 8, 27–51. Gray, J. B., and W. H. Woodall (1994). The maximum size of standardized and internally studentized residuals in regression analysis. The American Statistician, 48, 111–113. Graybill, F. A. (1961). An Introduction to Linear Statistical Models. New York: McGraw-Hill. Grechanovsky, E., and I. Pinsker (1995). Conditional p-values for the F-statistic in a forward selection procedure. Computational Statistics and Data Analysis, 20, 239–263. Green, P. J., and B. W. Silverman (1994). Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach. London: Chapman and Hall. Gregory, F. G. (1928). Studies in the energy relations of plants, II. Annals of Botany, 42, 469–507. Hadi, A. S. (1992). A new measure of overall potential influence in linear regression. Computational Statistics and Data Analysis, 14, 1–27. Haid, A. (1952). Statistical Theory with Engineering Applications. New York: Wiley. Hall, P. (1995). The Bootstrap and Edgeworth Expansion. New York: Springer-Verlag. Hamilton, D. (1987a). Sometimes $$Rˆ2 > r_{yx_1 }ˆ2 + r_{yx_2 }ˆ2 $$. The American Statistician, 41, 129–132. Hamilton, D. (1987b). Reply to Freund and Mitra. The American Statistician, 42, 90–91. Hampel, F. R. (1971). A general qualitative definition of robustness. Annals of Mathematical Statistics, 42, 1887–1896. Hampel, F. R. (1974). The influence curve and its role in robust estimation. Journal of the American Statistical Association, 69, 383–393. Hampel, F. R. (1975). Beyond location parameters: robust concepts and methods. Proceedings of the 40th Session of the ISI, 46, 375–391. Hampel, F. R. (1978). Optimally bounding the gross-error-sensitivity and the influence of position in factor space. Proceedings of the Statistical Computing Section, pp. 59–64. Washington, DC: American Statistical Association. Hampel, F. R., E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel (1986). Robust Statistics: The Approach Based on Influence Functions. New York: Wiley. Hartley, H. O. (1961). The modified Gauss–Newton method for the fitting of non-linear regression functions by least squares. Technometrics, 3, 269–280. Harvey, P. H., and G. M. Mace (1982). Comparison between taxa and adaptive trends: problems of methodology. In Current Problems in Sociobiology. New York: Cambridge University Press. Harville, D. A. (1997). Matrix Algebra From a Statistician's Perspective. New York: Springer-Verlag. Hawkins, D. M. (1980). Identification of Outliers. London: Chapman and Hall. Healy, M. J. R. (1984). The use of R2 as a measure of goodness of fit. Journal of the Royal Statistical Society, Series A, 147, 608–609. Healy, M. J. R. (1988). GLIM: An Introduction. Oxford, UK: Oxford University Press. Herschel, J. F. W. (1849). Outliers of Astronomy. Philadelphia: Lee & Blanchard. Hettmansperger, T. P., and S. J. Sheather (1992). A cautionary note on the method of least median squares. The American Statistician, 46, 79–83. Hilbe, J. M. (1994). Generalized linear models. The American Statistician, 48, 255–265. This article reviews seven software packages: GAIM (Version 1.1), GENSTAT 5 (Release 2), GLIM (Release 4.0), SAS 6.08 for Windows, S-Plus for Windows (Version 3.1), XLISP-Stat (Version 2.1R3), and XploRe (Version 3.1). Hill, R. W. (1979). On estimating the covariance matrix of robust regression M-estimates. Communications in Statistics, A8, 1183–1196. Hill, R. W., and P. W. Holland (1977). Two robust alternatives to least squares regression. Journal of the American Statistical Association, 72, 828–833. Hines, R. J. O., and W. G. S. Hines (1995). Exploring Cook's statistic graphically. The American Statistician, 49, 389–394. Hjorth, J. S. U. (1994). Computer Intensive Statistical Methods. London: Chapman and Hall. Hocking, R. R. (1996). Methods and Applications of Linear Models. New York: Wiley. Hoerl, A. E. (1954). Fitting curves to data. Chemical Business Handbook. New York: McGraw-Hill. Hoerl, A. E., and R. W. Kennard (1970a). Ridge regression: biased estimation for non-orthogonal problems. Technometrics, 12, 55–67. Hoerl, A. E., and R. W. Kennard (1970b). Ridge regression: applications to non-orthogonal problems. Technometrics, 12, 69–82 (correction, 12, 723). Hoerl, A. E., and R. W. Kennard (1975). A note on a power generalization of ridge regression. Technometrics, 17, 269. Hoerl, A. E., and R. W. Kennard (1976). Ridge regression: iterative estimation of the biasing parameter. Communications in Statistics, A5, 77–88. Hoerl, A. E., and R. W. Kennard (1981). Ridge Regression 1980, Advances, Algorithms, and Applications. Columbus, OH: American Sciences Press. Hoerl, A. E., R. W. Kennard, and K. F. Baldwin (1975). Ridge regression, some simulations. Communications in Statistics, A4, 105–123. Hogg, R. V. (1979a). Statistical robustness: one view of its use in applications today. The American Statistician, 33, 108–115. Hogg, R. V. (1979b). An introduction to robust estimation. In R. L. Launer and G. N. Wilkinson (eds.). Robustness in Statistics, pp. 1–18. New York: Academic Press. Hogg, R. V., and R. H. Randies (1975). Adaptive distribution-free regression methods and their applications. Technometrics, 17, 399–407. Holland, P. W., and R. E. Welsch (1977). Robust regression using iteratively reweighted least-squares. Communications in Statistics, A6, 813–888. Hosmer, D. W. Jr., and S. Lemeshow (1989). Applied Logistic Regression. New York: Wiley. Huber, P. (1964). Robust estimation of a location parameter. Annals of Mathematical Statistics, 35, 73–101. Huber, P. J. (1972). Robust statistics: a review. Annals of Mathematical Statistics, 43, 1041–1067. Huber, P. J. (1973). Robust regression: asymptotics, conjectures, and Monte Carlo. Annals of Statistics, 1, 799–821. Huber, P. J. (1975). Robustness and designs. A Survey of Statistical Design and Linear Models. Amsterdam: North-Holland. Huber, P. (1981). Robust Statistics. New York: Wiley. Huber, P. J. (1983). Minimax aspects of bounded-influence regression (with discussion). Journal of the American Statistical Association, 78, 66–80. Huff, D. (1954). How to Lie with Statistics. New York: W. W. Norton. Hunter, W. G., and R. Mezaki (1964). A model-building technique for chemical engineering kinetics. American Institute of Chemical Engineering Journal, 10, 315–322. [But note error in text below Table 4, p. 320; if the sixth residual were positive (it is just negative) the pattern would be obvious.] Jackson, J. E., and W. H. Lawton (1967). Answer to Query 22. Technometrics, 9(2), 339–340. Jaske, D. R. (1994). Illustrating the Gauss–Markov theorem. The American Statistician, 48, 237–238. Jefferys, W. H. (1990). Robust estimation when more than one variable per equation of condition has error. Biometrika, 77, 597–607. John, J. A., and N. R. Draper (1980). An alternative family of transformations. Applied Statistics, 29, 190–197. Joiner, B. L., and J. R. Rosenblatt (1971). Some properties of the range in samples from Tukey's symmetric λ distributions. Journal of the American Statistical Association, 66, 394–399. Judge, G. G., and T. Takayama (1966). Equality restrictions in regression analysis. Journal of the American Statistical Association, 61, 166–181. Jureckova, J., and S. Portnoy (1987). Asymptotics for one-step M-estimators with application to combining efficiency and high breakdown point. Communications in Statistics,
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