References Abdelbasit, K.M. and Plackett, R.L. (1983). Experimental design for binary data. J. Amer. Statist. Assoc., 78, 90–98. Armitage, P. (1975). Sequential medical trials. Oxford: Blackwell. Aslett, R., Buck, R.J., Duvall, S.G., Sacks, J. and Welch, W.J. (1998). Circuit optimization via sequential computer experiments: design of an output buffer. Appl. Statist., 47, 31–48. Atiqullah, M. and Cox, D.R. (1962). The use of control observations as an alternative to incomplete block designs. J. R. Statist. Soc. B, 24, 464–471. Atkinson, A.C. (1985). Plots, transformation and regression. Oxford University Press. Atkinson, A.C. and Donev, A.N. (1992). Optimal experimental designs. Oxford University Press. Atkinson, A.C. and Donev, A.N. (1996). Experimental designs optimally balanced against trend. Technometrics, 38, 333–341. Aza¨is, J.-M., Monod, H. and Bailey, R.A. (1998). The influence of design on validity and efficiency of neighbour methods. Biometrics, 54, 1374– 1387. Azzalini, A. and Cox, D.R. (1984). Two new tests associated with analysis of variance. J. R. Statist. Soc. B, 46, 335–343. Bailey, R.A. and Rowley, C.A. (1987). Valid randomization. Proc. Roy. Soc. London, A, 410, 105–124. Bartlett, M.S. (1933). The vector representation of a sample. Proc. Camb. Phil. Soc., 30, 327–340. Bartlett, M.S. (1938). The approximate recovery of information from field experiments with large blocks. J. Agric. Sci., 28, 418–427. Bartlett, M.S. (1978). Nearest neighbour models in the analysis of field experiments (with discussion). J. R. Statist. Soc. B, 40, 147–170. Bartlett, R.H., Roloff, D.W., Cornell, R.G., Andrews, A.F., Dillon, P.W. and Zwischenberger, J.B. (1985). Extracorporeal circulation in neonatal respiratory failure: A prospective randomized study. Pediatrics, 76, 479–487. Begg, C.B. (1990). On inferences from Wei’s biased coin design for clinical trials (with discussion). Biometrika, 77, 467–485 Besag, J. and Higdon, D. (1999). Bayesian analysis of agricultural field

© 2000 by Chapman & Hall/CRC

experiments (with discussion). J. R. Statist. Soc. B, 61, 691–746. Beveridge, W.V.I. (1952). The art of scientific investigation. London: Heinemann. Biggers, J.D. and Heyner, R.R. (1961). Studies on the amino acid requirements of cartilaginous long bone rudiments in vitro. J. Experimental Zoology, 147, 95–112. Blackwell, D. and Hodges, J.L. (1957). Design for the control of selection bias. Ann. Math. Statist., 28, 449–460. Blot, W.J. and 17 others (1993). Nutritional intervention trials in Linxian, China: supplementation with specific vitamin-mineral combinations, cancer incidence, and disease-specific mortality in the general population. J. Nat. Cancer Inst., 85, 1483–1492. Booth, K.H.V. and Cox, D.R. (1962). Some systematic supersaturated designs. Technometrics, 4, 489–495. Bose, R.C. (1938). On the application of Galois fields to the problem of the construction of Hyper-Graeco Latin squares. Sankhy¯ a, 3, 323–338. Bose, R.C. and Bush, K.A. (1952). Orthogonal arrays of strength two and three. Ann. Math. Statist., 23, 508–524. Bose, R.C., Shrikhande, S.S. and Parker, E.T. (1960). Further results on the construction of mutually orthogonal Latin squares and the falsity of Euler’s conjecture. Canad. J. Math, 12, 189-203. Box, G.E.P. and Draper, N.R. (1959). A basis for the selection of a response surface design. J. Amer. Statist. Assoc., 54, 622–654. Box, G.E.P. and Draper, N.R. (1969). Evolutionary operation. New York: Wiley. Box, G.E.P. and Hunter, J.S. (1957). Multi-factor experimental designs for exploring response surfaces. Ann. Math. Statist., 28, 195–241. Box, G.E.P. and Lucas, H.L. (1959). Design of experiments in nonlinear situations. Biometrika, 46, 77–90. Box, G.E.P. and Wilson, K.B. (1951). On the experimental attainment of optimum conditions (with discussion). J. R. Statist. Soc., B, 13, 1–45. Box, G.E.P., Hunter, W.G. and Hunter, J.S. (1978). Statistics for experimenters. New York: Wiley. Brien, C.J. and Payne, R.W. (1999). Tiers, structure formulae and the analysis of complicated experiments. J. R. Statist. Soc. D, 48, 41–52. Carlin, B.P., Kadane, J. and Gelfand, A.E. (1998). Approaches for optimal sequential decision analysis in clinical trials. Biometrics, 54, 964–975. Carmichael, R.D. (1937). Introduction to the theory of groups of finite order. New York: Dover, 1956 reprint. Chaloner, K. (1993). A note on optimal Bayesian design for nonlinear problems. J. Statist. Plann. Inf., 37, 229–235. Chaloner, K. and Verdinelli, I. (1995). Bayesian experimental design: a

© 2000 by Chapman & Hall/CRC

review. Statist. Sci., 10, 273–304. Chambers, J.M. and Hastie, T.J. (editors) (1992). Statistical models in S. Pacific Grove: Wadsworth & Brooks/Cole. Chao, S.-C. and Shao, J. (1997). Statistical methods for two-sequence three-period cross-over trials with incomplete data. Statistics in Medicine, 16, 1071–1039. Cheng, C.-S., Martin, R.J. and Tang, B. (1998). Two-level factorial designs with extreme numbers of level changes. Ann. Statist., 26, 1522– 1539. Cheng, C.-S. and Mukerjee, R. (1998). Regular fractional factorial designs with minimum aberration and maximum estimation capacity. Ann. Statist., 26, 2289–2300. Chernoff, H. (1953). Locally optimal designs for estimating parameters. Ann. Math. Statist., 24, 586–602. Ciminera, J.L., Heyse, J.F., Nguyen, H. and Tukey, J.W. (1993). Tests for qualitative treatment by center interaction using a push-back procedure. Statistics in Medicine, 12, 1033–1045. Claringbold, P.J. (1955). Use of the simplex design in the study of the joint reaction of related hormones. Biometrics, 11, 174–185. Clarke, G.M. and Kempson, R.E. (1997). Introduction to the design and analysis of experiments. London: Arnold. Cochran, W.G. and Cox, G.M. (1958). Experimental designs. Second edition. New York: Wiley. Cook, R.D. and Weisberg, S. (1982). Residuals and inference in regression. London: Chapman & Hall. Copas, J.B. (1973). Randomization models for the matched and unmatched 2 × 2 tables. Biometrika, 60, 467–476. Cornell, J.A. (1981). Experiments with mixtures. New York: Wiley. Cornfield, J. (1978). Randomization by group: a formal analysis. American J. Epidemiology, 108, 100–102. Covey–Crump, P.A.K. and Silvey, S.D. (1970). Optimal regression designs with previous observations. Biometrika, 57, 551–566. Cox, D.R. (1951). Some systematic experimental designs. Biometrika, 38, 310–315. Cox, D.R. (1954). The design of an experiment in which certain treatment arrangements are inadmissible. Biometrika, 41, 287–295. Cox, D.R. (1957). The use of a concomitant variable in selecting an experimental design. Biometrika, 44, 150–158. Cox, D.R. (1958). Planning of experiments. New York: Wiley. Cox, D.R. (1971). A note on polynomial response functions for mixtures. Biometrika, 58, 155–159. Cox, D.R. (1982). Randomization and concomitant variables in the design of experiments. In Statistics and Probability: Essays in honor of C.R. Rao. Editors G. Kallianpur, P.R. Krishnaiah and J.K. Ghosh.

© 2000 by Chapman & Hall/CRC

Amsterdam: North Holland, pp. 197–202. Cox, D.R. (1984a). Interaction (with discussion). Int. Statist. Rev., 52, 1–31. Cox, D.R. (1984b). Effective degrees of freedom and the likelihood ratio test. Biometrika, 71, 487–493. Cox, D.R. (1992). Causality: some statistical aspects. J. R. Statist. Soc., A, 155, 291–301. Cox, D.R. and Hinkley, D.V. (1974). Theoretical statistics. London: Chapman & Hall. Cox, D.R. and McCullagh, P. (1982). Some aspects of analysis of covariance (with discussion). Biometrics, 38, 541–561. Cox, D.R. and Snell, E.J. (1981). Applied statistics. London: Chapman & Hall. Cox, D.R. and Wermuth, N. (1996). Multivariate dependencies. London: Chapman & Hall. Daniel, C. (1959). Use of half normal plot in interpreting factorial twolevel experiments. Technometrics, 1, 311–341. Daniel, C. (1994). Factorial one-factor-at-a-time experiments. American Statistician, 48, 132–135. Davies, O.L. (editor) (1956). Design and analysis of industrial experiments. 2nd ed. Edinburgh: Oliver & Boyd. Dawid, A.P. (2000). Causality without counterfactuals (with discussion). J. Amer. Statist. Assoc., 95, to appear. Dawid, A.P. and Sebastiani, P. (1999). Coherent dispersion criteria for optimal experimental design. Ann. Statist., 27, 65–81. Dean, A. and Voss, D. (1999). Design and analysis of experiments. New York: Springer. Den´es, J. and Keedwell, A.D. (1974). Latin squares and their applications. London: English Universities Press. Desu, M.M. and Raghavarao, D. (1990). Sample size methodology. New York: Academic Press. Dey, A. and Mukerjee, R. (1999). Fractional factorial plans. New York: Wiley. Donnelly, C.A. and Ferguson, N.M. (1999). Statistical aspects of BSE and vCJD. London: Chapman & Hall. Draper, N. and Smith, H. (1998). Applied regression analysis. 3rd edition. New York: Wiley. Easton, D.F., Peto, J. and Babiker, A.G. (1991). Floating absolute risk: alternative to relative risk in survival and case-control analysis avoiding and arbitrary reference group. Statistics in Medicine, 10, 1025– 1035. Elfving, G. (1952). Optimum allocation in linear regression theory. Ann. Math. Statist., 23, 255–262. Elfving, G. (1959). Design of linear experiments. In Cram¨er Festschrift

© 2000 by Chapman & Hall/CRC

volume, ed. U. Grenander, pp.58–74. New York: Wiley. Fang, K.-T. and Wang, Y. (1993). Number-theoretic methods in statistics. London: Chapman & Hall. Fang, K.-T., Wang, Y. and Bentler, P.M. (1994). Some applications of number-theoretic methods in statistics. Statist. Sci., 9, 416–428. Farewell, V.T. and Herzberg, A.M. (2000). Plaid designs for the evaluation of training for medical practitioners. To appear. Fearn, T. (1992). Box-Cox transformations and the Taguchi method: an alternative analysis of a Taguchi case study. Appl. Statist., 41, 553–559. Fedorov, V.V. (1972). Theory of optimal experiments. (English translation from earlier Russian edition). New York: Academic Press. Fedorov, V.V. and Hackl, P. (1997). Model oriented design of experiments. New York: Springer. Finney, D.J. (1945a). Some orthogonal properties of the 4 × 2 and 6 × 6 Latin squares. Ann. Eugenics, 12, 213–217. Finney, D.J. (1945b). The fractional replication of factorial arrangements. Ann. Eugenics, 12, 283–290. Firth, D. and Menezes, R. (2000). Quasi-variances for comparing groups: control relative not absolute error. To appear. Fisher, R.A. (1926). The arrangement of field experiments. J. Ministry of Agric., 33, 503–513. Fisher, R.A. (1935). Design of experiments. Edinburgh: Oliver & Boyd. Fisher, R.A. and Mackenzie, W.A. (1923). Studies in crop variation II. The manurial response of different potato varieties. J. Agric. Sci., 13, 311–320. Flournoy, N., Rosenberger, W.F. and Wong, W.K. (1998) (eds). New developments and applications in experimental design. Hayward: Institute of Mathematical Statistics. Fries, A. and Hunter, W.G. (1980). Minimum aberration in 2k−p designs. Technometrics, 222, 601–608. Gail, M. and Simon, R. (1985). Testing for qualitative interaction between treatment effects and patient subsets. Biometrics, 41, 361–372. Gilmour, A.R., Cullis, B.R. and Verbyla, A.P. (1997). Accounting for natural and extraneous variation in the analysis of field experiments. J. Agric. Bio. Environ. Statist., 2, 269–273. Gilmour, S.G. and Ringrose, T.J. (1999). Controlling processes in food technology by simplifying the canonical form of fitted response surfaces. Appl. Statist., 48, 91–102. Ginsberg, E.S., Mello, N.K., Mendelson, J.H., Barbieri, R.L., Teoh, S.K., Rothman, M., Goa, X. and Sholar, J.W. (1996). Effects of alcohol ingestion on œstrogens in postmenopausal women. J. Amer. Med. Assoc. 276, 1747–1751. Goetghebeur, E. and Houwelingen, H.C. van (1998)(eds). Special issue

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on noncompliance in clinical trials. Statistics in Medicine, 17, 247– 390. Good, I.J. (1958). The interaction algorithm and practical Fourier analysis. J. R. Statist. Soc., B, 20, 361–372. Grundy, P.M. and Healy, M.J.R. (1950). Restricted randomization and quasi-Latin squares. J. R. Statist. Soc., B, 12, 286–291. Guyatt, G., Sackett, D., Taylor, D.W., Chong, J., Roberts, R. and Pugsley, S. (1986). Determining optimal therapy-randomized trials in individual patients. New England J. Medicine, 314, 889–892. Hald, A. (1948). The decomposition of a series of observations. Copenhagen: Gads Forlag. Hald, A. (1998). A history of mathematical statistics. New York: Wiley. Hartley, H.O. and Smith, C.A.B. (1948). The construction of Youden squares. J. R. Statist. Soc., B, 10, 262–263. Hedayat, A.S., Sloane, N.J.A. and Stufken, J. (1999). Orthogonal arrays: theory and applications. New York: Springer. Heise, M.A. and Myers, R.H. (1996). Optimal designs for bivariate logistic regression. Biometrics, 52, 613–624. Herzberg, A.M. (1967). The behaviour of the variance function of the difference between two estimated responses. J. R. Statist. Soc., B, 29, 174–179. Herzberg, A.M. and Cox, D.R. (1969). Recent work on the design of experiments: a bibliography and a review. J. R. Statist. Soc., A, 132, 29–67. Hill, R. (1986). A first course in coding theory. Oxford University Press. Hinkelman, K. and Kempthorne, O. (1994). Design and analysis of experiments. New York: Wiley. Holland, P.W. (1986). Statistics and causal inference (with discussion). J. Amer. Statist. Assoc., 81, 945–970. Huang, Y.-C. and Wong, W.-K. (1998). Sequential considerations of multiple-objective optimal designs. Biometrics, 54, 1388–1397. Hurrion, R.D. and Birgil, S. (1999). A comparison of factorial and random experimental design methods for the development of regression and neural simulation metamodels. J. Operat. Res. Soc., 50, 1018– 1033. Jennison, C. and Turnbull, B.W. (2000). Group sequential methods with applications to clinical trials. London: Chapman & Hall. John, J.A. and Quenouille, M.H. (1977). Experiments: design and analysis. London: Griffin. John, J.A., Russell, K.G., Williams, E.R. and Whitaker, D. (1999). Resolvable designs with unequal block sizes. Austr. and NZ J. Statist., 41, 111–116. John, J.A. and Williams, E.R. (1995). Cyclic and computer generated designs. 2nd edition. London: Chapman & Hall.

© 2000 by Chapman & Hall/CRC

John, P.W.M. (1971). Statistical design and analysis of experiments. New York: Macmillan. Johnson, T. (1998). Clinical trials in psychiatry: background and statistical perspective. Statist. Methods in Medical Res., 7, 209–234. Jones, B. and Kenward, M.G. (1989). Design and analysis of crossover trials. London: Chapman & Hall. Kempthorne, O. (1952). Design of experiments. New York: Wiley. Kiefer, J. (1958). On the nonrandomized optimality and randomized nonoptimality of symmetrical designs. Ann. Math. Statist., 29, 675– 699. Kiefer, J. (1959). Optimum experimental design (with discussion). J. R. Statist. Soc., B, 21, 272–319. Kiefer, J. (1975). Optimal design: variation in structure and performance under change of criterion. Biometrika, 62, 277–288. Kiefer, J. (1985). Collected papers. eds. L. Brown, I. Olkin, J. Sacks and H.P. Wynn. New York: Springer. Kiefer, J. and Wolfowitz, J. (1959). Optimal designs in regression problems. Ann. Math. Statist., 30, 271–294. Kruskal, W.H. (1961). The coordinate-free approach to Gauss-Markov estimation, and its application to missing and extra observations. Proc. 4th Berkeley Symposium, 1, 435–451. Lauritzen, S.L. (2000). Causal inference from graphical models. In Complex stochastic systems. C. Kl¨ uppelberg, O.E. Barndorff-Nielsen and D.R. Cox, editors. London: Chapman & Hall/CRC. Leber, P.D. and Davis, C.S. (1998). Threats to the validity of clinical trials employing enrichment strategies for sample selection. Controlled Clinical Trials, 19, 178–187. Lehmann, E.L. (1975). Nonparametrics: statistical methods based on ranks. San Francisco: Holden-Day. Lindley, D.V. (1956). On the measure of information provided by an experiment. Ann. Math. Statist., 27, 986–1005. Logothetis, N. (1990). Box-Cox transformations and the Taguchi method. Appl. Statist., 39, 31–48. Logothetis, N. and Wynn, H.P. (1989). Quality through design. Oxford University Press. McCullagh, P. (2000). Invariance and factorial models (with discussion). J. R. Statist. Soc., B, 62, 209–256. McCullagh, P. and Nelder, J.A. (1989). Generalized linear models. 2nd edition. London: Chapman & Hall. McKay, M.D., Beckman, R.J. and Conover, W.J. (1979). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21, 239–245. Manly, B.J.F. (1997). Randomization, bootstrap and Monte Carlo methods in biology. London: Chapman & Hall.

© 2000 by Chapman & Hall/CRC

Mehrabi, Y. and Matthews, J.N.S. (1998). Implementable Bayesian designs for limiting dilution assays. Biometrics, 54, 1398–1406. Mesenbrink, P., Lu, J-C., McKenzie, R. and Taheri, J. (1994). Characterization and optimization of a wave-soldering process. J. Amer. Statist. Assoc., 89, 1209–1217. Meyer, R.D., Steinberg, D.M. and Box, G.E.P. (1996). Follow up designs to resolve confounding in multifactorial experiments. Technometrics, 38, 303–318. Monod, H., Aza¨is, J.-M. and Bailey, R.A. (1996). Valid randomization for the first difference analysis. Austr. J. Statist., 38, 91–106. Montgomery, D.C. (1997). Design and analysis of experiments. 4th edition. New York: Wiley. Montgomery, D.C. (1999). Experimental design for product and process design and development (with comments). J. R. Statist. Soc., D, 48, 159–177. Nair, V.J. (editor) (1992). Taguchi’s parameter design: a panel discussion. Technometrics, 34, 127–161. Neiderreiter, H. (1992). Random number generation and quasi-Monte Carlo methods. Philiadelphia: SIAM. Nelder, J.A. (1965a). The analysis of experiments with orthogonal block structure. I Block structure and the null analysis of variance. Proc. Roy. Soc. London, A, 283, 147–162. Nelder, J.A. (1965b). The analysis of experiments with orthogonal block structure. II Treatment structure and the general analysis of variance. Proc. Roy. Soc. London, A, 283, 163–178. Newcombe, R.G. (1996). Sequentially balanced three-squares cross-over designs. Statistics in Medicine, 15, 2143–2147. Neyman, J. (1923). On the application of probability theory to agricultural experiments. Essay on principles. Roczniki Nauk Rolniczych, 10, 1–51 (in Polish). English translation of Section 9 by D.M. Dabrowska and T.P. Speed (1990), Statist. Sci., 9, 465–480. Olguin, J. and Fearn, T. (1997). A new look at half-normal plots for assessing the significance of contrasts for unreplicated factorials. Appl. Statist., 46, 449–462. Owen, A. (1992). Orthogonal arrays for computer experiments, integration, and visualization. Statist. Sinica, 2, 459–452. Owen, A. (1993). A central limit theorem for Latin hypercube sampling. J. R. Statist. Soc., B, 54, 541–551. Papadakis, J.S. (1937). M´ethods statistique poure des exp´eriences sur champ. Bull. Inst. Am´er. Plantes ` a Salonique, No. 23. Patterson, H.D. and Williams, E.R. (1976). A new class of resolvable incomplete block designs. Biometrika, 63, 83–92. Pearce, S.C. (1970). The efficiency of block designs in general. Biometrika 57, 339–346.

© 2000 by Chapman & Hall/CRC

Pearl, J. (2000). Causality: models, reasoning and inference. Cambridge: Cambridge University Press. Pearson, E.S. (1947). The choice of statistical tests illustrated on the interpretation of data classed in a 2 × 2 table. Biometrika, 34, 139– 167. Piantadosi, S. (1997). Clinical trials. New York: Wiley. Pistone, G. and Wynn, H.P. (1996). Generalised confounding with Gr¨ obner bases. Biometrika, 83, 653–666. Pistone, G., Riccomagno, E. and Wynn, H.P. (2000). Algebraic statistics. London: Chapman & Hall/CRC. Pitman, E.J.G. (1937). Significance tests which may be applied to samples from any populations: III The analysis of variance test. Biometrika, 29, 322–335. Plackett, R.L. and Burman, J.P. (1945). The design of optimum multifactorial experiments. Biometrika, 33, 305–325. Preece, A.W., Iwi, G., Davies-Smith, A., Wesnes, K., Butler, S., Lim, E. and Varney, A. (1999). Effect of 915-MHz simulated mobile phone signal on cognitive function in man. Int. J. Radiation Biology, 75, 447–456. Preece, D.A. (1983). Latin squares, Latin cubes, Latin rectangles, etc. In Encyclopedia of statistical sciences, Vol.4. S. Kotz and N.L. Johnson, eds, 504–510. Preece, D.A. (1988). Semi-Latin squares. In Encyclopedia of statistical sciences, Vol.8. S. Kotz and N.L. Johnson, eds, 359–361. Pukelsheim, F. (1993). Optimal design of experiments. New York: Wiley. Quenouille, M.H. (1953). The design and analysis of experiments. London: Griffin. Raghavarao, D. (1971). Construction and combinatorial problems in design of experiments. New York: Wiley. Raghavarao, D. and Zhou, B. (1997). A method of constructing 3designs. Utilitas Mathematica, 52, 91–96. Raghavarao, D. and Zhou, B. (1998). Universal optimality of UE 3designs for a competing effects model. Comm. Statist.–Theory Meth., 27, 153–164. Rao, C. R. (1947). Factorial experiments derivable from combinatorial arrangements of arrays. Suppl. J. R. Statist. Soc., 9, 128–139. Redelmeier, D. and Tibshirani, R. (1997a). Association between cellular phones and car collisions. N. England J. Med., 336, 453–458. Redelmeier, D. and Tibshirani, R.J. (1997b). Is using a cell phone like driving drunk? Chance, 10, 5–9. Reeves, G.K. (1991). Estimation of contrast variances in linear models. Biometrika, 78, 7–14. Ridout, M.S. (1989). Summarizing the results of fitting generalized linear models from designed experiments. In Statistical modelling: Proceed-

© 2000 by Chapman & Hall/CRC

ings of GLIM89. A. Decarli et al. editors, pp. 262–269. New York: Springer. Robbins, H. and Monro, S. (1951). A stochastic approximation method. Ann. Math. Statist., 22, 400–407. Rosenbaum, P.R. (1987). The role of a second control group in an observational study (with discussion). Statist. Sci., 2, 292–316. Rosenbaum, P.R. (1999). Blocking in compound dispersion experiments. Technometrics, 41, 125–134. Rosenberger, W.F. and Grill, S.E. (1997). A sequential design for psychophysical experiments: an application to estimating timing of sensory events. Statistics in Medicine, 16, 2245–2260. Rubin, D.B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. J. Educ. Psychol., 66, 688–701. Sacks, J., Welch, W.J., Mitchell, T.J. and Wynn, H.P. (1989). Design and analysis of computer experiments (with discussion). Statist. Sci., 4, 409–436. Sattherthwaite, F. (1958). Random balanced experimentation. Technometrics, 1, 111–137. Scheff´e, H. (1958). Experiments with mixtures (with discussion). J.R. Statist. Soc., B, 20, 344–360. Scheff´e, H. (1959). Analysis of variance. New York: Wiley. Senn, S.J. (1993). Cross-over trials in clinical research. Chichester: Wiley. Shah, K.R. and Sinha, B.K. (1989). Theory of optimal designs. Berlin: Springer. Silvey, S.D. (1980). Optimal design. London: Chapman & Hall. Singer, B.H. and Pincus, S. (1998). Irregular arrays and randomization. Proc. Nat. Acad. Sci. USA, 95, 1363–1368. Smith, K. (1918). On the standard deviation of adjusted and interpolated values of an observed polynomial function and its constants, and the guidance they give towards a proper choice of the distribution of observations. Biometrika, 12, 1–85. Spector, P. (1994). An introduction to S and S-Plus. Belmont: Duxbury. Speed, T.P. (1987). What is an analysis of variance? (with discussion). Ann. Statist., 15, 885–941. Stein, M. (1987). Large sample properties of simulations using Latin hypercube sampling. Technometrics, 29, 143–151. Stigler, S.M. (1986). The history of statistics. Cambridge, Mass: Harvard University Press. Street, A.P. and Street, D.J. (1987). Combinatorics of experimental design. Oxford University Press. Tang, B. (1993). Orthogonal array-based Latin hypercubes. J. Amer. Statist. Assoc., 88, 1392–1397. Thompson, M. E. (1997). Theory of sample surveys. London: Chapman

© 2000 by Chapman & Hall/CRC

& Hall. Tsai, P.W., Gilmour, S.G., and Mead, R. (1996). An alternative analysis of Logothetis’s plasma etching data. Letter to the editor. Appl. Statist., 45, 498–503. Tuck, M.G., Lewis, S.M. and Cottrell, J.I.L. (1993). Response surface methodology and Taguchi: a quality improvement study from the milling industry. Appl. Statist., 42, 671–681. Tukey, J.W. (1949). One degree of freedom for non-additivity. Biometrics, 5, 232–242. UK Collaborative ECMO Trial Group. (1986). UK collaborative randomised trial of neonatal extracorporeal membrane oxygenation. Lancet, 249, 1213–1217. Vaart, A.W. van der (1998). Asymptotic statistics. Cambridge: Cambridge University Press. Vajda, S. (1967a). Patterns and configurations in finite spaces. London: Griffin. Vajda, S. (1967b). The mathematics of experimental design; incomplete block designs and Latin squares. London: Griffin. Venables, W.M. and Ripley, B.D. (1999). Modern applied statistics with S-PLUS. 3rd ed. Berlin: Springer. Wald, A. (1947). Sequential analysis. New York: Wiley. Wang, J.C. and Wu, C.F.J. (1991). An approach to the construction of asymmetric orthogonal arrays. J. Amer. Statist. Assoc., 86, 450–456. Wang, Y.-G. and Leung, D. H.-Y. (1998). An optimal design for screening trials. Biometrics, 54, 243–250. Ware, J.H. (1989). Investigating therapies of potentially great benefit: ECMO (with discussion). Statist. Sci., 4, 298–340. Wei, L.J. (1988). Exact two-sample permutation tests based on the randomized play-the-winner rule. Biometrika, 75, 603–606. Welch, B.L. (1937). On the z test in randomized blocks and Latin squares. Biometrika, 29, 21–52. Wetherill, G.B. and Glazebrook, K.D. (1986). Sequential methods in statistics. 3rd edition. London: Chapman & Hall. Whitehead, J. (1997). The design and analysis of sequential medical trials. 2nd edition. Chichester: Wiley. Williams, E.J. (1949). Experimental designs balanced for the estimation of residual effects of treatments. Australian J. Sci. Res., A, 2, 149– 168. Williams, E.J. (1950). Experimental designs balanced for pairs of residual effects. Australian J. Sci. Res., A, 3, 351–363. Williams, R.M. (1952). Experimental designs for serially correlated observations. Biometrika, 39, 151–167. Wilson, E.B. (1952). Introduction to scientific research. New York: McGraw Hill.

© 2000 by Chapman & Hall/CRC

Wynn, H.P. (1970). The sequential generation of D-optimum experimental designs. Ann. Statist., 5, 1655–1664. Yates, F. (1935). Complex experiments (with discussion). Suppl. J. R. Statist. Soc., 2, 181–247. Yates, F. (1936). A new method of arranging variety trials involving a large number of varieties. J. Agric. Sci., 26, 424–455. Yates, F. (1937). The design and analysis of factorial experiments. Technical communication 35. Harpenden: Imperial Bureau of Soil Science. Yates, F. (1951a). Bases logiques de la planification des exp´eriences. Ann. Inst. H. Poincar´ e, 12, 97–112. Yates, F. (1951b). Quelques d´eveloppements modernes dans la planification des exp´eriences. Ann. Inst. H. Poincar´ e, 12, 113–130. Yates, F. (1952). Principles governing the amount of experimentation in developmental work. Nature, 170, 138–140. Youden, W.J. (1956). Randomization and experimentation (abstract). Ann. Math. Statist., 27, 1185–1186.

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