Topics  Some of my
publications 
General background 
R. A. Bailey:
Confounding.
In Encyclopedia of Statistical Sciences
(eds. S. Kotz and N. L. Johnson),
J. Wiley, New York,
Volume 2,
1982,
pp. 128–134.
R. A. Bailey:
Interaction.
In Encyclopedia of Statistical Sciences
(eds. S. Kotz and N. L. Johnson),
J. Wiley, New York,
Volume 4,
1983,
pp. 176–181.
R. A. Bailey:
Inference from randomized (factorial) experiments.
Statistical Science,
32
(2017),
352–355.
doi: 10.1214/16STS600

How can we identify confounding and aliasing in a given factorial
design? 
R. A. Bailey, F. H. L. Gilchrist and H. D. Patterson:
Identification of effects and confounding patterns in factorial designs.
Biometrika
64
(1977),
347–354.
R. A. Bailey:
Patterns of confounding in factorial designs.
Biometrika
64
(1977),
597–603.
[Maths Reviews 0501643 (58 #18945)]
H. D. Patterson and R. A. Bailey:
Design keys for factorial experiments.
Applied Statistics
27
(1978),
335–343.
R. A. Bailey:
Design keys for multiphase experiments.
In mODa 11—Advances in ModelOriented Design and Analysis
(eds. Joachim Kunert, Christine H. Müller and Anthony C. Atkinson),
Springer International Publishing, 2016,
pp. 27–35.
doi: 10.1007/9783319312668_4

How can we construct factorial designs so as to achieve specified
confounding of factorial treatment effects in specified strata, or
so that designated treatment effects are estimable? 
M. F. Franklin and R. A. Bailey:
Selection of defining contrasts and confounded effects in twolevel
experiments.
Applied Statistics
26
(1977),
321–326.
H. D. Patterson and R. A. Bailey:
Design keys for factorial experiments.
Applied Statistics
27
(1978),
335–343.
R. A. Bailey:
Design keys for multiphase experiments.
In mODa 11—Advances in ModelOriented Design and Analysis
(eds. Joachim Kunert, Christine H. Müller and Anthony C. Atkinson),
Springer International Publishing, 2016,
pp. 27–35.
doi: 10.1007/9783319312668_4
André Kobilinsky, Hervé Monod and R. A. Bailey:
Automatic generation of generalised regular factorial designs.
Computational Statistics and Data Analysis,
113
(2017),
311–329.
doi: 10.1016/j.csda.2016.09.003

How can we use character theory of Abelian groups to identify confounding
and aliasing in factorial designs, or to construct designs with specified
confounding and aliasing? 
R. A. Bailey:
Patterns of confounding in factorial designs.
Biometrika
64
(1977),
597–603.
[Maths Reviews 0501643 (58 #18945)]
R. A. Bailey:
Dual Abelian groups in the design of experiments.
In Algebraic Structures and Applications
(eds. P. Schultz, C. E. Praeger and R. P. Sullivan),
Marcel Dekker, New York,
1982,
pp. 45–54.
[Maths Reviews 0647165 (83m: 62135)]
R. A. Bailey:
Factorial design and Abelian groups.
Linear Algebra and its Applications
70
(1985),
349–368.
[Maths Reviews 0808552 (87c: 62151)]

In nonorthogonal designs, when does the partial confounding
correspond to the factorial decomposition? 
R. A. Bailey:
Balance, orthogonality and efficiency factors in factorial design.
Journal of the Royal Statistical Society, Series B
47
(1985),
453–458.
[Maths Reviews 0844475 (87k: 62124)]
R. A. Bailey and C. A. Rowley:
General balance and treatment permutations.
Linear Algebra and its Applications
127
(1990),
183–225.
[Maths Reviews 1048802 (91d: 05014)]
R. A. Bailey:
Cyclic designs and factorial designs.
In Probability, Statistics and Design of Experiments
(proceedings of the R. C. Bose Symposium on
Probability, Statistics and Design of Experiments, Delhi, 27–30
December, 1988)
(ed. R. R. Bahadur),
Wiley Eastern, New Delhi,
1990,
pp. 51–74.
H. Monod and R. A. Bailey:
Pseudofactors: normal use to improve design and facilitate analysis.
Applied Statistics
41
(1992),
317–336.
C. J. Brien and R. A. Bailey:
Decomposition tables for experiments. I. A chain of randomizations.
Annals of Statistics
37
(2009),
4184–4213.
doi: 10.1214/09AOS717
[Maths Reviews 2572457 (2010k: 62294)]
R. A. Bailey:
Symmetric factorial designs in blocks.
Journal of Statistical Theory and Practice
15
(2011),
13–24.
[Maths Reviews 2829819]

How do the above methods apply if some treatment factors are
quantitative? 
R. A. Bailey:
The decomposition of treatment degrees of freedom in quantitative
factorial experiments.
Journal of the Royal Statistical Society, Series B
44
(1982),
63–70.
[Maths Reviews 0655375 (83e: 62103) and 0721758 (85d: 62076)]
R. A. Bailey:
Cyclic designs and factorial designs.
In Probability, Statistics and Design of Experiments
(proceedings of the R. C. Bose Symposium on
Probability, Statistics and Design of Experiments, Delhi, 27–30
December, 1988)
(ed. R. R. Bahadur),
Wiley Eastern, New Delhi,
1990,
pp. 51–74.
H. Monod and R. A. Bailey:
Pseudofactors: normal use to improve design and facilitate analysis.
Applied Statistics
41
(1992),
317–336.

If the treatments are applied and investigated consecutively, how can we
ensure that important factorial effects are orthogonal to a loworder
polynomial in the timetrend? 
R. A. Bailey, ChingShui Cheng and Patricia Kipnis:
Construction of trendresistant factorial designs.
Statistica Sinica
2
(1992),
393–411.
[Maths Reviews 1187950 (93i: 62197)]

Regular fractions 
Ulrike Grömping and Rosemary A. Bailey:
Regular fractions of factorial arrays.
In mODa 11—Advances in ModelOriented Design and Analysis
(eds. Joachim Kunert, Christine H. Müller and Anthony C. Atkinson),
Springer International Publishing, 2016,
pp. 143–151.
doi: 10.1007/9783319312668_17

Irregular fractions 
R. A. Bailey:
A note on loosely balanced incomplete block designs.
Computational Statistics and Data Analysis
3
(1985),
115–117 and 121–122.
R. A. Bailey:
Contribution to the discussion of
`Detection of interactions in experiments on large numbers of factors'
by S. M. Lewis and A. M. Dean.
Journal of the Royal Statistical Society, Series B
63
(2001),
662.
R. A. Bailey:
Contribution to the discussion of
`Optimum design of experiments for statistical inference'
by Steven G. Gilmour and Luzia A. Trinca.
Applied Statistics
61
(2012),
374–375.

Multipart designs 
R. A. Bailey and Peter J. Cameron:
Multipart balanced incompleteblock designs.
Statistical Papers,
60 (2019), 55–76.
