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I. Olkin 27Milton Sobel was the teaching assistant for the 111a course; Robert Bechoferand Allan Birnbaum were also TAs. I remember that Milton Sobel satin on Wald’s class on analysis of variance. Because he was at least one yearahead of me I thought that he would have taken this course earlier. He said hedid take it earlier but the course was totally different depending on who wasteaching it. It was depressing to think that I would have to take every coursetwice! As the course progressed Wald ran out of subscripts and superscripts, and subsequently added some subscripts onthe left-hand side.Wolfowitz recommended three books, and assigned homework from them:on the right-hand side, e.g., x klij(a) H. Cramér (1945): Mathematical Methods of Statistics(b) J.V. Uspensky (1937): Introduction to Mathematical Probability(c) S.S. Wilks (1943): Mathematical StatisticsHe mentioned references to Kolmogorov’s Foundation of Probability and theLévy and Roth book Elements of Probability.Wolfowitz used the term “chance variables” and commented that the Lawof Small Numbers should have been called the Law of Small Probabilities. AsI look through the notes it is funny to see the old-fashioned factorial symbols⌊n instead of n!. As I reread my notes it seems to me that this coursewas a rather simplified first course in probability. Some of the topics touchedupon the use of independence, Markov chains, joint distributions, conditionaldistributions, Chebychev’s inequality, stochastic convergence (Slutsky’s theorem),Law of Large Numbers, convolutions, characteristic functions, CentralLimit Theorem (with discussion of Lyapunov and Lindeberg conditions).I have a comment in which Wolfowitz notes an error in Cramér (p. 343): (a)if y 1 ,y 2 ,... is a sequence with ∑ y i = c i for all c and σ 2 (y i ) → 0 as i →∞,then p lim i→∞ (y i − c i )=0;(b)theconverseisnottrueinthatitmaybethatσ 2 (y i ) →∞and yet p lim(y i − c i ) = 0.The second basic course was 111b, taught by Wald. The topics includedpoint estimation, consistency, unbiasedness, asymptotic variance, maximumlikelihood, likelihood ratio tests, efficiency. This course was more mathematicalthan 111a in that there was more asymptotics. In terms of mathematicalbackground I note that he used Lagrange multipliers to show that, forw 1 ,...,w n ∈ [0, 1], ∑ ni=1 w2 i /(∑ ni=1 w i) 2 is minimized when w i =1/n for alli ∈{1,...,n}. Apparently, convexity was not discussed.There is a derivation of the chi-square distribution that includes a discussionof orthogonal matrices. This is one of the standard proofs. Other topicsinclude Schwarz inequality (but not used for the above minimization), andsufficiency. The second part of the course dealt with tests of hypotheses, withemphasis on the power function (Wald used the term “power curve”), acceptancesampling, and the OC curve.My Columbia days are now over 65 years ago, but I still remember themas exciting and an incubator for many friendships and collaborations.
28 Reminiscences from ColumbiaReferencesAnderson, T.W. Jr. (1955). The Department of Mathematical Statistics. InA History of the Faculty of Political Science, Columbia, R.G. Hoxie, Ed.Columbia University Press, pp. 250–255.Olkin, I. and Sampson, A.R. (2001a). Hotelling, Harold (1895–1973).In International Encyclopedia of the Social & Behavioral Sciences(N.J. Smelser and P.B. Baltes, Eds.). Pergamon, Oxford, 6921–6925. URL http://www.sciencedirect.com/science/article/pii/B0080430767002631.Olkin, I. and Sampson, A.R. (2001b). Harold Hotelling. In Statisticians ofthe Centuries. Springer,NewYork,pp.454–458.
Page 2 and 3: ContentsPrefacexviiContributorsI Th
Page 5 and 6: viii15 We live in exciting times 15
Page 7 and 8: x24.6 Open areas for research . . .
Page 9 and 10: xii36 Buried treasures 405Michael A
Page 11 and 12: xiv45.3 Multi-phase inference . . .
Page 14 and 15: PrefaceStatistics is the science of
Page 16: PrefacexixPart V comprises seven ar
Page 19 and 20: xxiiNancy FlournoyUniversity of Mis
Page 22: Part IThe history of COPSS
Page 25 and 26: 4 A brief history of COPSSOnchiota
Page 27 and 28: 6 A brief history of COPSSThe items
Page 29 and 30: 8 A brief history of COPSSTABLE 1.1
Page 31 and 32: 10 A brief history of COPSSwomen of
Page 33 and 34: 12 A brief history of COPSSTABLE 1.
Page 37 and 38: 16 A brief history of COPSS1.4.3 Ge
Page 41 and 42: 20 A brief history of COPSSFor more
Page 44 and 45: 2Reminiscences of the Columbia Univ
Page 46 and 47: I. Olkin 25Bechhofer, Allan Birnbau
Page 50 and 51: 3AcareerinstatisticsHerman Chernoff
Page 52 and 53: H. Chernoff 31given three-day basic
Page 54 and 55: H. Chernoff 33Savage tried to defen
Page 56 and 57: H. Chernoff 35In working on an arti
Page 58 and 59: H. Chernoff 37measure the loss as t
Page 60 and 61: H. Chernoff 39ReferencesAlbert, A.E
Page 62 and 63: 4“. . . how wonderful the field o
Page 64 and 65: D.R. Brillinger 43He went on to be
Page 66 and 67: D.R. Brillinger 45Tukey wrote many
Page 68: D.R. Brillinger 4718. Personal comm
Page 71 and 72: 50 Unorthodox journey to statistics
Page 80 and 81: 6Statistics before and after my COP
Page 82 and 83: P.J. Bickel 616.3.1 Imperial Colleg
Page 84 and 85: P.J. Bickel 63make. This was first
Page 86 and 87: P.J. Bickel 65We eventually develop
Page 88 and 89: P.J. Bickel 67school interest, biol
Page 90 and 91: P.J. Bickel 69ReferencesBean, D., B
Page 92: P.J. Bickel 71Meinshausen, N., Bick
Page 95 and 96: 74 Accidental biostatistics profess
Page 97 and 98: 76 Accidental biostatistics profess
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78 Accidental biostatistics profess
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84 Passion for statisticsme is its
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86 Passion for statisticsTheir Biom
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88 Passion for statisticsthe first
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90 Passion for statisticsIn my expe
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92 Passion for statistics8.5 Job an
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94 Passion for statisticsperiod. An
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96 Passion for statisticsNeyman, J.
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98 Reflections on a statistical car
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100 Reflections on a statistical ca
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110 Science mixes it up with statis
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11Lessons from a twisted career pat
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J.S. Rosenthal 119My math professor
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J.S. Rosenthal 121contrary, it null
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J.S. Rosenthal 123of statistical re
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J.S. Rosenthal 125be open to whatev
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J.S. Rosenthal 12711.4 Final though
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130 Promoting equityand fellowship
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132 Promoting equityevaluate such e
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134 Promoting equityfor equity. But
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136 Promoting equityAssistant Secre
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Part IIIPerspectives on the fieldan
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142 Statistics in service to the na
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154 Where are the majors? ArtBachel
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156 Where are the majors?References
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158 Exciting timesCritically, Efron
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160 Exciting timesIn general I find
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162 Exciting timesI entered the Uni
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164 Exciting times15.3.3 Joining th
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166 Exciting timesvariability of a
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168 Exciting timesHabemma, J.D.F.,
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16The bright future of applied stat
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R.A. Irizarry 173FIGURE 16.1Illustr
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R.A. Irizarry 17516.4 The bright fu
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17The road travelled: From statisti
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N. Chatterjee 179the Kaplan-Meyer e
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N. Chatterjee 18117.4 Genome-wide a
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N. Chatterjee 183by any means. I ju
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N. Chatterjee 185right information
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N. Chatterjee 187The risk of cancer
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190 Journey into genetics and genom
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19Reflections on women in statistic
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M.E. Thompson 205Pearson in England
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M.E. Thompson 207TABLE 19.1The 14 C
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M.E. Thompson 209time series, the s
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M.E. Thompson 211the founding Chair
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M.E. Thompson 21319.7 The current s
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M.E. Thompson 215Yarmie, A. (2003).
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218 “The whole women thing”seem
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220 “The whole women thing”of t
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222 “The whole women thing”on a
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224 “The whole women thing”20.5
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226 “The whole women thing”Ifin
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228 “The whole women thing”Lond
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230 Reflections on diversityas a st
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232 Reflections on diversityall of
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234 Reflections on diversityforce m
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22Why does statistics have two theo
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D.A.S. Fraser 23922.2 65 years and
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D.A.S. Fraser 241function is taken
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D.A.S. Fraser 24322.4 Inference for
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D.A.S. Fraser 245that record the ef
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D.A.S. Fraser 247variable that give
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D.A.S. Fraser 249simple scalar case
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D.A.S. Fraser 251DiCiccio, T.J. and
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23Conditioning is the issueJames O.
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J.O. Berger 255Pedagogical example:
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J.O. Berger 257The SRP does not say
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J.O. Berger 25923.5 Conditional fre
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J.O. Berger 261conditional error pr
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J.O. Berger 263others). If x is the
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J.O. Berger 265How does a frequenti
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24Statistical inference from aDemps
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A.P. Dempster 269tion is simply une
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A.P. Dempster 271As the world of st
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A.P. Dempster 273when the X i are a
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A.P. Dempster 27524.5 Nonparametric
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A.P. Dempster 277can easily happen
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A.P. Dempster 279ReferencesBorel,
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25Nonparametric BayesDavid B. Dunso
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D.B. Dunson 283troduced, providing
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D.B. Dunson 285els in spatial stati
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D.B. Dunson 287methods as also prov
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D.B. Dunson 289ing background, and
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D.B. Dunson 291Murray, J.S., Dunson
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294 Choosing default methodsimagine
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296 Choosing default methods(a) mat
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298 Choosing default methodspartial
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300 Choosing default methodsdirecti
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27Serial correlation and Durbin-Wat
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T.W. Anderson 305The characteristic
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T.W. Anderson 307Consider the seria
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28Anon-asymptoticwalkinprobabilitya
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P. Massart 311(at least for discret
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P. Massart 31328.2.2 Non-asymptopia
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P. Massart 315where P = sµ. In oth
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P. Massart 317Hence by Talagrand’
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P. Massart 319ReferencesAkaike, H.
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P. Massart 321Talagrand, M. (1995).
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324 The present’s futuredata. In
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326 The present’s futureTABLE 29.
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328 The present’s future124356981
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330 The present’s futuremethod, t
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332 The present’s futureFIGURE 29
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334 The present’s futureIchino, M
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336 Lessons in biostatistics30.2 It
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338 Lessons in biostatisticsprognos
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340 Lessons in biostatisticstime fo
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342 Lessons in biostatisticsmeaning
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344 Lessons in biostatistics75% to
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346 Lessons in biostatisticshistolo
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31AvignetteofdiscoveryNancy Flourno
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N. Flournoy 351FIGURE 31.1Incidence
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N. Flournoy 353FIGURE 31.3Mean resp
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N. Flournoy 355FIGURE 31.4Kaplan-Me
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N. Flournoy 357As the study proceed
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32Statistics and public health rese
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R.L. Prentice 36132.2 Public health
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R.L. Prentice 363from urinary excre
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R.L. Prentice 36532.5 Clinical tria
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R.L. Prentice 367Kaplan, E.L. and M
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370 Statistics in a new erative eff
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372 Statistics in a new eraular com
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374 Statistics in a new erajoint de
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376 Statistics in a new eraof the n
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378 Statistics in a new eraLai, T.L
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34Meta-analyses: Heterogeneity can
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N.M. Laird 383into the sum of a “
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N.M. Laird 385are “approximately
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N.M. Laird 387included in the major
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N.M. Laird 389Yusuf, S., Peto, R.,
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392 Good health: Statistical challe
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406 Buried treasuresmeasurement tec
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408 Buried treasuresIn a most rewar
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410 Buried treasureslems different
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37Survey sampling: Past controversi
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R.J. Little 41537.2 Probability or
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R.J. Little 417for the “design-ba
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R.J. Little 419estimate of variance
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R.J. Little 421Example 2 continued
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R.J. Little 42337.3.4 The design-mo
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R.J. Little 42537.5 ConclusionsI am
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R.J. Little 427Isaki, C.T. and Full
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38Environmental informatics: Uncert
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N. Cressie 431and conquer” strate
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N. Cressie 433In the last 20 years,
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N. Cressie 435Once the satellite ha
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N. Cressie 437FIGURE 38.1Locations
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N. Cressie 439regional and seasonal
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N. Cressie 441ciated with the spati
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N. Cressie 443is the set of all pix
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N. Cressie 445challenge is to devel
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N. Cressie 447Gourdji, S.M., Muelle
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N. Cressie 449Wikle, C.K., Milliff,
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452 Statistical geneticsTABLE 39.1T
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454 Statistical geneticsStewart, 19
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456 Statistical geneticslocidata
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458 Statistical geneticssufficientl
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460 Statistical geneticsBrown, M.D.
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462 Statistical geneticsLange, K. a
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464 Statistical geneticsThompson, E
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466 Targeted learningods for nonpar
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468 Targeted learningdenote the obs
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470 Targeted learningSuch an estima
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472 Targeted learningliterature pro
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474 Targeted learninglihood estimat
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476 Targeted learning40.6 Some spec
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478 Targeted learningmemory challen
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480 Targeted learningvan der Laan,
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482 Ah-ha momentwe have just seen.
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484 Ah-ha momentthe data is unchang
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486 Ah-ha momentis well defined and
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488 Ah-ha momentit was something of
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490 Ah-ha momentdefines a Euclidean
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492 Ah-ha momentparticipate in the
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494 Ah-ha momentLee, Y., Wahba, G.,
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42In praise of sparsity and convexi
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R.J. Tibshirani 499TABLE 42.1Asampl
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R.J. Tibshirani 501CancerEpithelial
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R.J. Tibshirani 503that the error v
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R.J. Tibshirani 505Tibshirani, R.J.
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508 Features of Big Dataproblems en
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510 Features of Big Datalearning st
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512 Features of Big Data1210(a)p =
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514 Features of Big Data25002000150
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516 Features of Big DataAs an illus
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518 Features of Big Datafor some gi
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520 Features of Big Data43.7.3 Prop
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522 Features of Big DataBühlmann,
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44Rise of the machinesLarry A. Wass
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L.A. Wasserman 527if an idea requir
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L.A. Wasserman 529●●●● ●
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L.A. Wasserman 531●●●●●
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L.A. Wasserman 533FIGURE 44.4Simula
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L.A. Wasserman 53544.7 Education an
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45Atrioofinferenceproblemsthatcould
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X.-L. Meng 539it is directly rooted
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X.-L. Meng 541exactly, but this doe
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X.-L. Meng 543That is, when we do n
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X.-L. Meng 545(a) For what classes
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X.-L. Meng 547subsequent analysts c
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X.-L. Meng 549can prove only that (
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X.-L. Meng 551In general, “What t
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X.-L. Meng 553vironmental progress
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X.-L. Meng 555which will be compare
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X.-L. Meng 557(a) Given partial kno
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X.-L. Meng 559AcknowledgementsThe m
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X.-L. Meng 561Knuth, D. (1997). The
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Part VAdvice for the nextgeneration
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566 Inspiration, aspiration, ambiti
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568 Inspiration, aspiration, ambiti
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47Personal reflections on the COPSS
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R.J. Carroll 573them is, by definit
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R.J. Carroll 575(Carroll, 2003). In
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R.J. Carroll 57747.8 After the Pres
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R.J. Carroll 579Carroll, R.J., Spie
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582 Publishing without perishingthe
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584 Publishing without perishingbeg
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586 Publishing without perishingme,
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588 Publishing without perishingalr
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590 Publishing without perishingpor
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49Converting rejections into positi
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D.B. Rubin 595This pair of submissi
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D.B. Rubin 597There are several les
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D.B. Rubin 599very sound article, w
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D.B. Rubin 60149.8 ConclusionIhaveb
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D.B. Rubin 603Rubin, D.B. (1972). A
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606 Importance of mentorstwice as t
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608 Importance of mentorsexperiment
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610 Importance of mentorsposition a
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612 Importance of mentors50.7 The t
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51Never ask for or give advice, mak
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T. Speed 617overly generous. Perhap
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T. Speed 619and for finding enjoyme
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622 Thirteen rules3. Waste a lot of
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