Mathias, Peter, Greg, Antonietta, Axel and Byeong will be joining Andreas Buja, Gerda Claeskens, Nancy Heckman, Kavita Ramanan and Ming Yuan (whose terms end August 2018); and Jean Bertoin, Song Xi Chen, Elizaveta Levina and Simon Tavaré (whose terms run through July 2019). Cun-Hui Zhang, who became *Statistical Science* Editor in January, still serves on Council (see below); his vacant position as an elected member is filled by Mathias Drton.

In addition to these elected members, IMS Council is made up of the Executive Committee and the Editors, who serve ex officio. The Executive Committee will, from August, comprise:

President: Alison Etheridge

Past President: Jon Wellner

President-elect: Xiao-Li Meng

Treasurer: Zhengjun Zhang

Program Secretary: Judith Rousseau

Executive Secretary: Edsel A. Peña

**Richard Davis**, outgoing past-president, will be leaving the Executive Committee after three years’ service. **Aurore Delaigle** will be stepping down after six years as Executive Secretary, replaced by **Edsel Peña**.

The Editors are Bálint Tóth (*Annals of Applied Probability*); Maria Eulalia Vares (*Annals of Probability*); Tilmann Gneiting (*Annals of Applied Statistics*); Ed George and Tailen Hsing (*Annals of Statistics*); Cun-Hui Zhang (*Statistical Science*); and Vlada Limic (*IMS Bulletin*). T.N. Sriram is Managing Editor for Statistics & Probability. **Maria Eulalia Vares** will be stepping down as Editor of the *Annals of Probability* at the end of this year, and **Amir Dembo **will be taking on this role from next January.

The elected members who are leaving Council this year are **Peter Bühlmann, Florentina Bunea, Geoffrey Grimmett, Aad van der Vaart** and **Naisyin Wang**. Thanks to all the outgoing officers, editors and council members for their dedicated service to our institute. Thanks, too, to all the candidates, and all who voted!

The Université catholique de Louvain (UCL), in Belgium, has awarded Professor Peter Bühlmann with a Doctor honoris causa degree. Peter Bühlmann received the honorary doctorate for his achievements in the fields of mathematical statistics, machine learning and high-dimensional data analysis. In addition, he has also “contributed to solving pertinent problems in the application of his fundamental research to the fields of biology and bio-medicine (in particular, in genetics and bioinformatics)”.

The ceremony was part of the UCL workshop on Data Sciences held in May 2017 in Louvain-la-Neuve (https://uclouvain.be/en/research-institutes/immaq/isba/dhc-data-sciences.html).

Peter Bühlmann is Professor of Mathematics and Statistics, and currently Chair of the Department of Mathematics, at ETH Zürich. He received his doctoral degree in mathematics in 1993 from ETHZ; after spending three years at UC Berkeley, he returned to ETHZ in 1997. His main research interests are in high-dimensional and computational statistics, machine learning, causal inference and applications in the bio-medical field. He has been a highly cited researcher in mathematics in the last few years.

Peter Bühlmann is a Fellow of IMS and ASA, and a recipient of several awards, including the Winton Research Prize. He served as Co-editor of the *Annals of Statistics *(2010–12), and has guided 29 doctoral students, to date.

—

Donald B. Rubin, the John L. Loeb Professor of Statistics at Harvard University, has received several awards this year. He won the 2017 Rao Prize for Outstanding Research in Statistics ( the last winner was David Cox in 2015); the 2017 Waksberg Prize for Contributions to Survey Methodology; and the 2017 ISI Karl Pearson Prize, shared with Roderick Little for their book on missing data, fist published in 1987. In January he received an Honorary Degree (his fourth) from the Medical Faculty at Uppsala University, Sweden; and will shortly receive one from Northwestern University, Evanston, Illinois (the first three are from Bamberg University, Germany, the University of Ljubljana, Slovenia, and Santo Tomas University, Columbia).

—

Among the Society for Industrial and Applied Mathematics (SIAM) 2017 SIAM Fellows is Emmanuel Candès, Stanford University. The 28 Fellows were nominated for their exemplary research as well as outstanding service to the community. Emmanuel’s citation reads: *For pioneering work in mathematics of information, compressive sensing, computational harmonic analysis, statistics, and scientific computing.*

See https://sinews.siam.org/Details-Page/siam-announces-class-of-2017-fellows

—

Professor Jeff Wu, Georgia Tech’s Stewart School of Industrial & Systems Engineering (ISyE) Coca-Cola Chair in Engineering Statistics, has received the 2017 Box Medal Award from ENBIS, the European Network for Business and Industrial Statistics.

The Box Medal is named after George Box, the late British–American statistician who is considered one of the greatest statistical minds of our time. Box was extremely influential on Wu’s work during his formative years as a young academic at the University of Wisconsin–Madison, where Box was also a professor. In a 2015 interview with Hugh Chapman and Roshan Joseph, Wu said Box was “a great scholar and a great lecturer. His opinions and passion for work were contagious … I respected him a lot.”

The ENBIS press release announcing Wu as this year’s Box Medal recipient stated that “with the medal, the link between two great statisticians is strengthened even further.” The press release also noted that Wu was chosen for his many contributions to the study of statistics, as well as “his ability to clearly explain complex concepts … and for systematically passing on his knowledge.” Wu has supervised 45 PhD students, many of whom are active researchers in the statistical sciences.

Jeff Wu will accept the Box Medal, and deliver a keynote speech, at the ENBIS conference, held from September 9–14, 2017, in Naples, Italy. See https://www.isye.gatech.edu/news/isye-professor-jeff-wu-receives-2017-enbis-box-medal-award-achievements-statistics

—

IMS Fellow Professor Gordon Slade, University of British Columbia, has been elected a Fellow of the UK’s Royal Society. Gordon’s research is in the fields of probability theory and mathematical physics, especially statistical mechanics. He is well-known for his work on the mathematical study of critical phenomena and phase transitions. With his collaborators, he developed the “lace expansion” into a powerful and flexible method for the analysis of high-dimensional critical phenomena in many mathematical models of interest in physics, including the self-avoiding walk and percolation. In more recent work, he and his collaborators have developed a rigorous renormalisation group method for the analysis of the critical behaviour of spin systems and the weakly self-avoiding walk. His awards include election as Fellow of the Royal Society of Canada in 2000, the CRM-Fields-PIMS Prize in 2010, and a University of British Columbia Killam Teaching Prize in 2017.

—

On April 20, Clifford Spiegelman was named the first official statistician of the Texas Holocaust and Genocide Commission. Spiegelman will aid in producing the Educator Survey, a major project for the commission. The survey will help the commission gain an understanding of what Texas educators know of the Holocaust and what they are teaching about this seminal event. William McWhorter, executive director of the commission, wrote that the survey is critical to meeting the commission’s mission and, with Spiegelman’s assistance, they hope to produce the most effective Educator Survey possible.

Visit the commissions’ website for details: http://thgc.texas.gov/

]]>

**Regina Nuzzo**, one of our new Contributing Editors, has a PhD in statistics and is also a graduate of the Science Communication program at University of California–Santa Cruz. Her work as a part-time freelancer over the past 12 years has appeared in Nature, New Scientist, Scientific American, Reader’s Digest, the New York Times and the Los Angeles Times, among others. In 2014 she received the American Statistical Association Excellence in Statistical Reporting Award for her Nature feature on p-values.

*Her geeky Latin friends tell her that a rough translation of the name of this column, Regina Explicat, is, “The queen disentangles.” She explains why below.*

—

A couple of years ago when I was at a conference at Stanford I spotted a fellow science journalist—Christie Aschwanden, a writer for the digital magazine FiveThirtyEight—pulling aside attendees one by one into the courtyard, where she would then flip on her video camera and fire a single question at them.

The interviewees—all gathered for the inaugural METRICS conference on “meta-science” to improve biomedical research—had different reactions. Some squirmed uncomfortably at the question, some gamely gave their best answer, and others (like me) dodged it entirely. Aschwanden eventually put them together for a short FiveThirtyEight article—one that, to be honest, was not entirely flattering to the field of statistics.

The hard-hitting question posed to the attendees: **“What is a p-value?”**

Aschwanden apparently interpreted our fumbling discomfort to mean that no one could say what this statistic really is—not even statisticians themselves.“I figured that if anyone could explain *p-*values in plain English, these folks could,” she wrote. “I was wrong.” And the subtext, perhaps, was that if experts couldn’t communicate it, then journalists and other non-statisticians shouldn’t feel too bad if they didn’t understand it either.

I think she was missing the point.

To be fair, Aschwanden was in a tough spot as a journalist. She’d recently had to print a correction to an article she’d written for FiveThirtyEight on cloud seeding, in which a *p-*value of 0.28 had been miscommunicated: “An earlier version of this article misstated the chance that cloud seeding produced a 3 percent increase in precipitation. There was a 28 percent probability that the result was at least that extreme if cloud seeding doesn’t actually work, not a 28 percent chance that the research could have happened by chance.”

Aschwanden’s original language, in turn, had been pulled from the official executive summary for the study her article was based on, the Wyoming Weather Modification Pilot Program: “The primary statistical analysis yielded a RRR [root regression ratio] of 1.03 and a *p-*value of 0.28. These results imply a 3% increase in precipitation with a 28% probability that the result occurred by chance.” (Ah, yes, the flipped conditional probability.)

So you can see why journalists might be frustrated. How can they convey to their readers the implication of *p* = 0.28 if they don’t know how to communicate it well themselves, and neither do the expert scientists?

This issue is not limited to *p-*values, of course. We could be talking instead about confidence intervals, odds ratios, nonparametric methods, Bayesian networks, logistic regression. This is about statistics communication—or, more broadly, “quantitative communication.”

And that leads to why I think Aschwanden’s bit of mathematical “gotcha” journalism ignored bigger issues at hand, but at the same time points to interesting opportunities for the statistical community.

First of all, statisticians are already quite good at communication, by and large, even if it’s not yet a formal part of our training. And what good communicators intuitively know is that audience and purpose are everything.

So I suspect the experts Aschwanden interviewed were uncomfortable with her question not because they didn’t know how to explain a *p-*value well, but simply because the question itself was devoid of context. It would have been fair to ask her, “Who is the audience? Why do they need to know this? Are you asking what this statistic is, or are you asking how it’s used? Do you have time for a concrete example, or is this just a sound bite?”

There’s no one-size-fits-all explanation for statistical ideas.

Yet while we may already be decent communicators, we can do better still. A good start would be discussions about best practices for discussing our work in different contexts, in which we realize that we may get only five minutes in a courtyard instead of a semester in the classroom.

Aschwanden wrote that her favorite *p-*value explanation invoked a coin-flip experiment. We could ask: Do examples like these strike the right balance of accuracy, simplicity, and brevity for this audience? Or should we focus on what *p-*values mean for researchers’ behavior, an idea discussed a few years ago on Andrew Gelman’s blog (http://andrewgelman.com/), rather than the number itself?

In these pages over the next year I plan to explore what good quantitative communication looks like, what we can learn from the scientists who have found ways to engage better with lay audiences, and also what’s unique about our own communications niche.

Despite the above example, this will not in fact be a column about *p-*values. Nor will this be a column about grammar, or even writing. Communication is much more than that. Hence the column’s name: *explicare* is to explain, unfold, disentangle, which feels like the perfect physical manifestation of communicating statistics.

I’d love to hear people’s ideas on this topic, so feel free to drop me a line:

Regina.Nuzzo@Gallaudet.edu.

The Wolfgang Doeblin Prize, which was founded in 2011 and is generously sponsored by Springer, is awarded biannually to a single individual who is in the beginning of his or her mathematical career, for outstanding research in the field of probability theory. The awardee will be invited to submit to the journal *Probability Theory and Related Fields* a paper for publication as the Wolfgang Doeblin Prize Article, and will also be invited to present the Doeblin Prize Lecture at a World Congress of the Bernoulli Society, or at a Conference on Stochastic Processes and their Applications.

More information about the Wolfgang Doeblin prize and past awardees can be viewed at http://www.bernoulli-society.org/index.php/prizes/

Each nomination should offer a brief but adequate case of support and should be sent by November 15, 2017, to the chair of the prize committee at the following e-mail address: Kavita_Ramanan@brown.edu with subject heading: **Doeblin Prize 2018**.

Kathie Bailey, Director of the Board on International Scientific Organizations at the National Academies, reported an update following the community comment period: “Not all applicants will be asked to complete the supplementary form, but the exact number of applicants who will be asked to complete it is still unknown. The new questions will be voluntary, but the form states that, ‘failure to provide the information may delay or prevent the processing of an individual visa application’.”

The letter from societies can be viewed on the AAAS website at https://mcmprodaaas.s3.amazonaws.com/s3fs-public/DS-5535%20Supplemental%20Questions%20for%20Visa%20Applicants%20Emergency%20Submission%20Comment%20051817%20FINAL.pdf

The National Academies International Visitors Office (IVO) provides direct assistance to scientists, engineers, and students in those fields who are experiencing difficulties or delays with US visa applications. The IVO works directly with the Department of State to seek resolution of those problems. Individuals experiencing difficulties should complete the questionnaire on the IVO’s webpage (http://sites.nationalacademies.org/PGA/biso/visas/index.htm). The IVO also works with organizers of large scientific meetings (over 100 foreign attendees), and registers those meetings with the Department of State.

If you are a non-US student with questions about traveling to the US for study, the NAFSA: Association of International Educators has created an excellent FAQ resource available at https://www.nafsa.org/2017/03/15/tips-for-surviving-in-a-time-of-immigration-uncertainty/.

]]>Alastair Scott, one of the finest statisticians New Zealand has produced, died in Auckland, New Zealand on Thursday, May 25. He served the University of Auckland with distinction from 1972 to 2005.

His research was characterised by deep insight and he made pioneering contributions across a wide range of statistical fields. Alastair was acknowledged, in particular, as a world leader in survey sampling theory and the development of methods to efficiently obtain and analyse data from medical studies. His methods are applied in a wide range of areas, notably in public health. Beyond research, he contributed prolifically to the statistical profession in academia, government, and society.

Alastair was a Fellow of the Royal Society of New Zealand, the American Statistical Association, the Institute of Mathematical Statistics and the Royal Statistical Society, and an honorary life member of the New Zealand Statistical Association. In November last year, Alastair was awarded the Royal Society of New Zealand’s Jones Medal, which recognised his lifetime contribution to the mathematical sciences.

Alastair gained his first degrees at the University of Auckland: BSc in Mathematics in 1961 and MSc in Mathematics in 1962. After a period at the New Zealand Department of Scientific and Industrial Research, he pursued a PhD in Statistics at the University of Chicago, graduating in 1965. He then worked at the London School of Economics from 1965-1972.

Alastair returned to New Zealand in 1972 to a post in what was then the Department of Mathematics and Statistics at the University of Auckland; he and wife Margaret had decided that they wanted to raise their children, Andrew and Julie, in New Zealand. Throughout his career, Alastair was regularly offered posts at prestigious universities overseas, but turned them down. However, he held visiting positions at Bell Labs, the universities of North Carolina, Wisconsin, and California Berkeley in the US, and at the University of Southampton in the UK.

In 1994, the University’s statistics staff, led by Professor George Seber, had a very amicable divorce from the Department of Mathematics and Statistics, and Alastair became the head of the new Department of Statistics. He helped set the tone for the department that still exists—hard-working, but welcoming, and social. The Department of Statistics is now the largest such school in Australasia.

In 2005, Alastair officially retired. A conference in Auckland that year in his honour attracted the largest concentration of first-rank international statisticians in New Zealand in one place at one time. Alastair kept an office in the department and continued writing and advising, coming into work almost every day.

Alastair Scott was an influential teacher and generous mentor to several generations of statisticians who valued his sage advice coupled with his trademark affability. Alastair had a full life professionally and personally. He was a wonderful teacher, mentor, colleague, and friend. We will all miss him greatly and we extend our sincere condolences to Margaret, Andrew and Julie, and his family, friends, and colleagues all over the world.

—

*Written by Ilze Ziedins, Chris Wild, and Chris Triggs, **Department of Statistics, University of Auckland, New Zealand*

A full obituary will appear in a future issue.

]]>*Subhashis Ghoshal is a professor of statistics at North Carolina State University, Raleigh. His research interests span many areas including Bayesian statistics, asymptotics, nonparametrics and high dimensional models, with diverse applications. In particular, his pioneering work on concentration of posterior distributions led to theoretical understanding of nonparametric Bayesian procedures. He was honored with fellowship from the IMS (2006), ASA (2010) and ISBA (2016). He has received several awards, including the P.C. Mahalanobis Gold Medal (1990), Indian Science Congress Young Scientist Award (1995), NSF Career Award (2003), Sigma-Xi Research Award (2004), International Indian Statistical Association Young Researcher Award (2007) and Cavell Brownie Mentoring Award (2015). He held the honorary positions of Eurandom Chair (2010–11) and the Royal Netherlands Academy Arts and Sciences Visiting Professorship (2013–14). His research has been supported by several US federal funding agencies, European granting institutions and industry grants. He serves or has served on the editorial boards of many leading statistics journals including the Annals of Statistics, Bernoulli, Electronic Journal of Statistics and Sankhya. Seventeen doctoral students so far have graduated under his advising.*

*Subhashis Ghoshal’s Medallion lecture will be given at the 2017 Joint Statistical Meetings in Baltimore (July 29–August 4, 2017). See the online program at http://ww2.amstat.org/meetings/jsm/2017/onlineprogram/index.cfm*

Subhashis Ghoshal’s Medallion lecture will discuss frequentist coverage properties of Bayesian credible sets for nonparametric and high dimensional models. Bayesians and frequentists quantify uncertainty in very different ways. While a Bayesian’s uncertainty quantification is based on a direct probability assessment of the parameters and thus has excellent interpretation in a conditional framework, potential strong dependence on the prior may lead to confusion. This necessitates the study of frequentist coverage of Bayesian credible sets. In the classical setting of repeated sampling from fixed dimensional smooth parametric families, the celebrated Bernstein–von Mises theorem asserts that the posterior distribution of the parameter is asymptotically normal with mean at the maximum likelihood estimator and variance the inverse of Fisher information. The most important consequence of this result is that the coverage of a Bayesian credible set approximately matches its credibility, and hence Bayesians and frequentists are in agreement about uncertainty quantification. Such a matching continues in many other fixed dimensional parametric families which are not smoothly parameterized as well as in models where the number of parameters grows to infinity sufficiently slowly. In certain nonparametric problems with parametric convergence rate (like that of estimating a distribution function), empirical estimators typically have Gaussian process limits by Donkser-type theorems. For certain priors, the posterior of the function centered at the empirical estimator can have the same limit, thus again Bayesian credible sets will have asymptotically valid coverage. A similar behavior is observed in many semi-parametric models for the parametric part, or for certain differentiable functionals of the function indexing a nonparametric model. In certain parametric models described by structural relations like in differential equation models, a type of “projection posterior” distributions gives credible sets with valid asymptotic frequentist coverage. However, in smoothing problems with interest in the whole function, all these niceties go away, even for the simple signal plus white noise model although a conjugate prior is immediately available. Under optimal smoothing to produce the best convergence rate, posterior credible regions may have arbitrarily low asymptotic frequentist coverage. The reason for such an anomaly is that the order of the bias under optimal smoothing matches the order of posterior variability, thus poorly centering posterior credible sets and spoiling coverage. Interestingly, the disagreement between Bayesian credibility and frequentist coverage at fixed credibility level may go away in the high credibility regime. In general the problem may be resolved by slightly inflating Bayesian credible sets, especially if uniform credible bands are desired. Assuring frequentist coverage of a Bayesian credible set which adapts its size to the smoothness of the underlying true function is a lot more subtle. The only possible way to maintain both coverage and size is to discard certain “deceptive parameters” from consideration which lead to “excessive bias” in the procedure. Then optimal sized inflated credible sets with guaranteed asymptotic coverage can be obtained. The talk will be concluded by considering a setup of shape-restricted models, for which it is observed that asymptotic coverage of a Bayesian credible set can be obtained explicitly but it may differ from the corresponding frequentist coverage. A simple modification of a credible set is devised to guarantee a desired coverage level.

]]>*Judith Rousseau is currently Professor at University Paris Dauphine. Her research interests range from theoretical aspects of Bayesian procedures, both parametric and nonparametric, to more methodological developments. From a theoretical perspective she is interested in the interface between Bayesian and frequentist approaches, looking at frequentist properties of Bayesian methods. From a more methodological perspective, she has worked on MCMC or related algorithms or on the elicitation of subjective priors. She is an associate editor of the Annals of Statistics, Bernoulli, ANZJS and Stat, and is currently the program secretary of IMS. She has also been active on various aspects of the ISBA society. She is an ISBA and an IMS fellow and received the Ethel Newbold prize in 2015.*

Bayesian nonparametrics has become a major field in Bayesian statistics, and more generally in statistics, over the last couple of decades with applications in a large number of fields within biostatistics, physics, economics, social sciences, computational biology, computer vision and language processing. Bayesian approaches are based on both a sampling model about observations given a parameter and on a prior model on the parameter.

With the elaboration of modern complex and large dimensional models, the need to understand their theoretical properties becomes crucial, in particular to understand what are the underlying assumptions behind the prior model. One way to shed light on such assumptions is to study the frequentist properties of the Bayesian procedures.

Consider a statistical model associated to a set of observations *Y ^{n}* ∈ Y

In large-dimensional models the influence of the prior is strong and does not entirely vanish asymptotically, i.e. when the information in the sample increases. It is then of interest to understand the types of implicit assumptions which are made by the choice of a specific prior and also within a family of priors which are the hyperparameters whose influence does not disappear as the number of observations increases.

Among the (many) advantages of Bayesian approaches is the fact that the inference is based on a whole probability distribution on the unknown parameter *ϑ*, namely the posterior distribution which is the conditional probability distribution of the parameter given the observations. With such a flexible tool, one can derive not only point estimators but also various measures of uncertainty. A common way to derive such measures of uncertainty is to construct credible regions, which are regions of the parameter space which have large posterior probability. These regions obviously depend on the prior distributions and it is important to understand how they are impacted by the assumptions (not necessarily explicit) made by the choice of the prior model. A way to do so is to study the asymptotic frequentist properties of these regions. The Bernstein–von Mises Theorem is a powerful tool to conduct such studies.

In my lecture I will describe some of the recent advances that have been obtained in the study of the Bernstein–von Mises theorem in large- and infinite-dimensional models, concentrating mainly in cases where only a finite-dimensional parameter is of interest in an infinite-dimensional model. I will consider both regular and irregular models.

]]>—

After a relaxed rendezvous with effulgent nothingness, we should now seriously get back to the problem corner. It is the turn of a problem in statistics.

We will pose a problem on **deconvolution**, sometimes brandished as *noisy data*. The basic model is that you get to observe a random variable $Y$ which has the distribution of the convolution of $X$ and $Z$, it being usually assumed that $Z$ has a completely known distribution, while the distribution of $X$ has unknown parameters, perhaps infinite dimensional, associated with it. We would want to infer about the distribution of $X$, knowing only $Y$; often, it is assumed that iid replicates of $Y$ are available. There is massive literature on deconvolution, particularly Gaussian deconvolution. Generally, the results are asymptotic in some sense. The problem we describe today was originally posed by C.R. Rao in 1952.

Here is the exact problem of this issue.

Suppose $X \sim \mbox{Bin}(n_1, 1/2)$ and $Z \sim \mbox{Bin}(n_2, p)$, $0 < p <1$ being an unknown parameter; $X$ and $Z$ are assumed to be independent. Due to (spatial) aggregation, we can only observe $Y = X+Z$.

(a) Is there always an MLE of $p$?

(b) In suitable asymptotic paradigms, are there consistent estimators of $p$ based on $Y$ alone?

(c) How does one construct a confidence interval for $p$, again, based on $Y$ alone?

(d) What can be said about minimax estimation of $p$ on the basis of $Y$, using squared error loss?

—

The problem asked was about the first hitting time $\tau $ of the set of prime numbers by the process $\{S_n\}, S_n$ being the sum of the first $n$ rolls in an infinite sequence of rolls of an honest die. Evidently, $P(\tau < \infty ) > 0$. The set of primes $\mathcal{P}$ is an infinite set, and in fact, with probability one, (the process) $S_n$ will hit $\mathcal{P}$ infinitely many times (see, e.g. Feller, Vol. 2, pp 360, 381). We know from classic number theory that for fixed $n$, large, $P(S_n \in \mathcal{P}) \approx \frac{1}{\log n}$, and since the series $\sum_{n = 2}^\infty \frac{1}{\log n}$ diverges, the expected number of hits is easily $+\infty $. The tailsum formula tells us

$E(\tau ) = 1 + \sum_{n = 1}^\infty P(\tau > n) = 1 + \sum_{n = 1}^\infty P(\cap _{k = 1}^n S_k \not\in \mathcal{P})$,

and a geometric approximation by terminating the infinite series at one million terms gives $E(\tau ) \approx

7.6$. The initial terms $P(\tau > k)$ can be calculated exactly. By summing $P(\tau > k)$ from $k = 0$ to $9$, we get $E(\tau ) > 2.34 > 7/3$.

It is less clear if $\tau $ has a finite variance, but it probably does. To my knowledge, the answer is not explicitly known.

**References: **

Brown, L. (private communication)

Davis, B. (private communication)

Feller, W. (1971)

Pitman, J. (private communication; provided an approximate value of 7.62 for $E(\tau )$)

Rao, C.R. (1952)

Szekely, G. (private communication)

A good number of people have asked me about what have been the best and the worst parts of being a dean. Whereas the worst part should only be shared over two glasses of Long Island iced tea (my first and still the most memorable iced tea I had in the US, though I have no memory of who paid for it), there are several “best parts” I am willing to share any time. Among the best parts are the opportunities to speak to many young talents about the roles of statistical thinking in their lives, especially as they are about to start their post-university lives. Perhaps as a fitting souvenir of having survived deanship for five years, I had the honor of delivering two graduation speeches this May, instead of the usual one for GSAS (Graduate School of Arts and Sciences) at Harvard. The extra one was at the kind invitation of the Department of Mathematics and Department of Statistics at the University of Illinois at Urbana-Champaign, where I took the opportunity to repeat a similar message as conveyed in two previous XL-Files (http://bulletin.imstat.org/2013/11/xl-files-romantic-regression-towards-the-mean/ and http://bulletin.imstat.org/2016/05/xl-files-lectures-marriages-that-last/). For those of you who just cannot get enough of regression towards the mean (and in regions where YouTube is not MuteTube), you can find my 15 minutes of fame between minutes 29 and 44 in the following video: https://www.youtube.com/watch?v=xQGBKNHLFqM&feature=youtu.be

For my regular GSAS one (which has been always held at Harvard’s largest classroom, Sanders Theater), I decided to give the Law of Large Numbers (LLN) a shot, especially as it has helped me to be a happier person—and a better fundraiser. Curious? Read on…

“How many of you heard my welcoming speech when you joined GSAS, in this very Sanders Theater? OK, I gather the rest of you either skipped your new student orientation or didn’t feel the urge to complete your degree within five years. Thanks to President Faust and Dean Smith’s trust and many colleagues’ strong support, I have had the privilege to serve as GSAS Dean for the past five years, and this commencement marks the completion of my first term as GSAS Dean. Naturally I reflect on what I have learned, starting from day one. I surmise that how I felt five years ago is not very different from how many of you are feeling right now: excited, anxious, and bedeviled by self-doubt: am I really ready to navigate a new world?

“Well, if you are seeking reassurance from me, my response will be a very short one: NO! I was not ready, nor are you ready for whatever your new world will bring, even if it’s just another degree program. A curveball is easy to handle because at least you know it is a ball, and the curve is eventually coming in your direction. But you just don’t know what you don’t know.

“I am not trying to scare you off so you can take the dropout option — it’s too late for that anyway, and indeed there is no way to drop out of life, only to drop dead. But I would like to share with you a key lesson that I learned in navigating a new world: the insights generated from whatever disciplines you studied can help you in ways that you may not expect. And God forbid, should your field not generate any useful insights (I’m sure Dean Smith will want to know which field this is, so he can stop funding it), you are always welcome to borrow mine, that is, statistics.

“So let me give you an example. A good part of my job is fundraising, for which I received no training whatsoever. But I was intrigued by it. Why would anyone give me money just because I asked for it? Can I be that persuasive or charming?

“I doubt that many of you have had fundraising experience. But I surmise that the following scenario may sound familiar to most of you. You were introduced to someone at a party and you hit it off. The evening was too short. You made an arrangement to have dinner, and it went as beautifully as you had hoped. You started to communicate with each other more frequently. The feeling was getting stronger and it seemed mutual. Your heart was starting to beat faster: OMG, this might be The One!

“Then, suddenly, it’s all silence. Your invitation for the next dinner was never answered, no text, no email, no nothing. You were completely puzzled. What did I do wrong? Did I move too fast? Did I misinterpret the whole thing from the very beginning? Am I just not that charming?

The chances are that you will never find out the real reason, no matter how much time you spend driving yourself crazy replaying every moment together, speculating, regretting, or even feeling guilty. In fundraising, that person could be someone who indeed had intended to give, but then their business went south; or someone who was flirting with multiple institutions and then decided to commit to another one; or someone who was treating philanthropy as an investment, and then realized that definitely was a mistake.

“Indeed, my initial mistake was to expect a positive return from every one of my investments, that is, the time and energy I put into building each fundraising relationship. But such expectations only bring disappointment, frustration, and even self-doubt—am I perhaps just not good at this job? Fortunately, my statistical training soon stopped me from consuming myself with these not very helpful thoughts.

“You see there are simply too many factors that are beyond my control, or even outside my awareness, that would determine the ultimate outcome of each fundraising effort. It is just unwise and unproductive for me to worry too much about each case and to overthink it. What I can predict reasonably well is the total amount of funds raised annually, which reflects the overall fundraising effort. That’s the essence of the Law of Large Numbers: while individual outcomes can vary tremendously for reasons hard to decipher, with enough trial and error, we can expect a rather stable average, capturing a central characteristic of our overall effort. That statistical insight redirected my energy from working unproductively on trying to save every fundraising relationship, to building and communicating the clear message of how additional funding can establish, sustain and enhance GSAS’s global leadership in supporting students’ well-being, scholarly training, and professional development.

“I also started to enjoy those fundraising conversations much more, because I no longer needed to worry about where any particular conversation would lead. All I cared about was knowing that as long as we communicated our message loudly and clearly, to as many people as possible, we would do better and better. Indeed, one day I received the largest check in my life from a GSAS alum, with a simple note: “Dean Meng, here is my number. Give me a call.” I called, and the alum told me that he very much liked the effort we were making and wanted to support it in ways he could. That’s how we were able to fund the new Center for Writing and Communicating Ideas, located in Dudley House, a center that celebrates writing and communication as a critical part of graduate education; it might already have helped a few of you to arrive here today.

“So, the Law of Large Numbers helped me to be more productive and happier. And I hope it can help you, too, as you navigate your new world, both professionally and personally. You of course should have high aspirations and you should work hard to achieve your goals. But you should not expect a positive return from every effort you make. That would make you miserable, and worse, make everyone around you miserable. I have seen some very unhappy colleagues, unfortunately in every generation, trying to receive recognition for everything they do, to compete and expect to win every grant or award, and to advance their careers at every possible opportunity. Perhaps the saddest thing is that many of them would have achieved what they wanted if only they hadn’t tried so hard, thereby making themselves less respected or liked by their peers. I certainly hope you won’t become one of them. With 95% confidence, I can also guarantee that your love life won’t last too long if you expect an ounce-for-ounce return every time you do something nice for your love interest. Keeping the Law of Large Numbers in mind can help to remind you that the payoff of your effort comes in aggregation and on average. That should be your aim, not to expect unrealistically positive returns in every effort you make.

“To practice what I just preached, and having given each of you some profound advice on how to have a happy (or at least a happier) life, I am not expecting a positive return from each of you. But I do expect that someday I will receive a few checks from some of you with a note, “Dean Meng, here is my number. Call me.” In fact, I am willing to expect even less. No need to write a note; just put your number on your check. I will call. Until then, may the Law of Large Numbers always be with you, and may your life be happier than those who don’t respect the law. Congratulations!”

—

Of course, all such life lessons have to be taken with “a grain of statistics,” especially regarding their precise statements. For example, a serious reader might worry about if the assumptions for LLN can be hold here – surely i.i.d would be problematic, as it would imply that things never improve (or deteriorate) on average. As I have already used up twice as many pages as my regular allotment permits, I’d leave it to the interested readers to impute what I didn’t have space (or time) for. For the rest, think about LLN the next time you are so bothered by a particular outcome. I guarantee that the thought would make some of you happier, but just don’t ask me which ones of you …

]]>The Institute of Mathematical Statistics has selected **Elyse Gustafson **as the recipient of this year’s Harry C. Carver Medal. The award is made for Elyse’s exceptional service and dedication as Executive Director of the IMS over the past 20 years. Throughout this time, which included relocation of the IMS office, unpredictable fiscal challenges and substantial changes in the publishing industry, the IMS functioned smoothly as a preeminent society and publisher, under the administrative leadership of Elyse Gustafson. As the sole permanent staff person, Elyse has admirably managed a team of dedicated contractors and provided outstanding support for the IMS Executive Committee, Council, multiple IMS committees and journal editorial boards. She is especially recognized for her extraordinary ability to cooperate efficiently and cheerfully with a huge number of members who volunteer their time to help with IMS activities and who have a wide range of ideas and working styles.

Elyse will receive the Carver Medal at the IMS Presidential Address and Awards ceremony on Monday, July 31, at JSM in Baltimore (8:00pm in Ballroom 1. See the JSM online program).

On hearing about her award, Elyse said, “I am surprised and honored to receive the Carver Medal. Working for the IMS for the last 20 years has been incredibly fulfilling. The volunteer leadership of the IMS is deeply dedicated to the mission of the organization. They are what makes this job so rewarding. Cultivating the organization together with these leaders has been more than I can hope for. I look forward to many more years together.”

The Carver Medal was created by the IMS in honor of Harry C. Carver, Founding Editor of the *Annals of Mathematical Statistics* and one of the founders of the IMS. The medal is for exceptional service specifically to the IMS and is open to any member of the IMS who has not previously been elected President. See http://www.imstat.org/awards/carver.html for more information on the nomination process (it’s not too early to start thinking about nominations for next year! You can check the list of past recipients).

Maury D. Bramson, professor of mathematics at the University of Minnesota, Minneapolis, was among the 84 new members and 21 foreign associates elected to the US National Academy of Sciences. Members and Associates are elected in recognition of their distinguished and continuing achievements in original research. Those elected this year bring the total number of active members to 2,290 and the total number of foreign associates to 475.

Maury Bramson works in probability theory, including interacting particle systems (with applications to mathematical physics, physical chemistry, and biological systems), branching Brownian motion (with applications to mathematical physics and biological systems), and stochastic networks (with applications to electrical and industrial engineering, computer science, and operations research). Among his honors, Bramson is a fellow of IMS and the American Mathematical Society, and was an invited speaker at the 1998 International Congress of Mathematicians.

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