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The last paper of the month for the academic year appeared in the Journal of Irreproducible Results (JIR). The title of the paper by Bennett et al. is “Neural Correlates of Interspecies Perspective Taking in the Post-Mortem Atlantic Salmon: An Argument For Proper Multiple Comparisons Correction”. The authors of the paper received the Ig Nobel prize in 2012, for neuroscience. According to the Ig Nobel website “The Ig Nobel Prizes honor achievements that first make people laugh, and then make them think.” Indeed, although the chosen paper of the month is light-hearted and has appeared in a science humor magazine, it makes a very good argument for the importance of accounting for multiple testing, especially in modern applications, where the number of simultaneous tests can be very large.
Notes Preparer: Haim Bar
Even an undergraduate student taking a regression course knows how to do model selection or variable selection through model selection criteria such as AIC (Akaike, 1974) or BIC (Schwarz, 1978). Both criteria penalize the measure of fit (log-likelihood) with a multiple of the number of free parameters to combat overfitting. BIC penalizes the number of parameters more heavily than AIC.
AIC is an estimate of the Kullback-Leibler divergence while BIC is an approximation of the posterior probability of a candidate model. AIC is best for prediction as it is asymptotically equivalent to leave-one-out validation. BIC is best for explanation as it allows consistent estimation of the underlying data generating process.
AIC and BIC should be learned together. It is tempting to wonder why the penalty scales on the number of parameters are what they are, which is usually not in textbooks. Both original papers are among the most cited statistical papers; as of now, Akaike (1974) has 39K and Schwarz (1978) has 34K Google citations. We present Schwarz (1978) simply because, despite its importance, it is short (3 pages with 4 references on the 4th page) and accessible. The justifications were made even easier to follow by Cavanugh (1997, Stat Prob Letters) for AIC and by Neath and Cavanaugh (2012, WIREs Comp Stat) for BIC. A StackExchange discussion is also a fun read.
Notes Preparer: Jun Yan
The “Paper of the Month” for February 2018 is selected to be W. Hoeffding’s 1948 paper “A Class of Statistics with Asymptotically Normal Distribution” where he introduced the concept of U-statistics and built much of the heavy-duty machineries. It was published on The Annals of Mathematical Statistics Vol. 19, No. 3 (1948), pp. 293-325. It was included in the “Breakthroughs in Statistics Volume 1: Foundations and Basic Theory” which was edited by S. Kotz and N. L. Johnson (1992, Springer, New York) with a lengthy and immensely valuable introduction prepared by P. K. Sen.
I suggested W. Hoeffding’s 1948 paper from the Annals of Math Stat where he introduced the concept of U-statistics and built much of the heavy-duty machineries. It was included in the “Breakthroughs in Statistics Volume 1: Foundations and Basic Theory” which was edited by S. Kotz and N. L. Johnson (1992, Springer, New York) with a lengthy and immensely valuable introduction prepared by P. K. Sen.
This paper changed completely the face of nonparametric statistics – both theory and practice – by creating the fundamentals to take this field to maturity. The Hoeffding paper indeed impacted many areas of classical statistical inference by showing how one must handle the probability theory behind the CLT and SLLN for dependent sequences of random variables. It gave rise to new and challenging pathways to handle moderate and large-deviation theories in the case of dependent sequences of random variables.
I totally fell in love with Hoeffding’s 1948 paper in the early 70’s when I was a PhD student at the Indian Statistical Institute-Calcutta. I had great opportunities to use his fundamental projections, CLT, and SLLN with total practical relevance of martingales and reverse martingales in my 1975 thesis-work. Personally, I have gone back to Hoeffding’s paper several hundred times in my life for its fundamentally breakthrough and truly fresh ideas. Beauty in this paper remains astonishing and unmatched given its path-breaking influences on numerous big sub-fields within statistics and probability theory including nonparametrics, large-sample theory, CLT’s for dependent processes and invariance principles, Berry-Esseen rates, theory of martingales and reversemartingales, tightness, geometric probability, and CLT’s for convex bodies.
I feel lucky that I could learn to appreciate the marvel of U-statistics theory early on (beginning 1970) in my career from P. K. Sen, Malay Ghosh, and Bob Serfling (all had close ties with Hoeffding) which helped me to build a large volume of my own significant publications to show for it. I also had the great pleasure of advising a number of PhD students (including Gaute Vik, John Judge, Mabel Moreno, and Bhargab Chattopadhyay) whose research have been significantly touched and influenced by Hoeffding’s paper. Two present students’ (Jun Hu and Chen Zhang) research overwhelmingly exploit Hoeffding’s theory of U-statistics. Our own Rick Vitale (with Herman Rubin) gave a complete asymptotic theory for square-integrable U-statistics in their 1980 Annals of Stat. paper.
After nearly 70 years of publication, W. Hoeffding’s original 1948 paper still inspires me and energizes me big time. Upon rereading the original paper the n-th time, I continue to surprise myself by discovering something interesting, deep and new that remained hidden from me during my previous passes.
Happy reading of Hoeffding (1948) paper. Just live it with TLC!
Notes Preparer: Nitis Mukhopadhyay.
December 2017–January 2018
Controlling the probability of falsely rejecting the null hypothesis is critical for valid and meaningful statistical analysis. But how should this probability be defined and calculated when there are multiple, simultaneous hypotheses? For many years, this question was mostly investigated in the analysis of variance (ANOVA) setting, in which the number of comparisons is typically small or moderate. Until 1995, the common approach to this problem was to control the family-wise error rate (FWER) which ensures that the probability of falsely rejecting at least one of the hypotheses is smaller than a user-specified bound. However, the advent of high-throughput methods, such as in genetics, resulted in a much larger number of simultaneous hypotheses and rendered the FWER approach impractical, in the sense that it was too stringent and lacked power to reject any hypothesis.
In 1995, Benjamini and Hochberg published their breakthrough paper “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing” in which they defined the False Discovery Rate (FDR) as the expected proportion of falsely rejected hypotheses, out of the total number of rejected hypotheses. The paper proposed a very simple procedure to estimate the FDR, and showed that indeed, the procedure controls the FDR at the desired level. This approach enabled significant advances in many areas, and is particularly useful in this age of “Big Data”.
The paper appeared in the Journal of the Royal Statistical Society, Series B, Vol. 57, No. 1. (1995), pp. 289-300. For additional reading, we also suggest reading about the q-value in the paper “A direct approach to false discovery rates“, by John Storey, which appeared in 2002, in the Journal of the Royal Statistical Society, Series B, 64: 479-498.
Notes Preparer: Haim Bar
Once a month during the academic year our faculty will select a paper which we encourage our students to read and discuss. Papers featured in this section should be generally understood by graduate students, and will be selected either because of their impact, or historical value, or because they contain a useful (perhaps overlooked) techniques or results.
The inaugural “Paper of the Month” (November 2017) was selected to be Brad Efron’s paper titled “Bootstrap Methods: Another Look at the Jackknife”, published in 1979 in the Annals of Statistics (Vol. 7, No. 1, pages 1-26). It is hard to overstate the impact of this paper. It allows researchers to construct confidence intervals in many settings, even when there is no closed-form derivation of the standard deviation.
A glance at Efron’s Google scholar page shows that this work was already cited tens of thousands of times. In his column in the IMS Bulletin from November 17, 2016, Professor Xiao-Li Meng included this paper among his five selected “Nobel-Prize (NP) Worthy i.i.d Ideas in Statistics” and wrote: “[the bootstrap] certainly has made many researchers’ lives much easier”, and that it “has literally generated an industry of research on proving when it works, when it doesn’t, and how to make it work when its vanilla version fails.” For example, we may refer to the paper “Two Guidelines for Bootstrap Hypothesis Testing” by Peter Hall and Susan R. Wilson (Biometrics, Vol. 47, No. 2 (Jun., 1991), pp. 757-762)
Notes Preparer: Haim Bar