Starting down the road to largescale inference, suppose now we are dealing with many. We live in a new age for statistical inference, where modern scientific technology such as microarrays and fmri machines routinely produce thousands and sometimes millions of parallel data sets, each with its own estimation or testing problem. Large scale inference bradley efron by amandawhiteside issuu. That largescale problem of multiple comparisons led efron et al. Pdf large scale statistical inference of signaling. Read the foreward for a concise introduction to this stanford statistics course taught by bradley efron. Efron has worked extensively on theories of statistical inference, and is the inventor of the bootstrap sampling technique. Applications of popular analysis methods, such as false discovery rate techniques, do not require independence of the zis, but their accuracy can be compromised in highcorrelation situations. Empirical bayes methods for estimation, testing, and prediction institute of mathematical statistics monographs. Bayesian statistics, bootstrapping and machine learning algorithms, and finally large scale inference. Inference after selection with the false discovery rate. Doing thousands of problems at once involves more than repeated application of. I chose the adjective largescale to describe massive data analysis prob.
Correlation and largescale simultaneous significance. A wide range of empirical bayes applications have the following structure. He has held visiting faculty appointments at harvard, uc berkeley, and imperial college london. Emphasis is on the inferential ideas underlying technical developments, illustrated using a large number of real examples. Maximum likelihood estimation in loglinear models fienberg, stephen e. Largescale inference stanford statistics stanford university. Carl morris, harvard university, massachusetts computer age statistical inference gives a lucid guide to modern statistical inference for estimation, hypothesis testing, and prediction. Everyday low prices and free delivery on eligible orders. Bradley efron department of statistics stanford university march 2010. Correlation and largescale simultaneous significance testing jstor. He also contrasts the methodology with more conventional multiple comparison procedures.
Fast parameter estimation in loss tomography for networks of general topology deng, ke, li, yang, zhu, weiping, and liu, jun s. We have provided text that allows you to teach and model the use of the inference reading strategies included here. Quantifying parameter uncertainty in a largescale glacier evolution model using bayesian inference. Two modeling strategies for empirical bayes estimation ncbi nih. Ebook computer age statistical inference as pdf download. Second, quadratic loss is the only proper scoring rule for probabilities that a depends. The automatic construction of bootstrap confidence intervals. This book takes a careful look at both the promise and pitfalls of large scale statistical inference, with particular attention to false discovery rates, the most successful of the new statistical techniques. Typical large scale applications have been more concerned with testing than estimation. Bayesian postselection inference in the linear model. Empirical bayes methods for estimation, testing and prediction bradley efron stanford university. Empirical bayes inference amounts to estimating certain nonlinear. Largescale observational datasets are prevalent in many areas of research, including biomedical informatics, computational social science, and finance. Empirical bayes methods for estimation, testing, and.
Largescale statistical inference blurs the line between bayesians and frequentists. The data distribution free, bca bootstrapping technique was used in this. However, our ability to use these data for decisionmaking lags behind our ability to collect and mine them. One reason for this is the lack of methods for inferring the causal impact of rare events. In the last decade, efron has played a leading role in laying down the foundations of large scale inference, not only in bringing back and developing old ideas, but also linking them with more recent developments, including the theory of false discovery rates and bayes methods. Download it once and read it on your kindle device, pc, phones or tablets. He is a past editor for theory and methods of the journal of the american statistical association, and he is the founding editor of the annals of applied statistics. Bradley efron is professor, department of statistics, stanford university. Largescale inference we live in a new age for statistical inference, where modern scienti. The choice of a null hypothesis bradleyefron current scienti c techniques in genomics and image processing routinely produce hypothesis testing problems with hundreds or thousands of cases to consider simultaneously.
Empirical bayes is an exciting new statistical idea, wellsuited to modern scientific technology, saying that experiments involving large numbers of parallel situations carry within them their own prior distribution. Bayesian information accumulates, and cannot be ignored, but the accumulation itself favors the use of frequentist tactics. Higher criticism for largescale inference, especially for. If judged by chapter titles, the book seems to share this. Largescale inferenceempirical bayesmethods for estimation,testing,prediction by bradley efron ebook free download. Efron has been president of the american statistical association 2004 and of the institute of mathematical statistics 19871988. Empirical bayes methods for estimation, testing, and prediction institute of mathematical statistics monographs reprint by bradley efron isbn. Get your kindle here, or download a free kindle reading app. However, while none of them can be classified as textbooks even though efron s has. Massive multiple testing for sparse intergroup differences. I have not looked at brad efrons course on large scale inference, which is certainly very new.
However, while none of them can be classified as textbooks even though efrons has. Underlying such problems is a sequence model, where each observation corresponds to a single parameter, and the parameters are typically not assumed to. Efron clearly explains important recent advances in estimating the local false discovery rate. Under a bayesian framework, the largescale studies allow the null distribution to be put into a probabilistic context with its nonnull counterparts. Largescale inference institute of mathematical statistics monographs kindle edition by efron, bradley. Read largescale inference empirical bayes methods for estimation, testing, and prediction by bradley efron available from rakuten kobo. In the last decade, efron has played a leading role in laying down the foundations of largescale inference, not only in bringing back and developing old ideas, but also linking them with more recent developments, including the theory of false discovery rates and bayes methods. Quantifying parameter uncertainty in a largescale glacier. Stein professor, professor of statistics, and professor of biomedical data science at stanford university.
Everetts dad lifted him into the seat of the shopping cart. Prior robust empirical bayes inference for largescale data by conditioning on rank with application to microarray data. Here we have two groups, a treatment and a control, and for each measured variable we test whether the two groups are di. Use features like bookmarks, note taking and highlighting while reading largescale inference institute of mathematical statistics monographs. Inferences an inference is a conclusion you draw based on evidence in a reading passage. Pdf causal inference with rare events in largescale. Largescale hypothesis testing problems, with hundreds or thousands of test statistics zi to consider at once, have become familiar in current practice. Inference on a selected subset of the parameters that turned out to be of interest after viewing the data. Efron focuses on empirical bayes methodology for largescale inference, by which he mostly means multiple testing rather than, say, data mining. January 1, 2000 supplement to the book contact lens complications, 2nd edition by nathan efron published by butterworthheinemann, 2004, isbn 0 7506 5534 8 vcefrongs.
On a scale of 0 to 4, it describes the severity of the following anterior ocular complications that can occur from contact lens wear. That model is simpler than those of higher values of k. The work, computer age statistical inference, was rst published by cambridge university press. Keywords classification control of fdr feature selection higher criticism large covariance matrix largescale inference rare and weak effects phase diagram sparse signal detection citation donoho, david. False discovery rate fdr control provides a useful framework for largescale multiple testing when the number of hypotheses is large efron. Empirical bayes methods for estimation, testing, and prediction bradley efron. Permutation methods permit modelfree estimates of the con ditional fdr, as. Statistical inference in twostage online controlled experiments with treatment selection and validation. Efron, bradley, winter 20092010, stats 329, largescale simultaneous inference, and accompanying data sets and programs. Bagging statistical network inference from largescale.
Outofstudy selection not evident in the published work file drawer problem publication bias. For concreteness, throughout the remainder of the paper, we focus on estimating the standardized effect size in casecontrol microarray experiments. Everett held his fathers hand as he crossed the busy parking lot. Further, gene expression data for network inference are largescale, although, the large small problem holds, because the number of explanatory variables genes exceeds the number of observations microarray samples.
Problems and solutions weve found a generic empirical bayes rankingselection method improved ranking and selection hall, p. Efron grading scales for contact lens complications sponsored by devised by professor nathan efron and illustrated by terry r tarrant. The efron grading scales provide a convenient clinical reference for eye care professionals. Doing thousands of problems at once is more than repeated application of classical methods. Cambridge core statistical theory and methods largescale inference by bradley. Computer age statistical inference now free eran raviv. So the historical perspective is delightful to read and is well placed in context. Selection criteria for scatterplot smoothers efron, bradley, annals of statistics, 2001. The idea was coined in the 1950s, but real developmental interest awaited the vast data sets of the 21st century. The chapters are a list of efrons best papers on false discovery rate fdr, bound in a book form. When sample size n is large, like over 10,000, the empirical nulls utilize a studys own data to estimate an appropriate null distribution. Since i read this book immediately after cox and donnellys principles of applied statistics, i was thinking of drawing a parallel between the two books.
Their work was inspired by and builds upon the ingenious development of the knocko. Bradley efrons 178 research works with 86,364 citations and 8,923 reads, including. Bradley efrons research works stanford university, ca. Largescale inference by bradley efron cambridge university press. Largescale inference by brad efron is the first ims monograph in this new series, coordinated by david cox and published by cambridge university press. Large scale statistical inference of signaling pathways from rnai and microarray data article pdf available in bmc bioinformatics 81. Stat 375 mathematical statistics cprs, math, and stat. Statistical inference in twostage online controlled.
Efron and hastie demonstrate the evergrowing power of statistical reasoning, past, present, and future. This book takes a careful look at both the promise and pitfalls of largescale statistical inference, with particular attention to false discovery rates, the most successful of the new statistical techniques. We live in a new age for statistical inference, where modern scientific technology such as microarrays and fmri machines. In addition, technical noise and outliers can make it difficult to gain access to the true biological signal of the. This poses new dif culties for the statistician, but also opens new opportunities. All text used in the lessons is provided at the back of this lesson set. We live in a new age for statistical inference, where modern scientific technology such as microarrays and fmri machines routinely produce. Prior robust empirical bayes inference for largescale. As a result, the book is centred on mathematical statistics and is more technical. Computer age statistical inference top results of your surfing computer age statistical inference start download portable document format pdf and ebooks electronic books free online rating news 20162017 is books that can provide inspiration, insight, knowledge to the reader. As the book progresses in its chronology, you will get some top. Starting down the road to largescale inference, suppose now we are dealing with.
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