andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2185 knowledge-graph by maker-knowledge-mining
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Introduction: I pointed Xihong Lin to this post from last month regarding Hastie and Tibshirani’s “bet on sparsity principle.” I argued that, in the worlds in which I work, in social and environmental science, every contrast is meaningful, even if not all of them can be distinguished from noise given a particular dataset. That is, I claim that effects are dense but data can be sparse—and any apparent sparsity of effects is typically just an artifact of sparsity of data. But things might be different in other fields. Xihong had an interesting perspective in the application areas where she works: Sparsity and density both appear in genetic studies too. For example, ethnicity has effects across millions of genetic variants across the genome (dense). Disease associated genetic variants are sparse.
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1 I pointed Xihong Lin to this post from last month regarding Hastie and Tibshirani’s “bet on sparsity principle. [sent-1, score-0.808]
2 ” I argued that, in the worlds in which I work, in social and environmental science, every contrast is meaningful, even if not all of them can be distinguished from noise given a particular dataset. [sent-2, score-0.741]
3 That is, I claim that effects are dense but data can be sparse—and any apparent sparsity of effects is typically just an artifact of sparsity of data. [sent-3, score-1.957]
4 Xihong had an interesting perspective in the application areas where she works: Sparsity and density both appear in genetic studies too. [sent-5, score-0.771]
5 For example, ethnicity has effects across millions of genetic variants across the genome (dense). [sent-6, score-1.284]
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same-blog 1 0.99999994 2185 andrew gelman stats-2014-01-25-Xihong Lin on sparsity and density
Introduction: I pointed Xihong Lin to this post from last month regarding Hastie and Tibshirani’s “bet on sparsity principle.” I argued that, in the worlds in which I work, in social and environmental science, every contrast is meaningful, even if not all of them can be distinguished from noise given a particular dataset. That is, I claim that effects are dense but data can be sparse—and any apparent sparsity of effects is typically just an artifact of sparsity of data. But things might be different in other fields. Xihong had an interesting perspective in the application areas where she works: Sparsity and density both appear in genetic studies too. For example, ethnicity has effects across millions of genetic variants across the genome (dense). Disease associated genetic variants are sparse.
2 0.43940607 2136 andrew gelman stats-2013-12-16-Whither the “bet on sparsity principle” in a nonsparse world?
Introduction: Rob Tibshirani writes : Hastie et al. (2001) coined the informal “Bet on Sparsity” principle. The l1 methods assume that the truth is sparse, in some basis. If the assumption holds true, then the parameters can be efficiently estimated using l1 penalties. If the assumption does not hold—so that the truth is dense—then no method will be able to recover the underlying model without a large amount of data per parameter. I’ve earlier expressed my full and sincere appreciation for Hastie and Tibshirani’s work in this area. Now I’d like to briefly comment on the above snippet. The question is, how do we think about the “bet on sparsity” principle in a world where the truth is dense? I’m thinking here of social science, where no effects are clean and no coefficient is zero (see page 960 of this article or various blog discussions in the past few years), where every contrast is meaningful—but some of these contrasts might be lost in the noise with any realistic size of data.
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Introduction: The new book titled: Past, Present, and Future of Statistical Science is now available for download . The official description makes the book sound pretty stuffy: Past, Present, and Future of Statistical Science, commissioned by the Committee of Presidents of Statistical Societies (COPSS) to celebrate its 50th anniversary and the International Year of Statistics, will be published in April by Taylor & Francis/CRC Press. Through the contributions of a distinguished group of 50 statisticians, the book showcases the breadth and vibrancy of statistics, describes current challenges and new opportunities, highlights the exciting future of statistical science, and provides guidance for future statisticians. Contributors are past COPSS award honorees. But it actually has lots of good stuff, including the chapter by Tibshirani which I discussed last year (in the context of the “bet on sparsity principle”), and chapters by XL and other fun people. Also my own chapter, How do we choo
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Introduction: I pointed Xihong Lin to this post from last month regarding Hastie and Tibshirani’s “bet on sparsity principle.” I argued that, in the worlds in which I work, in social and environmental science, every contrast is meaningful, even if not all of them can be distinguished from noise given a particular dataset. That is, I claim that effects are dense but data can be sparse—and any apparent sparsity of effects is typically just an artifact of sparsity of data. But things might be different in other fields. Xihong had an interesting perspective in the application areas where she works: Sparsity and density both appear in genetic studies too. For example, ethnicity has effects across millions of genetic variants across the genome (dense). Disease associated genetic variants are sparse.
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Introduction: I’m sorry I don’t have any new zombie papers in time for Halloween. Instead I’d like to be a little monster by reproducing a mini-rant from this article on experimental reasoning in social science: I will restrict my discussion to social science examples. Social scientists are often tempted to illustrate their ideas with examples from medical research. When it comes to medicine, though, we are, with rare exceptions, at best ignorant laypersons (in my case, not even reaching that level), and it is my impression that by reaching for medical analogies we are implicitly trying to borrow some of the scientific and cultural authority of that field for our own purposes. Evidence-based medicine is the subject of a large literature of its own (see, for example, Lau, Ioannidis, and Schmid, 1998).
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Introduction: John Cook and Joseph Delaney point to an article by Yurii Aulchenko et al., who write: 54 loci showing strong statistical evidence for association to human height were described, providing us with potential genomic means of human height prediction. In a population-based study of 5748 people, we find that a 54-loci genomic profile explained 4-6% of the sex- and age-adjusted height variance, and had limited ability to discriminate tall/short people. . . . In a family-based study of 550 people, with both parents having height measurements, we find that the Galtonian mid-parental prediction method explained 40% of the sex- and age-adjusted height variance, and showed high discriminative accuracy. . . . The message is that the simple approach of predicting child’s height using a regression model given parents’ average height performs much better than the method they have based on combining 54 genes. They also find that, if you start with the prediction based on parents’ heigh
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