andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2298 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: Mon : Ticket to Baaaath Tues : Ticket to Baaaaarf Wed : Thinking of doing a list experiment? Here’s a list of reasons why you should think again Thurs : An open site for researchers to post and share papers Fri : Questions about “Too Good to Be True” Sat : Sleazy sock puppet can’t stop spamming our discussion of compressed sensing and promoting the work of Xiteng Liu Sun : White stripes and dead armadillos
sentIndex sentText sentNum sentScore
1 Mon : Ticket to Baaaath Tues : Ticket to Baaaaarf Wed : Thinking of doing a list experiment? [sent-1, score-0.185]
wordName wordTfidf (topN-words)
[('ticket', 0.323), ('armadillos', 0.234), ('baaaaarf', 0.234), ('baaaath', 0.234), ('stripes', 0.234), ('puppet', 0.221), ('xiteng', 0.221), ('spamming', 0.204), ('sock', 0.198), ('sensing', 0.193), ('sleazy', 0.188), ('list', 0.185), ('compressed', 0.181), ('liu', 0.165), ('promoting', 0.163), ('sun', 0.163), ('fri', 0.16), ('mon', 0.156), ('tues', 0.156), ('thurs', 0.152), ('wed', 0.152), ('dead', 0.14), ('sat', 0.129), ('site', 0.122), ('white', 0.115), ('stop', 0.114), ('experiment', 0.107), ('share', 0.106), ('reasons', 0.097), ('open', 0.092), ('papers', 0.08), ('questions', 0.078), ('true', 0.072), ('researchers', 0.071), ('thinking', 0.071), ('post', 0.06), ('discussion', 0.057), ('work', 0.04), ('good', 0.039), ('think', 0.025)]
simIndex simValue blogId blogTitle
same-blog 1 0.99999976 2298 andrew gelman stats-2014-04-21-On deck this week
Introduction: Mon : Ticket to Baaaath Tues : Ticket to Baaaaarf Wed : Thinking of doing a list experiment? Here’s a list of reasons why you should think again Thurs : An open site for researchers to post and share papers Fri : Questions about “Too Good to Be True” Sat : Sleazy sock puppet can’t stop spamming our discussion of compressed sensing and promoting the work of Xiteng Liu Sun : White stripes and dead armadillos
2 0.37705636 2264 andrew gelman stats-2014-03-24-On deck this month
Introduction: Actually, more like the next month and a half . . . I just have this long backlog so I thought I might as well share it with you: Empirical implications of Empirical Implications of Theoretical Models A statistical graphics course and statistical graphics advice What property is important in a risk prediction model? Discrimination or calibration? Beyond the Valley of the Trolls Science tells us that fast food lovers are more likely to marry other fast food lovers References (with code) for Bayesian hierarchical (multilevel) modeling and structural equation modeling Adjudicating between alternative interpretations of a statistical interaction? The most-cited statistics papers ever American Psychological Society announces a new journal Am I too negative? As the boldest experiment in journalism history, you admit you made a mistake Personally, I’d rather go with Teragram Bizarre academic spam An old discussion of food deserts Skepticism about a published cl
Introduction: Some asshole who has a bug up his ass about compressed sensing is spamming our comments with a bunch of sock puppets. All from the same IP address: “George Stoneriver,” Scott Wolfe,” and just plain “Paul,” all saying pretty much the same thing in the same sort of broken English (except for Paul, whose post was too short to do a dialect analysis). “Scott Wolfe” is a generic sort of name, but a quick google search reveals nothing related to this topic. “George Stoneriver” seems to have no internet presence at all (besides the comments at this blog). As for “Paul,” I don’t know, maybe the spammer was too lazy to invent a last name? Our spammer spends about half his time slamming the field of compressed sensing and the other half pumping up the work of someone named Xiteng Liu. There’s no excuse for this behavior. It’s horrible, a true abuse of our scholarly community. If Scott Adams wants to use a sock puppet, fine, the guy’s an artist and we should cut him some slack. If tha
4 0.23852827 2366 andrew gelman stats-2014-06-09-On deck this week
Introduction: Mon: I hate polynomials Tues: Spring forward, fall back, drop dead? Wed: Bayes in the research conversation Thurs: The health policy innovation center: how best to move from pilot studies to large-scale practice? Fri: Stroopy names Sat: He’s not so great in math but wants to do statistics and machine learning Sun: Comparing the full model to the partial model
5 0.23652185 2240 andrew gelman stats-2014-03-10-On deck this week: Things people sent me
Introduction: Mon: Preregistration: what’s in it for you? Tues: What if I were to stop publishing in journals? Wed: Empirical implications of Empirical Implications of Theoretical Models Thurs: An Economist’s Guide to Visualizing Data Fri: The maximal information coefficient Sat: Problematic interpretations of confidence intervals Sun: The more you look, the more you find
6 0.22305441 2290 andrew gelman stats-2014-04-14-On deck this week
7 0.21707264 2276 andrew gelman stats-2014-03-31-On deck this week
8 0.1987592 2348 andrew gelman stats-2014-05-26-On deck this week
9 0.1969624 2300 andrew gelman stats-2014-04-21-Ticket to Baaaath
10 0.19513036 2321 andrew gelman stats-2014-05-05-On deck this week
11 0.18022858 2331 andrew gelman stats-2014-05-12-On deck this week
12 0.17378135 2339 andrew gelman stats-2014-05-19-On deck this week
13 0.16626191 2206 andrew gelman stats-2014-02-10-On deck this week
14 0.16255754 2253 andrew gelman stats-2014-03-17-On deck this week: Revisitings
15 0.16011439 2356 andrew gelman stats-2014-06-02-On deck this week
16 0.15713707 2222 andrew gelman stats-2014-02-24-On deck this week
17 0.15405181 2285 andrew gelman stats-2014-04-07-On deck this week
18 0.1470134 2079 andrew gelman stats-2013-10-27-Uncompressing the concept of compressed sensing
19 0.14202394 2310 andrew gelman stats-2014-04-28-On deck this week
20 0.12875929 2214 andrew gelman stats-2014-02-17-On deck this week
topicId topicWeight
[(0, 0.057), (1, -0.026), (2, -0.043), (3, -0.008), (4, 0.004), (5, 0.016), (6, -0.039), (7, -0.068), (8, 0.017), (9, -0.094), (10, -0.054), (11, 0.292), (12, 0.119), (13, 0.221), (14, -0.02), (15, -0.01), (16, 0.044), (17, 0.009), (18, 0.055), (19, -0.037), (20, -0.018), (21, 0.031), (22, 0.05), (23, 0.044), (24, 0.026), (25, -0.058), (26, 0.045), (27, 0.058), (28, -0.037), (29, 0.045), (30, 0.004), (31, -0.031), (32, 0.052), (33, 0.03), (34, -0.047), (35, -0.059), (36, -0.004), (37, -0.012), (38, -0.035), (39, 0.022), (40, 0.044), (41, -0.03), (42, -0.015), (43, 0.044), (44, 0.009), (45, -0.004), (46, 0.019), (47, -0.012), (48, 0.006), (49, 0.012)]
simIndex simValue blogId blogTitle
same-blog 1 0.98141432 2298 andrew gelman stats-2014-04-21-On deck this week
Introduction: Mon : Ticket to Baaaath Tues : Ticket to Baaaaarf Wed : Thinking of doing a list experiment? Here’s a list of reasons why you should think again Thurs : An open site for researchers to post and share papers Fri : Questions about “Too Good to Be True” Sat : Sleazy sock puppet can’t stop spamming our discussion of compressed sensing and promoting the work of Xiteng Liu Sun : White stripes and dead armadillos
2 0.88451952 2276 andrew gelman stats-2014-03-31-On deck this week
Introduction: Mon : The most-cited statistics papers ever Tues : American Psychological Society announces a new journal Wed : Am I too negative? Thurs : As the boldest experiment in journalism history, you admit you made a mistake Fri : The Notorious N.H.S.T. presents: Mo P-values Mo Problems Sat : Bizarre academic spam Sun : An old discussion of food deserts
3 0.8693049 2290 andrew gelman stats-2014-04-14-On deck this week
Introduction: Mon : Transitioning to Stan Tues : When you believe in things that you don’t understand Wed : Looking for Bayesian expertise in India, for the purpose of analysis of sarcoma trials Thurs : If you get to the point of asking, just do it. But some difficulties do arise . . . Fri : One-tailed or two-tailed? Sat : Index or indicator variables Sun : Fooled by randomness
4 0.86320078 2240 andrew gelman stats-2014-03-10-On deck this week: Things people sent me
Introduction: Mon: Preregistration: what’s in it for you? Tues: What if I were to stop publishing in journals? Wed: Empirical implications of Empirical Implications of Theoretical Models Thurs: An Economist’s Guide to Visualizing Data Fri: The maximal information coefficient Sat: Problematic interpretations of confidence intervals Sun: The more you look, the more you find
5 0.85550016 2310 andrew gelman stats-2014-04-28-On deck this week
Introduction: Mon : Crowdstorming a dataset Tues : Ken Rice presents a unifying approach to statistical inference and hypothesis testing Wed : The health policy innovation center: how best to move from pilot studies to large-scale practice? Thurs : Heller, Heller, and Gorfine on univariate and multivariate information measures Fri : Discovering general multidimensional associations Sat : “The graph clearly shows that mammography adds virtually nothing to survival and if anything, decreases survival (and increases cost and provides unnecessary treatment)” Sun : Honored oldsters write about statistics
6 0.8024022 2253 andrew gelman stats-2014-03-17-On deck this week: Revisitings
7 0.79694289 2366 andrew gelman stats-2014-06-09-On deck this week
8 0.7923373 2348 andrew gelman stats-2014-05-26-On deck this week
9 0.78924388 2331 andrew gelman stats-2014-05-12-On deck this week
10 0.78166437 2206 andrew gelman stats-2014-02-10-On deck this week
11 0.76629686 2356 andrew gelman stats-2014-06-02-On deck this week
12 0.75169009 2285 andrew gelman stats-2014-04-07-On deck this week
13 0.74132335 2214 andrew gelman stats-2014-02-17-On deck this week
14 0.69270879 2265 andrew gelman stats-2014-03-24-On deck this week
15 0.68929237 2321 andrew gelman stats-2014-05-05-On deck this week
16 0.68699712 2339 andrew gelman stats-2014-05-19-On deck this week
17 0.63770086 2264 andrew gelman stats-2014-03-24-On deck this month
19 0.61482257 2222 andrew gelman stats-2014-02-24-On deck this week
20 0.52800852 679 andrew gelman stats-2011-04-25-My talk at Stanford on Tuesday
topicId topicWeight
[(9, 0.032), (13, 0.029), (21, 0.326), (24, 0.115), (43, 0.025), (47, 0.044), (62, 0.03), (71, 0.047), (74, 0.026), (85, 0.049), (89, 0.026), (98, 0.025), (99, 0.096)]
simIndex simValue blogId blogTitle
same-blog 1 0.93698567 2298 andrew gelman stats-2014-04-21-On deck this week
Introduction: Mon : Ticket to Baaaath Tues : Ticket to Baaaaarf Wed : Thinking of doing a list experiment? Here’s a list of reasons why you should think again Thurs : An open site for researchers to post and share papers Fri : Questions about “Too Good to Be True” Sat : Sleazy sock puppet can’t stop spamming our discussion of compressed sensing and promoting the work of Xiteng Liu Sun : White stripes and dead armadillos
2 0.9290297 1232 andrew gelman stats-2012-03-27-Banned in NYC school tests
Introduction: The list includes “hunting” but not “fishing,” so that’s cool. I wonder how they’d feel about a question involving different cuts of meat. In any case, I’m happy to see that “Bayes” is not on the banned list. P.S. Russell explains .
3 0.86873764 672 andrew gelman stats-2011-04-20-The R code for those time-use graphs
Introduction: By popular demand, here’s my R script for the time-use graphs : # The data a1 <- c(4.2,3.2,11.1,1.3,2.2,2.0) a2 <- c(3.9,3.2,10.0,0.8,3.1,3.1) a3 <- c(6.3,2.5,9.8,0.9,2.2,2.4) a4 <- c(4.4,3.1,9.8,0.8,3.3,2.7) a5 <- c(4.8,3.0,9.9,0.7,3.3,2.4) a6 <- c(4.0,3.4,10.5,0.7,3.3,2.1) a <- rbind(a1,a2,a3,a4,a5,a6) avg <- colMeans (a) avg.array <- t (array (avg, rev(dim(a)))) diff <- a - avg.array country.name <- c("France", "Germany", "Japan", "Britain", "USA", "Turkey") # The line plots par (mfrow=c(2,3), mar=c(4,4,2,.5), mgp=c(2,.7,0), tck=-.02, oma=c(3,0,4,0), bg="gray96", fg="gray30") for (i in 1:6){ plot (c(1,6), c(-1,1.7), xlab="", ylab="", xaxt="n", yaxt="n", bty="l", type="n") lines (1:6, diff[i,], col="blue") points (1:6, diff[i,], pch=19, col="black") if (i>3){ axis (1, c(1,3,5), c ("Work,\nstudy", "Eat,\nsleep", "Leisure"), mgp=c(2,1.5,0), tck=0, cex.axis=1.2) axis (1, c(2,4,6), c ("Unpaid\nwork", "Personal\nCare", "Other"), mgp=c(2,1.5,0),
4 0.78605771 151 andrew gelman stats-2010-07-16-Wanted: Probability distributions for rank orderings
Introduction: Dietrich Stoyan writes: I asked the IMS people for an expert in statistics of voting/elections and they wrote me your name. I am a statistician, but never worked in the field voting/elections. It was my son-in-law who asked me for statistical theories in that field. He posed in particular the following problem: The aim of the voting is to come to a ranking of c candidates. Every vote is a permutation of these c candidates. The problem is to have probability distributions in the set of all permutations of c elements. Are there theories for such distributions? I should be very grateful for a fast answer with hints to literature. (I confess that I do not know your books.) My reply: Rather than trying to model the ranks directly, I’d recommend modeling a latent continuous outcome which then implies a distribution on ranks, if the ranks are of interest. There are lots of distributions of c-dimensional continuous outcomes. In political science, the usual way to start is
Introduction: A tall thin young man came to my office today to talk about one of my current pet topics: stories and social science. I brought up Tom Wolfe and his goal of compressing an entire city into a single novel, and how this reminded me of the psychologists Kahneman and Tversky’s concept of “the law of small numbers,” the idea that we expect any small sample to replicate all the properties of the larger population that it represents. Strictly speaking, the law of small numbers is impossible—any small sample necessarily has its own unique features—but this is even more true if we consider network properties. The average American knows about 700 people (depending on how you define “know”) and this defines a social network over the population. Now suppose you look at a few hundred people and all their connections. This mini-network will almost necessarily look much much sparser than the national network, as we’re removing the connections to the people not in the sample. Now consider how
6 0.71877933 432 andrew gelman stats-2010-11-27-Neumann update
7 0.71329594 1401 andrew gelman stats-2012-06-30-David Hogg on statistics
8 0.70999616 894 andrew gelman stats-2011-09-07-Hipmunk FAIL: Graphics without content is not enough
9 0.69763231 2272 andrew gelman stats-2014-03-29-I agree with this comment
11 0.68509901 1275 andrew gelman stats-2012-04-22-Please stop me before I barf again
12 0.68476152 62 andrew gelman stats-2010-06-01-Two Postdoc Positions Available on Bayesian Hierarchical Modeling
13 0.67754197 854 andrew gelman stats-2011-08-15-A silly paper that tries to make fun of multilevel models
14 0.67694372 1857 andrew gelman stats-2013-05-15-Does quantum uncertainty have a place in everyday applied statistics?
15 0.67508769 1675 andrew gelman stats-2013-01-15-“10 Things You Need to Know About Causal Effects”
16 0.65479594 514 andrew gelman stats-2011-01-13-News coverage of statistical issues…how did I do?
18 0.64969337 1049 andrew gelman stats-2011-12-09-Today in the sister blog
20 0.6207276 900 andrew gelman stats-2011-09-11-Symptomatic innumeracy