**gelman.andrew**37

[1507.02646] Pareto Smoothed Importance Sampling

17 days ago by cshalizi

"Importance weighting is a general way to adjust Monte Carlo integration to account for draws from the wrong distribution, but the resulting estimate can be noisy when the importance ratios have a heavy right tail. This routinely occurs when there are aspects of the target distribution that are not well captured by the approximating distribution, in which case more stable estimates can be obtained by modifying extreme importance ratios. We present a new method for stabilizing importance weights using a generalized Pareto distribution fit to the upper tail of the distribution of the simulated importance ratios. The method, which empirically performs better than existing methods for stabilizing importance sampling estimates, includes stabilized effective sample size estimates, Monte Carlo error estimates and convergence diagnostics."

to:NB
monte_carlo
importance_sampling
heavy_tails
computational_statistics
statistics
re:fitness_sampling
gelman.andrew
17 days ago by cshalizi

When the Revolution Came for Amy Cuddy - The New York Times

january 2018 by cshalizi

Morals under the "to teach" tag:

1. Don't do science like this.

2. Don't be a jerk when criticizing others for doing bad science.

(I realize that I am one to talk about #2.)

have_read
social_science_methodology
social_psychology
psychology
replication_crisis
gelman.andrew
popular_social_science
data_analysis
to_teach:undergrad-research
1. Don't do science like this.

2. Don't be a jerk when criticizing others for doing bad science.

(I realize that I am one to talk about #2.)

january 2018 by cshalizi

Red State/Blue State Divisions in the 2012 Presidential Election

march 2014 by resteorts

"The so-called “red/blue paradox” is that rich individuals are more likely to vote Republican but rich states are more likely to support the Democrats. Previ- ous research argued that this seeming paradox could be explained by comparing rich and poor voters within each state – the difference in the Republican vote share between rich and poor voters was much larger in low-income, con- servative, middle-American states like Mississippi than in high-income, liberal, coastal states like Connecticut. We use exit poll and other survey data to assess whether this was still the case for the 2012 Presidential election. Based on this preliminary analysis, we find that, while the red/ blue paradox is still strong, the explanation offered by Gel- man et al. no longer appears to hold. We explore several empirical patterns from this election and suggest possible avenues for resolving the questions posed by the new data."

to:NB
have_read
us_politics
statistics
to_teach:undergrad-ADA
to_teach:statcomp
kith_and_kin
gelman.andrew
via:cshalizi
march 2014 by resteorts

Red State/Blue State Divisions in the 2012 Presidential Election

july 2013 by cshalizi

"The so-called “red/blue paradox” is that rich individuals are more likely to vote Republican but rich states are more likely to support the Democrats. Previ- ous research argued that this seeming paradox could be explained by comparing rich and poor voters within each state – the difference in the Republican vote share between rich and poor voters was much larger in low-income, con- servative, middle-American states like Mississippi than in high-income, liberal, coastal states like Connecticut. We use exit poll and other survey data to assess whether this was still the case for the 2012 Presidential election. Based on this preliminary analysis, we find that, while the red/ blue paradox is still strong, the explanation offered by Gel- man et al. no longer appears to hold. We explore several empirical patterns from this election and suggest possible avenues for resolving the questions posed by the new data."

to:NB
have_read
us_politics
statistics
to_teach:undergrad-ADA
to_teach:statcomp
kith_and_kin
gelman.andrew
july 2013 by cshalizi

Taylor & Francis Online :: Infovis and Statistical Graphics: Different Goals, Different Looks - Journal of Computational and Graphical Statistics - Volume 22, Issue 1

march 2013 by cshalizi

"The importance of graphical displays in statistical practice has been recognized sporadically in the statistical literature over the past century, with wider awareness following Tukey's Exploratory Data Analysis and Tufte's books in the succeeding decades. But statistical graphics still occupy an awkward in-between position: within statistics, exploratory and graphical methods represent a minor subfield and are not well integrated with larger themes of modeling and inference. Outside of statistics, infographics (also called information visualization or Infovis) are huge, but their purveyors and enthusiasts appear largely to be uninterested in statistical principles.

"We present here a set of goals for graphical displays discussed primarily from the statistical point of view and discuss some inherent contradictions in these goals that may be impeding communication between the fields of statistics and Infovis. One of our constructive suggestions, to Infovis practitioners and statisticians alike, is to try not to cram into a single graph what can be better displayed in two or more. We recognize that we offer only one perspective and intend this article to be a starting point for a wide-ranging discussion among graphic designers, statisticians, and users of statistical methods. The purpose of this article is not to criticize but to explore the different goals that lead researchers in different fields to value different aspects of data visualization."

--- The comment by Wickham looks especially useful.

to:NB
visual_display_of_quantitative_information
statistics
gelman.andrew
"We present here a set of goals for graphical displays discussed primarily from the statistical point of view and discuss some inherent contradictions in these goals that may be impeding communication between the fields of statistics and Infovis. One of our constructive suggestions, to Infovis practitioners and statisticians alike, is to try not to cram into a single graph what can be better displayed in two or more. We recognize that we offer only one perspective and intend this article to be a starting point for a wide-ranging discussion among graphic designers, statisticians, and users of statistical methods. The purpose of this article is not to criticize but to explore the different goals that lead researchers in different fields to value different aspects of data visualization."

--- The comment by Wickham looks especially useful.

march 2013 by cshalizi

Everyone’s trading bias for variance at some point, it’s just done at different places in the analyses « Statistical Modeling, Causal Inference, and Social Science

march 2013 by cshalizi

"Some things I respect

"When it comes to meta-models of statistics, here are two philosophies that I respect:

"1. (My) Bayesian approach, which I associate with E. T. Jaynes, in which you construct models with strong assumptions, ride your models hard, check their fit to data, and then scrap them and improve them as necessary.

"2. At the other extreme, model-free statistical procedures that are designed to work well under very weak assumptions—for example, instead of assuming a distribution is Gaussian, you would just want the procedure to work well under some conditions on the smoothness of the second derivative of the log density function.

"Both the above philosophies recognize that (almost) all important assumptions will be wrong, and they resolve this concern via aggressive model checking or via robustness. And of course there are intermediate positions, such as working with Bayesian models that have been shown to be robust, and then still checking them. Or, to flip it around, using robust methods and checking their implicit assumptions.

"I don’t like these

"The statistical philosophies I don’t like so much are those that make strong assumptions with no checking and no robustness. For example, the purely subjective Bayes approach in which it’s illegal to check the fit of a model because it’a supposed to represent your personal belief. I’ve always thought this was ridiculous, first because personal beliefs should be checked where possible, second because it’s hard for me to believe that all these analysts happen to be using logistic regression, normal distributions, and all the other standard tools, out of personal belief. Or the likelihood approach, advocated by those people who refuse to make any assumptions or restrictions on parameters but are willing to rely 100% on the normal distributions, logistic regressions, etc., that they pull out of the toolbox."

to_teach:undergrad-ADA
statistics
foundations_of_statistics
gelman.andrew
"When it comes to meta-models of statistics, here are two philosophies that I respect:

"1. (My) Bayesian approach, which I associate with E. T. Jaynes, in which you construct models with strong assumptions, ride your models hard, check their fit to data, and then scrap them and improve them as necessary.

"2. At the other extreme, model-free statistical procedures that are designed to work well under very weak assumptions—for example, instead of assuming a distribution is Gaussian, you would just want the procedure to work well under some conditions on the smoothness of the second derivative of the log density function.

"Both the above philosophies recognize that (almost) all important assumptions will be wrong, and they resolve this concern via aggressive model checking or via robustness. And of course there are intermediate positions, such as working with Bayesian models that have been shown to be robust, and then still checking them. Or, to flip it around, using robust methods and checking their implicit assumptions.

"I don’t like these

"The statistical philosophies I don’t like so much are those that make strong assumptions with no checking and no robustness. For example, the purely subjective Bayes approach in which it’s illegal to check the fit of a model because it’a supposed to represent your personal belief. I’ve always thought this was ridiculous, first because personal beliefs should be checked where possible, second because it’s hard for me to believe that all these analysts happen to be using logistic regression, normal distributions, and all the other standard tools, out of personal belief. Or the likelihood approach, advocated by those people who refuse to make any assumptions or restrictions on parameters but are willing to rely 100% on the normal distributions, logistic regressions, etc., that they pull out of the toolbox."

march 2013 by cshalizi

[1302.2142] Simulation-efficient shortest probability intervals

february 2013 by cshalizi

"Bayesian highest posterior density (HPD) intervals can be estimated directly from simulations via empirical shortest intervals. Unfortunately, these can be noisy (that is, have a high Monte Carlo error). We derive an optimal weighting strategy using bootstrap and quadratic programming to obtain a more compu- tationally stable HPD, or in general, shortest probability interval (Spin). We prove the consistency of our method. Simulation studies on a range of theoret- ical and real-data examples, some with symmetric and some with asymmetric posterior densities, show that intervals constructed using Spin have better cov- erage (relative to the posterior distribution) and lower Monte Carlo error than empirical shortest intervals. We implement the new method in an R package (SPIn) so it can be routinely used in post-processing of Bayesian simulations."

in_NB
confidence_sets
monte_carlo
kith_and_kin
gelman.andrew
february 2013 by cshalizi

What Too Close to Call Really Means - NYTimes.com

november 2012 by jimmykduong

"A political statistician explains why it's hard to get our heads around the presidential polls."

op-ed
politics
political.science
gelman.andrew
statistics
november 2012 by jimmykduong

Lamentably common misunderstanding of meritocracy « Statistical Modeling, Causal Inference, and Social Science

december 2011 by cshalizi

Economics as right-wing ideology, iteration #5,678,183 in a series of aleph-null.

meritocracy
gives_economists_a_bad_name
moral_philosophy
zingales.luigi
gelman.andrew
evisceration
running_dogs_of_reaction
to:blog
december 2011 by cshalizi

Why we (usually) don’t have to worry about multiple comparison

november 2011 by edanielson

Abstract: "Applied researchers often find themselves making statistical inferences in settings that would seem to require multiple comparisons adjustments. We challenge the Type I error paradigm that underlies these corrections. Moreover we posit that the problem of multiple comparisons can disappear entirely when viewed from a hierarchical Bayesian perspective. We propose building multilevel models in the settings where multiple comparisons arise.

Multilevel models perform partial pooling (shifting estimates toward each other), whereas classical procedures typically keep the centers of intervals stationary, adjusting for multiple comparisons by making the intervals wider (or, equivalently, adjusting the p-values corresponding to intervals of fixed width). Thus, multilevel models address the multiple comparisons problem and also yield more efficient estimates, especially in settings with low group-level variation, which is where multiple comparisons are a particular concern."

Gelman.Andrew
bayesian_inference
hierarchical_modeling
multiple_comparisons
Type_S_error
statistical_significance
Multilevel models perform partial pooling (shifting estimates toward each other), whereas classical procedures typically keep the centers of intervals stationary, adjusting for multiple comparisons by making the intervals wider (or, equivalently, adjusting the p-values corresponding to intervals of fixed width). Thus, multilevel models address the multiple comparisons problem and also yield more efficient estimates, especially in settings with low group-level variation, which is where multiple comparisons are a particular concern."

november 2011 by edanielson

Even the liberal New Republic opposes Occupy Wall Street: What does that mean? — The Monkey Cage

october 2011 by cshalizi

Why, at this late date, would anyone think that the New Republic _wants_ to advance liberal goals? (Which is Andrew's point, I think, when one unwraps all the nested ironies.)

even_the_liberal_new_republic
us_politics
occupy_wall_street
gelman.andrew
october 2011 by cshalizi

“One of the easiest ways to differentiate an economist from almost anyone else in society” « Statistical Modeling, Causal Inference, and Social Science

july 2011 by cshalizi

This is one of the few convincing deconstructions I have ever seen. (Andrew would not of course call it "deconstruction".)

rhetoric
economics
rhetorical_self-fashioning
gelman.andrew
to:blog
july 2011 by cshalizi

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