andrew_gelman_stats andrew_gelman_stats-2014 andrew_gelman_stats-2014-2260 knowledge-graph by maker-knowledge-mining
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Introduction: Julie Josse sends along this job announcement: A post-doctoral position is available in the applied mathematics department of Agrocampus Rennes. The postdoc will be funded by the Henri Lebesgue Center (see http://www.lebesgue.fr/) if the application is selected. Applicants are expected to send their application before 31 March 2014. The research focus is on development of new methods to deal with missing values and their implementation in the free R software to make them available. We study new multiple imputation methods based on principal component methods. Different aspects are expected to be covered: dealing with missing values in multi-blocks, multi-groups data (groups of individuals and variables); regularization in this framework using a Bayesian approach, dealing with different types of data (continuous, categoricals, etc.). Fields of application are wide and include biological data as well as socio-economic data. Key words: missing values, matrix completion, PCA, B
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1 The postdoc will be funded by the Henri Lebesgue Center (see http://www. [sent-2, score-0.163]
2 Applicants are expected to send their application before 31 March 2014. [sent-5, score-0.397]
3 The research focus is on development of new methods to deal with missing values and their implementation in the free R software to make them available. [sent-6, score-0.614]
4 We study new multiple imputation methods based on principal component methods. [sent-7, score-0.421]
5 Different aspects are expected to be covered: dealing with missing values in multi-blocks, multi-groups data (groups of individuals and variables); regularization in this framework using a Bayesian approach, dealing with different types of data (continuous, categoricals, etc. [sent-8, score-0.853]
6 Fields of application are wide and include biological data as well as socio-economic data. [sent-10, score-0.339]
7 Key words: missing values, matrix completion, PCA, Bayesian PCA, dimensionality reduction methods, exploratory multivariate data analysis methods, correspondence analysis, multi-groups, multi-blocks methods, singular values thresholding, low rank approximation. [sent-11, score-1.065]
8 fr Applicants are requested to send CV and one letter of recommendation. [sent-14, score-0.291]
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Introduction: Julie Josse sends along this job announcement: A post-doctoral position is available in the applied mathematics department of Agrocampus Rennes. The postdoc will be funded by the Henri Lebesgue Center (see http://www.lebesgue.fr/) if the application is selected. Applicants are expected to send their application before 31 March 2014. The research focus is on development of new methods to deal with missing values and their implementation in the free R software to make them available. We study new multiple imputation methods based on principal component methods. Different aspects are expected to be covered: dealing with missing values in multi-blocks, multi-groups data (groups of individuals and variables); regularization in this framework using a Bayesian approach, dealing with different types of data (continuous, categoricals, etc.). Fields of application are wide and include biological data as well as socio-economic data. Key words: missing values, matrix completion, PCA, B
2 0.1464528 361 andrew gelman stats-2010-10-21-Tenure-track statistics job at Teachers College, here at Columbia!
Introduction: See below for the job announcement. It’s for Teachers College, which is about 2 blocks from the statistics department and 2 blocks from the political science department. So even though I don’t have any official connection with Teachers College (besides occasionally working with them on research projects), I very much would like to have another exciting young applied researcher here, to complement all the people we currently have in stat, poli sci, engineering, etc. In particular, we have zillions of interesting and important social science research projects going on here, and they all need statistics work. A lot of social scientists do statistics, but it’s not so easy to find a statistician who does serious social science research. All this is to say that I hope this job gets some applicants from some people who are serious about applied statistics and the development of new models and methods. Teachers College, Columbia University Department of Human Development APPLIE
Introduction: Liz Stuart sends this one along: Johns Hopkins University Post-Doctoral Fellow Opening The Department of Mental Health invites applications for a post-doctoral research fellow in Statistical Methods for Mental Health Research, to be supervised by Dr. Elizabeth Stuart. The successful applicant for this position will work on an NIH funded study to develop and evaluate propensity score methods when the covariates are measured with error, or when datasets contain similar, but not identical, measures of covariates of interest. The post-doc will work with a team of researchers on development of new statistical methods, analysis of existing data, Monte Carlo simulations, and will take the lead on scientific publications. Opportunities will be provided for training in causal inference, missing data, latent variable methods, the evaluation of statistical methods, and the application of statistics to mental health and education. The three particular motivating examples will be: earl
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Introduction: Andrew Gelman (Columbia University) and Jennifer Hill (New York University) seek to hire a post-doctoral fellow to work on development of iterative imputation algorithms and diagnostics for missing-data imputation. Activities would include model-development, programming, and data analysis. This project is funded by a grant from the Institute of Education Sciences. Other collaborators on the project include Jingchen Liu and Ben Goodrich. This is a two-year position that would start this summer (2011) or earlier if possible. The ideal candidate will have a statistics or computer science background, will be interested in statistical modeling, serious programming, and applications. He or she should be able to work fluently in R and should already know about hierarchical models and Bayesian inference and computation. Experience or interest in designing GUIs would be a bonus, since we are attempting to improve our GUI for missing data imputation. The successful candidate will beco
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Introduction: Postdoc #1. Hierarchical Modeling and Computation: We are fitting hierarchical regression models with deep interactions. We’re working on new models with structured prior distributions, and this also requires advances in Bayesian computation. Applications include public opinion, climate reconstruction, and education research. Postdoc #1 is funded by grants from the Department of Energy, Institute of Education Sciences, and National Science Foundation. Postdoc #2. Hierarchical Modeling and Statistical Graphics: The goal of this research program is to investigate the application of the latest methods of multi-level data analysis, visualization and regression modeling to an important commercial problem: forecasting retail sales at the individual item level. These forecasts are used to make ordering, pricing and promotions decisions which can have significant economic impact to the retail chain such that even modest improvements in the accuracy of predictions, across a large retailer’s
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same-blog 1 0.95544577 2260 andrew gelman stats-2014-03-22-Postdoc at Rennes on multilevel missing data imputation
Introduction: Julie Josse sends along this job announcement: A post-doctoral position is available in the applied mathematics department of Agrocampus Rennes. The postdoc will be funded by the Henri Lebesgue Center (see http://www.lebesgue.fr/) if the application is selected. Applicants are expected to send their application before 31 March 2014. The research focus is on development of new methods to deal with missing values and their implementation in the free R software to make them available. We study new multiple imputation methods based on principal component methods. Different aspects are expected to be covered: dealing with missing values in multi-blocks, multi-groups data (groups of individuals and variables); regularization in this framework using a Bayesian approach, dealing with different types of data (continuous, categoricals, etc.). Fields of application are wide and include biological data as well as socio-economic data. Key words: missing values, matrix completion, PCA, B
Introduction: Liz Stuart sends this one along: Johns Hopkins University Post-Doctoral Fellow Opening The Department of Mental Health invites applications for a post-doctoral research fellow in Statistical Methods for Mental Health Research, to be supervised by Dr. Elizabeth Stuart. The successful applicant for this position will work on an NIH funded study to develop and evaluate propensity score methods when the covariates are measured with error, or when datasets contain similar, but not identical, measures of covariates of interest. The post-doc will work with a team of researchers on development of new statistical methods, analysis of existing data, Monte Carlo simulations, and will take the lead on scientific publications. Opportunities will be provided for training in causal inference, missing data, latent variable methods, the evaluation of statistical methods, and the application of statistics to mental health and education. The three particular motivating examples will be: earl
Introduction: The Division of Biostatistics and Epidemiology at Weill Medical College of Cornell University invite applications for a post-doctoral fellow position in Biostatistics. We are seeking a highly motivated individual to develop novel statistical methods for missing data imputation and applications in health disparities research using national administrative data, with funding from Agency of Healthcare Research and Quality. The candidate will be jointly mentored by Dr. Yan Ma (PI of the grant) at the Hospital for Special Surgery (HSS)/Weill Cornell Medical College (WCMC) and Dr. Andrew Gelman in the Department of Statistics at Columbia University. The candidate will also have excellent opportunities to collaborate with researchers in biostatistics, epidemiology, health disparities, and orthopedics through the Clinical and Translational Science Center at WCMC. Candidates should have a doctoral degree in Statistics or Biostatistics with strong background in Bayesian methods. Experien
4 0.79029351 538 andrew gelman stats-2011-01-25-Postdoc Position #2: Hierarchical Modeling and Statistical Graphics
Introduction: Andrew Gelman (Columbia University) and Eric Johnson (Columbia University) seek to hire a post-doctoral fellow to work on the application of the latest methods of multilevel data analysis, visualization and regression modeling to an important commercial problem: forecasting retail sales at the individual item level. These forecasts are used to make ordering, pricing and promotions decisions which can have significant economic impact to the retail chain such that even modest improvements in the accuracy of predictions, across a large retailer’s product line, can yield substantial margin improvements. Activities focus on the development of iterative imputation algorithms and diagnostics for missing-data imputation. Activities would include model-development, programming, and data analysis. This project is to be undertaken with, and largely funded by, a firm which provides forecasting technology and services to large retail chains, and which will provide access to a unique and rich
5 0.78180021 1110 andrew gelman stats-2012-01-10-Jobs in statistics research! In New Jersey!
Introduction: Kenny writes: The Statistics Research group in AT&T; Labs invites applications for full time research positions. Applicants should have a Ph.D. in Statistics (or a related field), and be able to make major, widely-recognized contributions to statistics research: theory, methods, computing, and data analysis. Candidates must demonstrate a potential for excellence in research, a knowledge of fundamental statistical theory, a collaborative spirit, and strong communication skills. We are especially interested in statisticians who are interested in and capable of working on large scale data problems. A non-exclusive list of statistical fields we are interested in include: data mining, statistical computing, forecasting, time series, spatial statistics, social networks, machine learning, and Bayesian analysis. We invite applications from both new and experienced Ph.Ds, and women and underrepresented minorities are especially encouraged to apply. AT&T; Companies are Equal Opportunity Emp
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1 0.97048569 873 andrew gelman stats-2011-08-26-Luck or knowledge?
Introduction: Joan Ginther has won the Texas lottery four times. First, she won $5.4 million, then a decade later, she won $2million, then two years later $3million and in the summer of 2010, she hit a $10million jackpot. The odds of this has been calculated at one in eighteen septillion and luck like this could only come once every quadrillion years. According to Forbes, the residents of Bishop, Texas, seem to believe God was behind it all. The Texas Lottery Commission told Mr Rich that Ms Ginther must have been ‘born under a lucky star’, and that they don’t suspect foul play. Harper’s reporter Nathanial Rich recently wrote an article about Ms Ginther, which calls the the validity of her ‘luck’ into question. First, he points out, Ms Ginther is a former math professor with a PhD from Stanford University specialising in statistics. More at Daily Mail. [Edited Saturday] In comments, C Ryan King points to the original article at Harper’s and Bill Jefferys to Wired .
2 0.96108526 253 andrew gelman stats-2010-09-03-Gladwell vs Pinker
Introduction: I just happened to notice this from last year. Eric Loken writes : Steven Pinker reviewed Malcolm Gladwell’s latest book and criticized him rather harshly for several shortcomings. Gladwell appears to have made things worse for himself in a letter to the editor of the NYT by defending a manifestly weak claim from one of his essays – the claim that NFL quarterback performance is unrelated to the order they were drafted out of college. The reason w [Loken and his colleagues] are implicated is that Pinker identified an earlier blog post of ours as one of three sources he used to challenge Gladwell (yay us!). But Gladwell either misrepresented or misunderstood our post in his response, and admonishes Pinker by saying “we should agree that our differences owe less to what can be found in the scientific literature than they do to what can be found on Google.” Well, here’s what you can find on Google. Follow this link to request the data for NFL quarterbacks drafted between 1980 and
3 0.95985216 1547 andrew gelman stats-2012-10-25-College football, voting, and the law of large numbers
Introduction: In an article provocatively entitled, “Will Ohio State’s football team decide who wins the White House?”, Tyler Cowen and Kevin Grier report : It is statistically possible that the outcome of a handful of college football games in the right battleground states could determine the race for the White House. Economists Andrew Healy, Neil Malhotra, and Cecilia Mo make this argument in a fascinating article in the Proceedings of the National Academy of Science. They examined whether the outcomes of college football games on the eve of elections for presidents, senators, and governors affected the choices voters made. They found that a win by the local team, in the week before an election, raises the vote going to the incumbent by around 1.5 percentage points. When it comes to the 20 highest attendance teams—big athletic programs like the University of Michigan, Oklahoma, and Southern Cal—a victory on the eve of an election pushes the vote for the incumbent up by 3 percentage points. T
4 0.95615828 1718 andrew gelman stats-2013-02-11-Toward a framework for automatic model building
Introduction: Patrick Caldon writes: I saw your recent blog post where you discussed in passing an iterative-chain-of models approach to AI. I essentially built such a thing for my PhD thesis – not in a Bayesian context, but in a logic programming context – and proved it had a few properties and showed how you could solve some toy problems. The important bit of my framework was that at various points you also go and get more data in the process – in a statistical context this might be seen as building a little univariate model on a subset of the data, then iteratively extending into a better model with more data and more independent variables – a generalized forward stepwise regression if you like. It wrapped a proper computational framework around E.M. Gold’s identification/learning in the limit based on a logic my advisor (Eric Martin) had invented. What’s not written up in the thesis is a few months of failed struggle trying to shoehorn some simple statistical inference into this
5 0.95407665 904 andrew gelman stats-2011-09-13-My wikipedia edit
Introduction: The other day someone mentioned my complaint about the Wikipedia article on “Bayesian inference” (see footnote 1 of this article ) and he said I should fix the Wikipedia entry myself. And so I did . I didn’t have the energy to rewrite the whole article–in particular, all of its examples involve discrete parameters, whereas the Bayesian problems I work on generally have continuous parameters, and its “mathematical foundations” section focuses on “independent identically distributed observations x” rather than data y which can have different distributions. It’s just a wacky, unbalanced article. But I altered the first few paragraphs to get rid of the stuff about the posterior probability that a model is true. I much prefer the Scholarpedia article on Bayesian statistics by David Spiegelhalter and Kenneth Rice, but I couldn’t bring myself to simply delete the Wikipedia article and replace it with the Scholarpedia content. Just to be clear: I’m not at all trying to disparage
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