andrew_gelman_stats andrew_gelman_stats-2010 andrew_gelman_stats-2010-62 knowledge-graph by maker-knowledge-mining
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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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1 Hierarchical Modeling and Computation: We are fitting hierarchical regression models with deep interactions. [sent-2, score-0.316]
2 We’re working on new models with structured prior distributions, and this also requires advances in Bayesian computation. [sent-3, score-0.216]
3 Applications include public opinion, climate reconstruction, and education research. [sent-4, score-0.194]
4 Postdoc #1 is funded by grants from the Department of Energy, Institute of Education Sciences, and National Science Foundation. [sent-5, score-0.214]
5 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. [sent-7, score-0.854]
6 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. [sent-8, score-0.673]
7 Project #2 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 set of proprietary data. [sent-9, score-0.913]
8 The postdoc will be expected to spend some time working directly with this firm, but it is fundamentally a research position. [sent-10, score-0.55]
9 Ideally, postdoc #1 will have a statistics or computer science background, will be interested in statistical modeling, serious programming, and applications. [sent-11, score-0.725]
10 Ideally, postdoc #2 will have a background in statistics, psychometrics, or economics and be interested in marketing or related topics. [sent-12, score-0.793]
11 Both postdocs should be able to work fluently in R and should already know about hierarchical models and Bayesian inference and computation. [sent-13, score-0.417]
12 Collaborators on Postdoc #1 include Jinchen Liu, Jennifer Hill, Matt Schofield, Upmanu Lall, Chad Scherrer, Alan Edelman, and Sophia Rabe-Hesketh. [sent-15, score-0.108]
13 If you’re interested in either, please send a letter of application, a c. [sent-17, score-0.09]
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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
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