Toward a Common Framework for Statistical Analysis and Development

By Kosuke Imai, Gary King, Olivia Lau. 2008

367 citations recorded by the source corpus.

We develop a general ontology of statistical methods and use it to propose a common framework for statistical analysis and software development built on and within the R language, including R's numerous existing packages. This framework offers a simple unified structure and syntax that can encompass a large fraction of existing statistical procedures. We conjecture that it can be used to encompass and present simply a vast majority of existing statistical methods, without requiring changes in existing approaches, and regardless of the theory of inference on which they are based, notation with which they were developed, and programming syntax with which they have been implemented. This development enabled us, and should enable others, to design statistical software with a single, simple, and unified user interface that helps overcome the conflicting notation, syntax, jargon, and statistical methods existing across the methods subfields of numerous academic disciplines. The approach also enables one to build a graphical user interface that automatically includes any method encompassed within the framework. We hope that the result of this line of research will greatly reduce the time from the creation of a new statistical innovation to its widespread use by applied researchers whether or not they use or program in R.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f47acc9c47b69e026fc9ff083796de2dc92f5c88

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In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

The VGAM Package for Categorical Data Analysis (2010, background): “Imai et al. (2008) present another perspective on the x ij problem with illustrations from Zelig (Imai et al. 2009).”
MICE: Multivariate Imputation by Chained Equations in R (2011, background): “4-6 (Imai et al. 2008) offers a framework with a simple structure and syntax that encompasses a number of existing statistical methods.”
Emergency and Escape: Explaining Derogations from Human Rights Treaties (2011, background): “…democracies with strong courts are statistically the most likely to derogate, followed by democracies that are not stable with strong courts+ 77+ Imai, King, and Lau 2008+ The model’s findings also reject the four competing hypotheses we described earlier+ First, the statistical evidence on…”
Disagreeing About Disagreement: How Conflict in Social Networks Affects Political Behavior (2013, methodology): “(Cross-Cutting Partisanship) Type of Network (09/08) Difference Agreeable Disagreeable Strength of Partisanship (01/08) 2.7 3.4 +.7 (t = -12.82, p < .01) Strength of Ideology (01/08) 2.6 2.8 +.2 (t = -3.80, p < .01) Strength of Likes/Dislikes About Democrats (01/08) 2.4 2.7 +.3 (t = -5.25, p = > .01”
MatchIt: Nonparametric Preprocessing for Parametric Causal Inference (2011, methodology): “A good way to learn R , if you do not know it already, is to learn Zelig which includes a self-contained introduction to R and can be used to analyze the matched data after running MatchIt (Imai, King, and Lau 2011, 2008a).”