Discussion of "Analysis of variance--why it is more important than ever" by A. Gelman

By Andrew Gelman. 2005

813 citations recorded by the source corpus.

Discussion of ``Analysis of variance--why it is more important than ever'' by A. Gelman [math.ST/0504499]

Subfields: Applied Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/10f26a107a5b3bcbe6c548b5a764b7cdc9dda347

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In Applied Mathematics

In Statistics and Probability

Citations in the corpus

Neglected biological patterns in the residuals (2011, methodology): “The exact definition of fixed effects is controversial (Gelman 2005).”
Simple means to improve the interpretability of regression coefficients (2010, background): “This is clearly advantageous to compare P values, since it puts the focus on effect sizes rather than significance (Gelman 2005; Nakagawa &Cuthill 2007).”
Default Bayes factors for ANOVA designs (2012, methodology): “This coherence is captured by the exchangeability of level (Gelman, 2005) as implemented by the correlations in the multivariate Cauchy.”
Evidence of Online Performance Deterioration in User Sessions on Reddit (2016, background): “corporation of heterogeneous eects and behavioral dierences accounting for the non-independent nature of longitudinal data at hand. Mixed-eects models include both xed and random eects; following [10], we refer to xed eects as eects being constant across levels (e.g., individuals) and random eects a”
Repeated Measures Correlation (2017, background): “Partial pooling estimates parameters at multiple levels by treating a lower level of analysis (e.g., individuals) as random/varying effects from a probability distribution drawn from a higher level of analysis (e.g., experiment) (see Gelman, 2005).”
Species and environmental characteristics point to flow regulation and drought as drivers of riparian plant invasion (2014, methodology): “Following the methods of Hector et al. (2011) and Gelman (2005), we built a hierarchical (multilevel) linear model where the response was the proportion of total terrestrial plant cover in a quadrat that was exotic.”
Fitting Linear Mixed-Effects Models Using lme4 (2014, background): “Much has been written about important practical and philosophical differences between these two types of interactions (e.g., Henderson Jr. 1982; Gelman 2005).”
Scale-Dependent Priors for Variance Parameters in Structured Additive Distributional Regression (2016, methodology): “…priors give rise to the Weibull prior for the smoothing variance, Principle 4 can also be applied to choose hyperparameters of other prior structures, such as inverse gamma priors or half-normal, half-Cauchy and proper uniform priors for standard deviations (Gelman, 2005, 2006; Hodges, 2013).”
2. Average Predictive Comparisons for Models with Nonlinearity, Interactions, and Variance Components (2007, background): “Variance Components Models “Random effects” or “mixed effects” or “variance components” models correspond to categorical input variables whose parameters are structured in batches (e.g., see, Searle, Casella, and McCulloch 1992 and Gelman 2005).”
Online Evaluation for Information Retrieval (2016, background): “Examples of these areas are top/left regions, as evidenced by multiple eye-tracking studies, where user attention heat maps form an F-shaped pattern or a golden triangle, among others; see Granka et al. [2004], Guan and Cutrell [2007a,b] and the references therein.”
Globally, functional traits are weak predictors of juvenile tree growth, and we do not know why (2015, methodology): “graphic regions following the approach of Gelman (2005). Our aim”
Bayesian data analysis. (2010, methodology): “Thus, estimates of outlying conditions are shrunk toward the grand mean, and this shrinkage attenuates false alarms (e.g., Berry & Hochberg, 1999; Gelman, 2005; Gelman et al., 2009; Lindquist & Gelman, 2009; Meng & Dempster, 1987).”
Bayesian Analysis of Factorial Designs (2017, methodology): “Our modeling framework is influenced by Gelman (2005), who conceptualizes ANOVA as a hierarchical model in which effects are clustered within factors.”
Should I Use Fixed or Random Effects? (2014, background): “…has been written on the theoretical properties of both approaches (for example, Kreft and DeLeeuw 1998; Robinson 1998; Kennedy 2003; Frees 2004; Gelman 2005; Wilson and Butler 2007; Arceneaux and Nickerson 2009; Wooldridge 2010; Greene 2012), recommendations for applied researchers are often…”