Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

By S. Lynch. 2007

582 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/07d32b5380f57b86f8caa1af246575e7b678db8f

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

A Bayesian Approach for Estimating Multilevel Latent Contextual Models (2016, background): “Bayesian point and interval estimates can be derived when the chain has converged and approximates the posterior distribution sufficiently well (for an introduction, see Christensen, Johnson, Branscum, & Hanson, 2011; Jackman, 2009; Lynch, 2007).”
Improving Transparency and Replication in Bayesian Statistics: The WAMBS-Checklist (2017, unknown): “…novice reader to, among many others: Bolstad (2007); Carlin and Louis (2009); Christensen, Johnson, Branscum, and Hanson (2010); Depaoli and Boyajian, (2014); Gelman and Hill (2007); Kaplan, 2014; Kruschke (2010); Jackman (2009); Lynch (2007); Ntzoufras (2009); or van de Schoot and Depaoli (2014).”
A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research (2013, unknown): “Also, the prior precision needs to be speci ﬁ ed, which re ﬂ ects the certainty or uncertainty about the prior mean.”
Assessing creativity with self-report scales: A review and empirical evaluation. (2012, methodology): “Researchers in the Bayesian tradition can use Bayesian Marcov Chain Monte Carlo (MCMC) methods (Lynch, 2007), which don't make the distributional assumptions made by traditional frequentist statistics, although we suspect that it will be a”
Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art (2008, methodology): “In contrast, the full NC-RUM and BIN are estimated within a fully Bayesian framework (Gelman, Carlin, Stern, & Rubin, 1995; Kim & Bolt, 2007; Lynch, 2007; Rupp, Dey, & Zumbo, 2004), and analyses can take several hours or even days until convergence is achieved.”
Bayesian Estimation and Inference (2015, unknown): “An exemplary book is by Gill (2008), but many others are also very good (e.g., Bolstad, 2007; Christensen, Johnson, & Branscum, 2011; Gelman et al., 2004; Hoff, 2009; Jackman, 2009; Kruschke, 2011; Lee, 2004; Lynch, 2010; for concise pro-Bayesian arguments, see Robert, 2007, chap.”
On a signal detection approach to m-alternative forced choice with bias, with maximum likelihood and Bayesian approaches to estimation (2012, methodology): “Some useful textbooks on Bayesian analysis are Congdon (2005), Gelman, Carlin, Stern, and Rubin (2004), Jackman (2009), and Lynch (2007).”
Bayesian data analysis. (2010, methodology): “This is a straightforward approach to assessing the credibility of null values, and it is presented in many textbooks (e.g., Berry, 1996; Bolstad, 2007; Carlin & Louis, 2009; Gelman, Carlin, Stern, & Rubin, 2004; Gelman & Hill, 2007; Lynch, 2007).”
A Systematic Review of the Methodology for Person Fit Research in Item Response Theory: Lessons about Generalizability of Inferences from the Design of Simulation Studies (2013, background): “In the context of estimation the distinction between frequentist and Bayesian approaches to model estimation become important; for details on Bayesian statistics more generally I refer the reader to sources like Lynch (2007), Patz and Junker (1999a, 1999b), or Rupp, Dey, and Zumbo (2004).”
GraphPIM: Enabling Instruction-Level PIM Offloading in Graph Computing Frameworks (2017, background): “(2) Rich Property (RP): In this category, vertices are associated with rich graph properties, which can be as complex as large stochastic tables, such as those in Gibbs inference [19] and Bayesian network computation [20].”
A Comparison of Maximum Likelihood and Bayesian Estimation for Polychoric Correlation Using Monte Carlo Simulation (2011, methodology): “The specification of prior distribution in Bayesian estimation has been historically known as a challenging task (Lynch, 2007), and one could argue that PPMC estimate could be a reasonable choice for the prior mean in Bayesian PCC estimation.”
Why is it difficult to accurately predict the COVID-19 epidemic? (2020, unknown): “Markov chain Monte Carlo (MCMC) algorithms are used to approximate a posterior distribution of parameters by randomly sampling the parameter space (Lynch, 2007).”
A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes (2015, background): “…(Kaplan & Depaoli, 2012; Lee, 2007; Muthén & Asparouhov, 2012a; Scheines, Hoijtink, & Boomsma, 1999) and MLMs (Congdon, 2010; Gelman & Hill, 2007; Lynch, 2007) is a prevalent methodological topic because Bayesian methods are useful for solving problems that are unique to these modeling…”