Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

doi:10.1007/978-0-387-71265-9

Cited by 330 publications

(42 citation statements)

References 42 publications

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“…Bayesian estimation of SEMs (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 frameworks. Compared with ML, Bayesian estimation of hierarchical models can produce more accurate and efficient parameter estimates when the data consist of a small number of groups (Asparouhov & Muthén, 2010;Baldwin & Fellingham, 2013).…”

Section: Benefits Of a Bayesian Approach For Multilevel Semmentioning

confidence: 99%

A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes

Depaoli

Clifton

2015

Structural Equation Modeling: A Multidisciplinary Journal

179

181

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Multilevel Structural equation models are most often estimated from a frequentist framework via maximum likelihood. However, as shown in this article, frequentist results are not always accurate. Alternatively, one can apply a Bayesian approach using Markov chain Monte Carlo estimation methods. This simulation study compared estimation quality using Bayesian and frequentist approaches in the context of a multilevel latent covariate model. Continuous and dichotomous variables were examined because it is not yet known how different types of outcomes-most notably categorical-affect parameter recovery in this modeling context. Within the Bayesian estimation framework, the impact of diffuse, weakly informative, and informative prior distributions were compared. Findings indicated that Bayesian estimation may be used to overcome convergence problems and improve parameter estimate bias. Results highlight the differences in estimation quality between dichotomous and continuous variable models and the importance of prior distribution choice for cluster-level random effects.

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Section: Benefits Of a Bayesian Approach For Multilevel Semmentioning

confidence: 99%

A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes

Depaoli

Clifton

2015

Structural Equation Modeling: A Multidisciplinary Journal

179

181

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“…Many of these books use the BUGS syntax (Lunn et al, 2000), which the probabilistic programming language JAGS (Plummer, 2012) also adopts; however, Stan code for these books is slowly becoming available on the Stan home page (https://github.com/stan-dev/ example-models/wiki). For those with introductory calculus, a slightly more technical introduction to Bayesian methods by Lynch (2007) is an excellent choice. Finally, the textbook by Gelman et al (2013) is the definitive modern guide, and provides a more advanced treatment.…”

Section: Further Readingmentioning

confidence: 99%

Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists

Sorensen¹,

Hohenstein²,

Vasishth³

2016

TQMP

157

136

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With the arrival of the R packages nlme and lme4, linear mixed models (LMMs) have come to be widely used in experimentally-driven areas like psychology, linguistics, and cognitive science. This tutorial provides a practical introduction to fitting LMMs in a Bayesian framework using the probabilistic programming language Stan. We choose Stan (rather than WinBUGS or JAGS) because it provides an elegant and scalable framework for fitting models in most of the standard applications of LMMs. We ease the reader into fitting increasingly complex LMMs, using a twocondition repeated measures self-paced reading study.

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“…Therefore, Jeffreys' prior is not preferable. In order to obtain the posterior distribution of parameters, this papers uses the MCMC (Markov chain Monte Carlo) simulation method with Gibbs sampling [22]. The discussion on the computing method is ignored here, as it is not the main concern of this paper.…”

Section: Bayesian Parameter Estimationmentioning

confidence: 99%

“…"Std" denotes the standard deviation. The lower and upper bounds of the credible interval are derived from the posterior distribution (22). Table 8 shows that the γ of the data for both trucks is below 0.5; the credible bounds of γ are far below 1.…”

Section: Analysis Using Exponent Bivariate Weibullmentioning

confidence: 99%

Reliability modelling on two-dimensional life data using bivariate Weibull distribution: with case study of truck in mines

Fuqing¹,

Barabadi²,

Lu³

2017

EiN

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Reliability modelling on two-dimensional life data using bivaRiate weibull distRibution: with case study of tRuck in mines modelowanie niezawodnościowe dwuwymiaRowych danych dotyczących okResu eksploatacji z wykoRzystaniem dwuwymiaRowego Rozkładu weibulla. z badań nad wywRotkami kopalnianymi Fuqing Y, BArABAdi A, Jinmei L. reliability modelling on two-dimensional life data using bivariate weibull distribution: with case study of truck in mines. eksploatacja i niezawodnosc -maintenance and reliability 2017; 19 (4): 650-659, http://dx

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Introduction to Applied Bayesian Statistics and Estimation for Social Scientists

Cited by 330 publications

References 42 publications

A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes

A Bayesian Approach to Multilevel Structural Equation Modeling With Continuous and Dichotomous Outcomes

Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists

Reliability modelling on two-dimensional life data using bivariate Weibull distribution: with case study of truck in mines

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