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20142018
most citedMultinomial Models with Linear Inequality Constraints: Overview and Improvements of Computational Methods for Bayesian Inference

38 citations · 105 across the 6 of their papers we have counts for

collaborators

6 papers

stat.CO2018★ 38 cited

Multinomial Models with Linear Inequality Constraints: Overview and Improvements of Computational Methods for Bayesian Inference

Daniel W. Heck, Clintin P. Davis-Stober

Many psychological theories can be operationalized as linear inequality constraints on the parameters of multinomial distributions (e.g., discrete choice analysis). These constrain…

stat.ME2018★ 18 cited

Model selection by minimum description length: Lower-bound sample sizes for the Fisher information approximation

Daniel W. Heck, Morten Moshagen, Edgar Erdfelder

The Fisher information approximation (FIA) is an implementation of the minimum description length principle for model selection. Unlike information criteria such as AIC or BIC, it…

stat.ME2017★ 16 cited

Quantifying Uncertainty in Transdimensional Markov Chain Monte Carlo Using Discrete Markov Models

Daniel W. Heck, Antony M. Overstall, Quentin F. Gronau +1

Bayesian analysis often concerns an evaluation of models with different dimensionality as is necessary in, for example, model selection or mixture models. To facilitate this evalua…

stat.ME2015★ 12 cited

Adjusted Priors for Bayes Factors Involving Reparameterized Order Constraints

Daniel W. Heck, Eric-Jan Wagenmakers

Many psychological theories that are instantiated as statistical models imply order constraints on the model parameters. To fit and test such restrictions, order constraints of the…

q-bio.NC2015★ 6 cited

On the relation between the (censored) shifted Wald and the Wiener distribution as measurement models for choice response times

Robert Miller, Stefan Scherbaum, Daniel W. Heck +2

Inferring processes or constructs from performance data is a major hallmark of cognitive psychometrics. Particularly, diffusion modeling of response times (RTs) from correct and er…

stat.ME2014★ 15 cited

Testing Order Constraints: Qualitative Differences Between Bayes Factors and Normalized Maximum Likelihood

Daniel W. Heck, Eric-Jan Wagenmakers, Richard D. Morey

We compared Bayes factors to normalized maximum likelihood for the simple case of selecting between an order-constrained versus a full binomial model. This comparison revealed two…