Herbert Hoijtink mostly deals with Econometrics, Bayes factor, Statistics, Prior probability and Gibbs sampling. His work on p-value as part of general Econometrics study is frequently connected to Data interpretation, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. His studies deal with areas such as Linear model, Bayesian linear regression and Model selection as well as Bayes factor.
His work carried out in the field of Prior probability brings together such families of science as Element, Analysis of covariance, Posterior probability and Bayesian inference. His studies examine the connections between Gibbs sampling and genetics, as well as such issues in Applied mathematics, with regards to Goodness of fit, Latent class model and Posterior predictive distribution. Herbert Hoijtink interconnects Normal approximation and Analysis of variance in the investigation of issues within Bayesian probability.
Herbert Hoijtink spends much of his time researching Econometrics, Statistics, Bayesian probability, Bayes factor and Prior probability. Herbert Hoijtink has included themes like Statistical hypothesis testing, Bayesian statistics, Bayesian inference, Item response theory and Statistical model in his Econometrics study. His study in the fields of Rasch model, Latent class model, Multivariate normal distribution and Multivariate statistics under the domain of Statistics overlaps with other disciplines such as Expectation–maximization algorithm.
His Bayesian probability study also includes
Herbert Hoijtink mainly investigates Bayes factor, Statistics, Bayesian probability, Econometrics and Bayes' theorem. His Bayes factor research incorporates elements of Statistical hypothesis testing, Machine learning, Statistical model and Frequentist probability. His work on Alternative hypothesis, Sample size determination and Variance decomposition of forecast errors as part of general Statistics study is frequently linked to R package and Law of total variance, therefore connecting diverse disciplines of science.
His study in Bayesian probability is interdisciplinary in nature, drawing from both Probability and statistics, Structural equation modeling, Multivariate statistics and Data mining. He studies Econometrics, focusing on p-value in particular. His work investigates the relationship between Bayes' theorem and topics such as Bayesian inference that intersect with problems in Probabilistic logic, Electroencephalography and Frequency.
His scientific interests lie mostly in Econometrics, Bayes factor, Statistics, Bayesian probability and Bayes' theorem. In Econometrics, Herbert Hoijtink works on issues like Statistical significance, which are connected to Replication crisis. Bayes factor is a subfield of Artificial intelligence that Herbert Hoijtink studies.
The various areas that Herbert Hoijtink examines in his Bayesian probability study include Reliability, Cognitive psychology, Cognition and Convergent validity. His Bayes' theorem study incorporates themes from Statistical hypothesis testing, Frequentist probability and Bayesian inference. His Prior probability research is multidisciplinary, incorporating perspectives in Null hypothesis and Type I and type II errors.
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Redefine statistical significance
Nature Human Behaviour (2018)
Redefine Statistical Significance
Daniel Benjamin;James Berger;Magnus Johannesson;Brian Nosek.
Research Papers in Economics (2017)
Bayesian estimation and testing of structural equation models
Richard Scheines;Herbert Hoijtink;Anne Boomsma.
Inequality Constrained Analysis of Variance: A Bayesian Approach.
Irene Klugkist;Olav Laudy;Herbert Hoijtink.
Psychological Methods (2005)
Reliability and validity of the Dutch version of the inventory of traumatic grief (ITG).
Paul A. Boelen;Jan Van Den Bout;Jos De Keijser;Herbert Hoijtink.
Death Studies (2003)
The many null distributions of person fit indices
Ivo W. Molenaar;Herbert Hoijtink.
The Multidimensionality of Self-Report Schizotypy in a Psychiatric Population: An Analysis Using Multidimensional Rasch Models
Meinte G. Vollema;Herbert Hoijtink.
Schizophrenia Bulletin (2000)
The Best of Both Worlds: Factor Analysis of Dichotomous Data Using Item Response Theory and Structural Equation Modeling
Angelika Glockner-Rist;Herbert Hoijtink.
Structural Equation Modeling (2003)
Bayesian Evaluation of Informative Hypotheses
Herbert Hoijtink;Irene Klugkist;Paul A Boelen.
A multidimensional item response model: Constrained latent class analysis using the gibbs sampler and posterior predictive checks
Herbert Hoijtink;Ivo W. Molenaar.
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