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Manuel C. Voelkle [5]Manuel Voelkle [1]
  1.  26
    Beyond “happy, angry, or sad?”: Age-of-poser and age-of-rater effects on multi-dimensional emotion perception.Michaela Riediger, Manuel C. Voelkle, Natalie C. Ebner & Ulman Lindenberger - 2011 - Cognition and Emotion 25 (6):968-982.
  2.  24
    An Adult Developmental Approach to Perceived Facial Attractiveness and Distinctiveness.Natalie C. Ebner, Joerg Luedicke, Manuel C. Voelkle, Michaela Riediger, Tian Lin & Ulman Lindenberger - 2018 - Frontiers in Psychology 9.
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  3.  6
    Score-Guided Structural Equation Model Trees.Manuel Arnold, Manuel C. Voelkle & Andreas M. Brandmaier - 2021 - Frontiers in Psychology 11.
    Structural equation model trees are data-driven tools for finding variables that predict group differences in SEM parameters. SEM trees build upon the decision tree paradigm by growing tree structures that divide a data set recursively into homogeneous subsets. In past research, SEM trees have been estimated predominantly with the R package semtree. The original algorithm in the semtree package selects split variables among covariates by calculating a likelihood ratio for each possible split of each covariate. Obtaining these likelihood ratios is (...)
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  4.  8
    Discrete- vs. Continuous-Time Modeling of Unequally Spaced Experience Sampling Method Data.Silvia de Haan-Rietdijk, Manuel C. Voelkle, Loes Keijsers & Ellen L. Hamaker - 2017 - Frontiers in Psychology 8.
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  5.  12
    Gaussian Process Panel Modeling—Machine Learning Inspired Analysis of Longitudinal Panel Data.Julian D. Karch, Andreas M. Brandmaier & Manuel C. Voelkle - 2020 - Frontiers in Psychology 11.
    In this article, we extend the Bayesian nonparametric regression method Gaussian Process Regression to the analysis of longitudinal panel data. We call this new approach Gaussian Process Panel Modeling (GPPM). GPPM provides great flexibility because of the large number of models it can represent. It allows classical statistical inference as well as machine learning inspired predictive modeling. GPPM offers frequentist and Bayesian inference without the need to resort to Markov chain Monte Carlo-based approximations, which makes the approach exact and fast. (...)
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  6.  23
    From Data to Causes III: Bayesian Priors for General Cross-Lagged Panel Models.Michael J. Zyphur, Ellen L. Hamaker, Louis Tay, Manuel Voelkle, Kristopher J. Preacher, Zhen Zhang, Paul D. Allison, Dean C. Pierides, Peter Koval & Edward F. Diener - 2021 - Frontiers in Psychology 12.
    This article describes some potential uses of Bayesian estimation for time-series and panel data models by incorporating information from prior probabilities in addition to observed data. Drawing on econometrics and other literatures we illustrate the use of informative “shrinkage” or “small variance” priors while extending prior work on the general cross-lagged panel model. Using a panel dataset of national income and subjective well-being we describe three key benefits of these priors. First, they shrink parameter estimates toward zero or toward each (...)
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