Titlebook: Dependent Data in Social Sciences Research; Forms, Issues, and M Mark Stemmler,Wolfgang Wiedermann,Francis L. Huang Book 2024Latest edition

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Jaitri Das,Buddhadeb Chattopadhyayestablished itself as one of the primary tools for the recursive partitioning of structural equation models (SEM). The resulting SEM trees partition the sample into groups of similar individuals while identifying the most important predictors of group differences in the process. However, until recen

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Climate Change and Agriculture, is often the main direction of influence, there are also bidirectional processes, e.g., as described in the parent-child coercive cycle (cf. Patterson GR, Coercive family process. Castalia, Eugene, 1982). These processes were mainly investigated in clinical and other studies from North America, but

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Electromagnetic Wave Absorption Materials,ine learning. This is a purely time-continuous approach relying on the theory of optimization for dynamical systems. We complement the proposed algorithm with a practical example, comparing the results of this approach to those obtained via Continuous Time Structural Equation Modeling (.). To this e

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s various tools to study such mechanisms. However, owing to the lack of background knowledge, it is often difficult to prepare causal graphs required for performing statistical causal inference. To alleviate the difficulty, we have worked on developing statistical methods for estimating causal relat

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Introduction to Manufacturing Engineering,al information on dependence in repeatedly measured outcomes, which may be valuable for building statistical models for explanation and prediction. This paper proposes an explorative approach to facilitate the understanding of dependence structures in longitudinal categorical data with ordinal outco

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Helical, Bevel, and Worm Gears,onal datasets. Based on principles from Bayesian statistics, this approach goes beyond mere pattern recognition, delving into the realm of causation by modeling the probabilistic conditional dependencies among variables. This chapter discusses the logic of using Bayesian network analysis as a causal

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