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Exploration of the MCMC Wald test with linear regression
Recently, Asparouhov and Muthén Structural Equation Modeling: A Multidisciplinary Journal , 28 , 1–14, (
2021a ,2021b ) proposed a variant of the Wald...
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Lineare Regression
Das „Was-man-wissen-sollte-Kapitel“ wird mit der bivariaten linearen Regression fortgesetzt. Auch diese gehört typischerweise zur Grundausbildung in...
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Erweiterungen der multiplen Regression
In diesem Kapitel soll es um einige wichtige Sonderfälle der Anwendung der multiplen Regression gehen. Es wird erläutert, wie man die multiple...
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A tutorial on Bayesian multi-model linear regression with BAS and JASP
Linear regression analyses commonly involve two consecutive stages of statistical inquiry. In the first stage, a single ‘best’ model is defined by a...
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Non-parametric Regression Among Factor Scores: Motivation and Diagnostics for Nonlinear Structural Equation Models
We provide a framework for motivating and diagnosing the functional form in the structural part of nonlinear or linear structural equation models...
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Diskriminanzanalyse und multinomiale logistische Regression
Mit Diskriminanzanalyse und multinomialer logistischer Regression wenden wir uns zwei Verfahren zu, die als Ziel haben, die Gruppenzugehörigkeit von...
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Logistic regression with sparse common and distinctive covariates
Having large sets of predictor variables from multiple sources concerning the same individuals is becoming increasingly common in behavioral...
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Assumptions of the Normal Error Regression Model
This chapter describes the main assumptions that are made in the derivation of regression-based normative data, i.e., equality of the error...
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Reducing Attenuation Bias in Regression Analyses Involving Rating Scale Data via Psychometric Modeling
Many studies in fields such as psychology and educational sciences obtain information about attributes of subjects through observational studies, in...
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Using External Information for More Precise Inferences in General Regression Models
Empirical research usually takes place in a space of available external information, like results from single studies, meta-analyses, official...
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Connecting process models to response times through Bayesian hierarchical regression analysis
Process models specify a series of mental operations necessary to complete a task. We demonstrate how to use process models to analyze response-time...
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Thinking Inside the Bounds: Improved Error Distributions for Indifference Point Data Analysis and Simulation Via Beta Regression using Common Discounting Functions
Standard nonlinear regression is commonly used when modeling indifference points due to its ability to closely follow observed data, resulting in a...
