Composite likelihood model comparison test under fixed and local alternatives

Stat ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. e182
Author(s):  
Yawen Xu ◽  
Xin Gao ◽  
Xiaogang Wang ◽  
Augustine Wong
Keyword(s):  
Model Comparison ◽  
Composite Likelihood ◽  
Local Alternatives ◽  
Comparison Test ◽  
Likelihood Model

scholarly journals Model comparison with composite likelihood information criteria

Bernoulli ◽  
2014 ◽  
Vol 20 (4) ◽  
pp. 1738-1764 ◽  
Author(s):  
Chi Tim Ng ◽  
Harry Joe
Keyword(s):  
Model Comparison ◽  
Information Criteria ◽  
Composite Likelihood

An unbiased model comparison test using cross-validation

2013 ◽  
Vol 48 (4) ◽  
pp. 2155-2173 ◽  
Author(s):  
Bruce A. Desmarais ◽  
Jeffrey J. Harden
Keyword(s):  
Model Comparison ◽  
Cross Validation ◽  
Comparison Test

scholarly journals A Parameter-Free Model Comparison Test Using Differential Algebra

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Heather A. Harrington ◽  
Kenneth L. Ho ◽  
Nicolette Meshkat
Keyword(s):  
Time Course ◽  
Model Comparison ◽  
Time Series Data ◽  
Dynamic Properties ◽  
Differential Algebra ◽  
Series Data ◽  
Comparison Test ◽  
Competing Models ◽  
Model Equations ◽  
Parameter Values

We present a method for rejecting competing models from noisy time-course data that does not rely on parameter inference. First we characterize ordinary differential equation models in only measurable variables using differential-algebra elimination. This procedure gives input-output equations, which serve as invariants for time series data. We develop a model comparison test using linear algebra and statistics to reject incorrect models from their invariants. This algorithm exploits the dynamic properties that are encoded in the structure of the model equations without recourse to parameter values, and, in this sense, the approach is parameter-free. We demonstrate this method by discriminating between different models from mathematical biology.

scholarly journals New Developments in Factor Score Regression: Fit Indices and a Model Comparison Test

2019 ◽  
Vol 79 (6) ◽  
pp. 1017-1037 ◽  
Author(s):  
Ines Devlieger ◽  
Wouter Talloen ◽  
Yves Rosseel
Keyword(s):  
Structural Equation Modeling ◽  
Structural Equation ◽  
Model Comparison ◽  
Type I Error ◽  
Factor Score ◽  
Regression Coefficients ◽  
Equation Modeling ◽  
Type I ◽  
Fit Indices ◽  
Comparison Test

Factor score regression (FSR) is a popular alternative for structural equation modeling. Naively applying FSR induces bias for the estimators of the regression coefficients. Croon proposed a method to correct for this bias. Next to estimating effects without bias, interest often lies in inference of regression coefficients or in the fit of the model. In this article, we propose fit indices for FSR that can be used to inspect the model fit. We also introduce a model comparison test based on one of these newly proposed fit indices that can be used for inference of the estimators on the regression coefficients. In a simulation study we compare FSR with Croon’s corrections and structural equation modeling in terms of bias of the regression coefficients, Type I error rate and power.

Identifying suboptimalities with factorial model comparison

2018 ◽  
Vol 41 ◽  
Author(s):  
Wei Ji Ma
Keyword(s):  
Experimental Design ◽  
Model Comparison ◽  
Process Models ◽  
Multiple Forms ◽  
Factorial Experimental Design ◽  
Factorial Model ◽  
Multiple Components ◽  
The Many

AbstractGiven the many types of suboptimality in perception, I ask how one should test for multiple forms of suboptimality at the same time – or, more generally, how one should compare process models that can differ in any or all of the multiple components. In analogy to factorial experimental design, I advocate for factorial model comparison.

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