A simultaneous test in the presence of nested alternative hypotheses

1986 ◽  
Vol 23 (A) ◽  
pp. 187-200 ◽  
Author(s):  
Yuzo Hosoya
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Likelihood Ratio ◽  
Error Rates ◽  
Time Series Model ◽  
Critical Values ◽  
Generalized Likelihood Ratio ◽  
Nested Models ◽  
Simultaneous Test ◽  
Alternative Hypotheses

This paper considers the generalized likelihood ratio (GLR) test or its modification dealing with nested models. The algorithm for evaluating the critical values and the error-rates for the canonical tests are provided; a table of critical values of a class of GLR tests is also given. The test proposed in the paper has applications in time-series model selection.

A simultaneous test in the presence of nested alternative hypotheses

1986 ◽  
Vol 23 (A) ◽  
pp. 187-200
Author(s):  
Yuzo Hosoya
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Likelihood Ratio ◽  
Error Rates ◽  
Time Series Model ◽  
Critical Values ◽  
Generalized Likelihood Ratio ◽  
Nested Models ◽  
Simultaneous Test ◽  
Alternative Hypotheses

This paper considers the generalized likelihood ratio (GLR) test or its modification dealing with nested models. The algorithm for evaluating the critical values and the error-rates for the canonical tests are provided; a table of critical values of a class of GLR tests is also given. The test proposed in the paper has applications in time-series model selection.

scholarly journals Bayesian Inference and Testing Any Hypothesis You Can Specify

2018 ◽  
Vol 1 (2) ◽  
pp. 281-295 ◽  
Author(s):  
Alexander Etz ◽  
Julia M. Haaf ◽  
Jeffrey N. Rouder ◽  
Joachim Vandekerckhove
Keyword(s):  
Bayesian Inference ◽  
Model Selection ◽  
Hypothesis Testing ◽  
Likelihood Ratio ◽  
Special Form ◽  
Null Hypothesis ◽  
Bayes Factor ◽  
Alternative Hypotheses ◽  
Competing Models

Hypothesis testing is a special form of model selection. Once a pair of competing models is fully defined, their definition immediately leads to a measure of how strongly each model supports the data. The ratio of their support is often called the likelihood ratio or the Bayes factor. Critical in the model-selection endeavor is the specification of the models. In the case of hypothesis testing, it is of the greatest importance that the researcher specify exactly what is meant by a “null” hypothesis as well as the alternative to which it is contrasted, and that these are suitable instantiations of theoretical positions. Here, we provide an overview of different instantiations of null and alternative hypotheses that can be useful in practice, but in all cases the inferential procedure is based on the same underlying method of likelihood comparison. An associated app can be found at https://osf.io/mvp53/ . This article is the work of the authors and is reformatted from the original, which was published under a CC-By Attribution 4.0 International license and is available at https://psyarxiv.com/wmf3r/ .

Regression and Time Series Model Selection

Technometrics ◽  
2000 ◽  
Vol 42 (2) ◽  
pp. 214
Author(s):  
Errol Caby ◽  
Allan D. R. McQuarrie ◽  
Chih-Ling Tsai
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Time Series Model

Quality Quandaries*: Time Series Model Selection and Parsimony

2009 ◽  
Vol 21 (3) ◽  
pp. 341-353 ◽  
Author(s):  
Søren Bisgaard ◽  
Murat Kulahci
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Time Series Model

Regression and Time Series Model Selection

10.1142/3573 ◽  
1998 ◽  
Author(s):  
Allan D R McQuarrie ◽  
Chih-Ling Tsai
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Time Series Model

Model Selection Information Criteria for Non-Nested Latent Class Models

1997 ◽  
Vol 22 (3) ◽  
pp. 249-264 ◽  
Author(s):  
Ting Hsiang Lin ◽  
C. Mitchell Dayton
Keyword(s):  
Model Selection ◽  
Latent Class ◽  
Error Rates ◽  
Latent Class Models ◽  
Information Criteria ◽  
Behavioral Analysis ◽  
Nested Models ◽  
Complex Models ◽  
Class Models ◽  
Simple Models

Latent class models have been developed for assessment of hierarchic relations in scaling and behavioral analysis. This article investigated the use of three model selection information criteria—Akaike AIC, Schwarz SIC, and Bozdogan CAIC—for non-nested models. In general, SIC and CAIC were superior to AIC for relatively simple models, whereas AIC was superior for more complex models, although accuracy was often quite low for such models. In addition, some effects were detected for error rates in the models.

Regression and Time Series Model Selection

2000 ◽  
Vol 95 (451) ◽  
pp. 1008 ◽  
Author(s):  
Elvezio Ronchetti ◽  
Allan D. R. McQuarrie ◽  
Chih-Ling Tsai
Keyword(s):  
Time Series ◽  
Model Selection ◽  
Time Series Model
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