scholarly journals Sensitivity and specificity of information criteria

2015 ◽  
Author(s):  
John J. Dziak ◽  
Donna L. Coffman ◽  
Stephanie T. Lanza ◽  
Runze Li
Keyword(s):  
Model Selection ◽  
Sample Size ◽  
Likelihood Ratio Test ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Information Criteria ◽  
Ratio Test ◽  
Relative Importance ◽  
Missed Opportunities ◽  
High Bias

Choosing a model with too few parameters can involve making unrealistically simple assumptions and lead to high bias, poor prediction, and missed opportunities for insight. Such models are not flexible enough to describe the sample or the population well. A model with too many parameters can t the observed data very well, but be too closely tailored to it. Such models may generalize poorly. Penalized-likelihood information criteria, such as Akaike's Information Criterion (AIC), the Bayesian Information Criterion (BIC), the Consistent AIC, and the Adjusted BIC, are widely used for model selection. However, different criteria sometimes support different models, leading to uncertainty about which criterion is the most trustworthy. In some simple cases the comparison of two models using information criteria can be viewed as equivalent to a likelihood ratio test, with the different models representing different alpha levels (i.e., different emphases on sensitivity or specificity; Lin & Dayton 1997). This perspective may lead to insights about how to interpret the criteria in less simple situations. For example, AIC or BIC could be preferable, depending on sample size and on the relative importance one assigns to sensitivity versus specificity. Understanding the differences among the criteria may make it easier to compare their results and to use them to make informed decisions.

scholarly journals Sensitivity and specificity of information criteria

2017 ◽  
Author(s):  
John J. Dziak ◽  
Donna L. Coffman ◽  
Stephanie T. Lanza ◽  
Runze Li
Keyword(s):  
Model Selection ◽  
Sample Size ◽  
Likelihood Ratio Test ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Information Criteria ◽  
Ratio Test ◽  
Relative Importance ◽  
Missed Opportunities ◽  
High Bias

Choosing a model with too few parameters can involve making unrealistically simple assumptions and lead to high bias, poor prediction, and missed opportunities for insight. Such models are not flexible enough to describe the sample or the population well. A model with too many parameters can fit the observed data very well, but be too closely tailored to it. Such models may generalize poorly. Penalized-likelihood information criteria, such as Akaike's Information Criterion (AIC), the Bayesian Information Criterion (BIC), the Consistent AIC, and the Adjusted BIC, are widely used for model selection. However, different criteria sometimes support different models, leading to uncertainty about which criterion is the most trustworthy. In some simple cases the comparison of two models using information criteria can be viewed as equivalent to a likelihood ratio test, with the different models representing different alpha levels (i.e., different emphases on sensitivity or specificity; Lin & Dayton 1997). This perspective may lead to insights about how to interpret the criteria in less simple situations. For example, AIC or BIC could be preferable, depending on sample size and on the relative importance one assigns to sensitivity versus specificity. Understanding the differences among the criteria may make it easier to compare their results and to use them to make informed decisions.

scholarly journals Sensitivity and specificity of information criteria

2017 ◽  
Author(s):  
John J. Dziak ◽  
Donna L. Coffman ◽  
Stephanie T. Lanza ◽  
Runze Li
Keyword(s):  
Model Selection ◽  
Sample Size ◽  
Likelihood Ratio Test ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Information Criteria ◽  
Ratio Test ◽  
Relative Importance ◽  
Missed Opportunities ◽  
High Bias

Choosing a model with too few parameters can involve making unrealistically simple assumptions and lead to high bias, poor prediction, and missed opportunities for insight. Such models are not flexible enough to describe the sample or the population well. A model with too many parameters can fit the observed data very well, but be too closely tailored to it. Such models may generalize poorly. Penalized-likelihood information criteria, such as Akaike's Information Criterion (AIC), the Bayesian Information Criterion (BIC), the Consistent AIC, and the Adjusted BIC, are widely used for model selection. However, different criteria sometimes support different models, leading to uncertainty about which criterion is the most trustworthy. In some simple cases the comparison of two models using information criteria can be viewed as equivalent to a likelihood ratio test, with the different models representing different alpha levels (i.e., different emphases on sensitivity or specificity; Lin & Dayton 1997). This perspective may lead to insights about how to interpret the criteria in less simple situations. For example, AIC or BIC could be preferable, depending on sample size and on the relative importance one assigns to sensitivity versus specificity. Understanding the differences among the criteria may make it easier to compare their results and to use them to make informed decisions.

scholarly journals Sensitivity and specificity of information criteria

2015 ◽  
Author(s):  
John J. Dziak ◽  
Donna L. Coffman ◽  
Stephanie T. Lanza ◽  
Runze Li
Keyword(s):  
Model Selection ◽  
Sample Size ◽  
Likelihood Ratio Test ◽  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Information Criteria ◽  
Ratio Test ◽  
Relative Importance ◽  
Missed Opportunities ◽  
High Bias

Choosing a model with too few parameters can involve making unrealistically simple assumptions and lead to high bias, poor prediction, and missed opportunities for insight. Such models are not flexible enough to describe the sample or the population well. A model with too many parameters can fit the observed data very well, but be too closely tailored to it. Such models may generalize poorly. Penalized-likelihood information criteria, such as Akaike's Information Criterion (AIC), the Bayesian Information Criterion (BIC), the Consistent AIC, and the Adjusted BIC, are widely used for model selection. However, different criteria sometimes support different models, leading to uncertainty about which criterion is the most trustworthy. In some simple cases the comparison of two models using information criteria can be viewed as equivalent to a likelihood ratio test, with the different models representing different alpha levels (i.e., different emphases on sensitivity or specificity; Lin & Dayton 1997). This perspective may lead to insights about how to interpret the criteria in less simple situations. For example, AIC or BIC could be preferable, depending on sample size and on the relative importance one assigns to sensitivity versus specificity. Understanding the differences among the criteria may make it easier to compare their results and to use them to make informed decisions.

scholarly journals Sensitivity and specificity of information criteria

2019 ◽  
Vol 21 (2) ◽  
pp. 553-565 ◽  
Author(s):  
John J Dziak ◽  
Donna L Coffman ◽  
Stephanie T Lanza ◽  
Runze Li ◽  
Lars S Jermiin
Keyword(s):  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Information Criteria ◽  
The Other ◽  
Biological Research ◽  
Ratio Test ◽  
Relative Importance ◽  
Fields Of Study ◽  
Alternative Perspective ◽  
Practical Implications

Abstract Information criteria (ICs) based on penalized likelihood, such as Akaike’s information criterion (AIC), the Bayesian information criterion (BIC) and sample-size-adjusted versions of them, are widely used for model selection in health and biological research. However, different criteria sometimes support different models, leading to discussions about which is the most trustworthy. Some researchers and fields of study habitually use one or the other, often without a clearly stated justification. They may not realize that the criteria may disagree. Others try to compare models using multiple criteria but encounter ambiguity when different criteria lead to substantively different answers, leading to questions about which criterion is best. In this paper we present an alternative perspective on these criteria that can help in interpreting their practical implications. Specifically, in some cases the comparison of two models using ICs can be viewed as equivalent to a likelihood ratio test, with the different criteria representing different alpha levels and BIC being a more conservative test than AIC. This perspective may lead to insights about how to interpret the ICs in more complex situations. For example, AIC or BIC could be preferable, depending on the relative importance one assigns to sensitivity versus specificity. Understanding the differences and similarities among the ICs can make it easier to compare their results and to use them to make informed decisions.

scholarly journals Sensitivity and Specificity of Information Criteria

2018 ◽  
Author(s):  
John J Dziak ◽  
Donna L Coffman ◽  
Stephanie T Lanza ◽  
Runze Li ◽  
Lars Sommer Jermiin
Keyword(s):  
Bayesian Information Criterion ◽  
Information Criterion ◽  
Information Criteria ◽  
The Other ◽  
Biological Research ◽  
Ratio Test ◽  
Relative Importance ◽  
Fields Of Study ◽  
Alternative Perspective ◽  
Practical Implications

Information criteria (ICs) based on penalized likelihood, such as Akaike's Information Criterion (AIC), the Bayesian Information Criterion (BIC), and sample-size-adjusted versions of them, are widely used for model selection in health and biological research. However, different criteria sometimes support different models, leading to discussions about which is the most trustworthy. Some researchers and fields of study habitually use one or the other, often without a clearly stated justification. They may not realize that the criteria may disagree. Others try to compare models using multiple criteria but encounter ambiguity when different criteria lead to substantively different answers, leading to questions about which criterion is best. In this paper we present an alternative perspective on these criteria that can help in interpreting their practical implications. Specifically, in some cases the comparison of two models using ICs can be viewed as equivalent to a likelihood ratio test, with the different criteria representing different alpha levels and BIC being a more conservative test than AIC. This perspective may lead to insights about how to interpret the ICs in more complex situations. For example, AIC or BIC could be preferable, depending on the relative importance one assigns to sensitivity versus specificity. Understanding the differences and similarities among the ICs can make it easier to compare their results and to use them to make informed decisions.

scholarly journals The Efficacy of Common Fit Indices for Enumerating Classes in Growth Mixture Models When Nested Data Structure Is Ignored

SAGE Open ◽  
2017 ◽  
Vol 7 (1) ◽  
pp. 215824401770045 ◽  
Author(s):  
Qi Chen ◽  
Wen Luo ◽  
Gregory J. Palardy ◽  
Ryan Glaman ◽  
Amber McEnturff
Keyword(s):  
Model Selection ◽  
Sample Size ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Information Criterion ◽  
Ratio Test ◽  
Growth Mixture Model ◽  
Growth Mixture ◽  
Selection Indices ◽  
Number Of Classes

Growth mixture model (GMM) is a flexible statistical technique for analyzing longitudinal data when there are unknown heterogeneous subpopulations with different growth trajectories. When individuals are nested within clusters, multilevel growth mixture model (MGMM) should be used to account for the clustering effect. A review of recent literature shows that a higher level of nesting was described in 43% of articles using GMM, none of which used MGMM to account for the clustered data. We conjecture that researchers sometimes ignore the higher level to reduce analytical complexity, but in other situations, ignoring the nesting is unavoidable. This Monte Carlo study investigated whether the correct number of classes can still be retrieved when a higher level of nesting in MGMM is ignored. We investigated six commonly used model selection indices: Akaike information criterion (AIC), consistent AIC (CAIC), Bayesian information criterion (BIC), sample size–adjusted BIC (SABIC), Vuong–Lo–Mendell–Rubin likelihood ratio test (VLMR), and adjusted Lo–Mendell–Rubin likelihood ratio test (ALMR). Results showed that accuracy of class enumeration decreased for all six indices when the higher level is ignored. BIC, CAIC, and SABIC were the most effective model selection indices under the misspecified model. BIC and CAIC were preferable when sample size was large and/or intraclass correlation (ICC) was small, whereas SABIC performed better when sample size was small and/or ICC was large. In addition, SABIC and VLMR/ALMR tended to overextract the number of classes when there are more than two subpopulations and the sample size is large.

scholarly journals Model Selection Procedures in Bounds Test of Cointegration: Theoretical Comparison and Empirical Evidence

Economies ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 49 ◽  
Author(s):  
Waqar Badshah ◽  
Mehmet Bulut
Keyword(s):  
Model Selection ◽  
Akaike Information Criterion ◽  
Bayesian Information Criterion ◽  
Selection Process ◽  
Information Criterion ◽  
Small Sample ◽  
Information Criteria ◽  
Path Model ◽  
Sample Sizes ◽  
Bounds Test

Only unstructured single-path model selection techniques, i.e., Information Criteria, are used by Bounds test of cointegration for model selection. The aim of this paper was twofold; one was to evaluate the performance of these five routinely used information criteria {Akaike Information Criterion (AIC), Akaike Information Criterion Corrected (AICC), Schwarz/Bayesian Information Criterion (SIC/BIC), Schwarz/Bayesian Information Criterion Corrected (SICC/BICC), and Hannan and Quinn Information Criterion (HQC)} and three structured approaches (Forward Selection, Backward Elimination, and Stepwise) by assessing their size and power properties at different sample sizes based on Monte Carlo simulations, and second was the assessment of the same based on real economic data. The second aim was achieved by the evaluation of the long-run relationship between three pairs of macroeconomic variables, i.e., Energy Consumption and GDP, Oil Price and GDP, and Broad Money and GDP for BRICS (Brazil, Russia, India, China and South Africa) countries using Bounds cointegration test. It was found that information criteria and structured procedures have the same powers for a sample size of 50 or greater. However, BICC and Stepwise are better at small sample sizes. In the light of simulation and real data results, a modified Bounds test with Stepwise model selection procedure may be used as it is strongly theoretically supported and avoids noise in the model selection process.

scholarly journals Using Model Selection Criteria to Choose the Number of Principal Components

2021 ◽  
Vol 20 (3) ◽  
pp. 450-461
Author(s):  
Stanley L. Sclove
Keyword(s):  
Model Selection ◽  
Principal Components ◽  
Bayesian Information Criterion ◽  
Selection Criteria ◽  
Information Criterion ◽  
Information Criteria ◽  
Akaike's Information Criterion ◽  
Model Selection Criteria ◽  
Adequate Number ◽  
Number Of Principal Components

AbstractThe use of information criteria, especially AIC (Akaike’s information criterion) and BIC (Bayesian information criterion), for choosing an adequate number of principal components is illustrated.

Comparing Groups of Life-Course Sequences Using the Bayesian Information Criterion and the Likelihood-Ratio Test

2020 ◽  
pp. 008117502095940
Author(s):  
Tim Futing Liao ◽  
Anette Eva Fasang
Keyword(s):  
Life Course ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Simulation Study ◽  
Bayesian Information Criterion ◽  
Sequence Data ◽  
Information Criterion ◽  
West Germany ◽  
Ratio Test ◽  
Life Courses

How can we statistically assess differences in groups of life-course trajectories? The authors address a long-standing inadequacy of social sequence analysis by proposing an adaption of the Bayesian information criterion (BIC) and the likelihood-ratio test (LRT) for assessing differences in groups of sequence data. Unlike previous methods, this adaption provides a useful measure for degrees of difference, that is, the substantive significance, and the statistical significance of differences between predefined groups of life-course trajectories. The authors present a simulation study and an empirical application on whether employment life-courses converged after reunification in the former East Germany and West Germany, using data for six birth-cohort groups ages 15 to 40 years from the German National Education Panel Study. The new methods allow the authors to show that convergence of employment life-courses around reunification was stronger for men than for women and that it was most pronounced in terms of the duration of employment states but weaker for their order and timing in the life-course. Convergence of East German and West German women’s employment lives set in earlier and reflects a secular trend toward a more gender-egalitarian division of labor in West Germany that is unrelated to reunification. The simulation study and the substantive application demonstrate the usefulness of the proposed BIC and LRT methods for assessing group differences in sequence data.

scholarly journals Does the choice of nucleotide substitution models matter topologically?

2016 ◽  
Author(s):  
Michael Hoff ◽  
Stefan Peter Orf ◽  
Benedikt Johannes Riehm ◽  
Diego Darriba ◽  
Alexandros Stamatakis
Keyword(s):  
Model Selection ◽  
Sample Size ◽  
Nucleotide Substitution ◽  
Information Criterion ◽  
Information Criteria ◽  
Substitution Models ◽  
Tree Topologies ◽  
Master Level ◽  
Institute Of Technology ◽  
Definition Of

Background: In the context of a master level programming practical at the computer science department of the Karlsruhe Institute of Technology, we developed and make available an open-source code for testing all 203 possible nucleotide substitution models in the Maximum Likelihood (ML) setting under the common Akaike, corrected Akaike, and Bayesian information criteria. We address the question if model selection matters topologically, that is, if conducting ML inferences under the optimal, instead of a standard General Time Reversible model, yields different tree topologies. We also assess, to which degree models selected and trees inferred under the three standard criteria (AIC, AICc, BIC) differ. Finally, we assess if the definition of the sample size (#sites versus #sites x #taxa) yields different models and, as a consequence, different tree topologies. Results: We find that, all three factors (by order of impact: nucleotide model selection, information criterion used, sample size definition) can yield topologically substantially different final tree topologies (topological difference exceeding 10%) for approximately 5% of the tree inferences conducted on the 39 empirical datasets used in our study. Conclusions: We find that, using the best-fit nucleotide substitution model may change the final ML tree topology compared to an inference under a default GTR model. The effect is less pronounced when comparing distinct information criteria. Nonetheless, in some cases we did obtain substantial topological differences.

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