Allan McCutcheon and Colin Mills
- Published in print:
- 1998
- Published Online:
- November 2003
- ISBN:
- 9780198292371
- eISBN:
- 9780191600159
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198292376.003.0005
- Subject:
- Political Science, Reference
Extending the basic regression model to the analysis of contingency tables, using odds and odds ratios. The worked example shows how log‐linear and latent class techniques can be assimilated into a ...
More
Extending the basic regression model to the analysis of contingency tables, using odds and odds ratios. The worked example shows how log‐linear and latent class techniques can be assimilated into a single model using GLIM, LCAG, and LEM software, and how to interpret the BIC and AIC statistics.Less
Extending the basic regression model to the analysis of contingency tables, using odds and odds ratios. The worked example shows how log‐linear and latent class techniques can be assimilated into a single model using GLIM, LCAG, and LEM software, and how to interpret the BIC and AIC statistics.
Ziheng Yang
- Published in print:
- 2014
- Published Online:
- August 2014
- ISBN:
- 9780199602605
- eISBN:
- 9780191782251
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199602605.003.0004
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Evolutionary Biology / Genetics
This chapter describes the pruning algorithm for calculating the likelihood on a tree, as well as extensions under complex substitution models, including the gamma and covarion models of rate ...
More
This chapter describes the pruning algorithm for calculating the likelihood on a tree, as well as extensions under complex substitution models, including the gamma and covarion models of rate variation among sites and lineages. It discusses numerical optimization algorithms for maximum likelihood estimation. It provides a critical assessment of methods for reconstructing ancestral states for both molecular sequences and morphological characters. Finally the chapter discusses model selection in phylogenetics using the likelihood ratio test (LRT) and information criteria such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC).Less
This chapter describes the pruning algorithm for calculating the likelihood on a tree, as well as extensions under complex substitution models, including the gamma and covarion models of rate variation among sites and lineages. It discusses numerical optimization algorithms for maximum likelihood estimation. It provides a critical assessment of methods for reconstructing ancestral states for both molecular sequences and morphological characters. Finally the chapter discusses model selection in phylogenetics using the likelihood ratio test (LRT) and information criteria such as the Akaike information criterion (AIC) and Bayesian information criterion (BIC).
Andy Hector
- Published in print:
- 2015
- Published Online:
- March 2015
- ISBN:
- 9780198729051
- eISBN:
- 9780191795855
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780198729051.003.0011
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies, Ecology
Generalized linear mixed-effects models (GLMMs) are introduced as a combination of the mixed-effects models and GLMs met in earlier chapters. The additional challenges of the analysis are explored ...
More
Generalized linear mixed-effects models (GLMMs) are introduced as a combination of the mixed-effects models and GLMs met in earlier chapters. The additional challenges of the analysis are explored and the differences in the R software functions and its output are explained. Model comparison and selection can be done using information criteria or likelihood ratio tests. In addition to the AIC and BIC the DIC was devised for use with multilevel models. The options for assessing how well the model assumptions are met are reduced relative to linear models and GLMs but the most accessible current options are demonstrated.Less
Generalized linear mixed-effects models (GLMMs) are introduced as a combination of the mixed-effects models and GLMs met in earlier chapters. The additional challenges of the analysis are explored and the differences in the R software functions and its output are explained. Model comparison and selection can be done using information criteria or likelihood ratio tests. In addition to the AIC and BIC the DIC was devised for use with multilevel models. The options for assessing how well the model assumptions are met are reduced relative to linear models and GLMs but the most accessible current options are demonstrated.
Jeffrey S. Racine
- Published in print:
- 2019
- Published Online:
- January 2019
- ISBN:
- 9780190900663
- eISBN:
- 9780190933647
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190900663.003.0006
- Subject:
- Economics and Finance, Econometrics
This chapter covers model selection methods and model averaging methods. It relies on knowledge of solving a quadratic program which is outlined in an appendix.
This chapter covers model selection methods and model averaging methods. It relies on knowledge of solving a quadratic program which is outlined in an appendix.
Rebecca Dirksen
- Published in print:
- 2020
- Published Online:
- April 2020
- ISBN:
- 9780190928056
- eISBN:
- 9780190928094
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190928056.003.0009
- Subject:
- Music, Ethnomusicology, World Music
This chapter considers the music examined in this text from a broader lens, pointing out that angaje (politically engaged) expressive forms have been tied to defining moments in Haitian history when ...
More
This chapter considers the music examined in this text from a broader lens, pointing out that angaje (politically engaged) expressive forms have been tied to defining moments in Haitian history when sovereignty has been challenged and fought for, including the Haitian Revolution, the US Occupation, and the downfall of Duvalier. This radical activist energy is intrinsic to mizik angaje (politically engaged music) and associated with artists such as Auguste Linstant de Pradines (Kandjo) and Manno Charlemagne. Reflections from musicians today who are widely thought of in terms of their serious engagement with social and political issues, from Matyas to Manzè to BIC (Roosevelt Saillant), suggest the wide array of current perspectives on what constitutes mizik angaje, or, alternatively, mizik sosyal (socially engaged music). This chapter goes on to underscore the deeper and more sustained cultural and political work that has to take place beyond the lyrics and musical performance for sustained change in society.Less
This chapter considers the music examined in this text from a broader lens, pointing out that angaje (politically engaged) expressive forms have been tied to defining moments in Haitian history when sovereignty has been challenged and fought for, including the Haitian Revolution, the US Occupation, and the downfall of Duvalier. This radical activist energy is intrinsic to mizik angaje (politically engaged music) and associated with artists such as Auguste Linstant de Pradines (Kandjo) and Manno Charlemagne. Reflections from musicians today who are widely thought of in terms of their serious engagement with social and political issues, from Matyas to Manzè to BIC (Roosevelt Saillant), suggest the wide array of current perspectives on what constitutes mizik angaje, or, alternatively, mizik sosyal (socially engaged music). This chapter goes on to underscore the deeper and more sustained cultural and political work that has to take place beyond the lyrics and musical performance for sustained change in society.
Jan Sprenger and Stephan Hartmann
- Published in print:
- 2019
- Published Online:
- October 2019
- ISBN:
- 9780199672110
- eISBN:
- 9780191881671
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780199672110.003.0010
- Subject:
- Philosophy, Philosophy of Science
Is simplicity a virtue of a good scientific theory, and are simpler theories more likely to be true or predictively successful? If so, how much should simplicity count vis-à-vis predictive accuracy? ...
More
Is simplicity a virtue of a good scientific theory, and are simpler theories more likely to be true or predictively successful? If so, how much should simplicity count vis-à-vis predictive accuracy? We address this question using Bayesian inference, focusing on the context of statistical model selection and an interpretation of simplicity via the degree of freedoms of a model. We rebut claims to prove the epistemic value of simplicity by means of showing its particular role in Bayesian model selection strategies (e.g., the BIC or the MML). Instead, we show that Bayesian inference in the context of model selection is usually done in a philosophically eclectic, instrumental fashion that is more tuned to practical applications than to philosophical foundations. Thus, these techniques cannot justify a particular “appropriate weight of simplicity in model selection”.Less
Is simplicity a virtue of a good scientific theory, and are simpler theories more likely to be true or predictively successful? If so, how much should simplicity count vis-à-vis predictive accuracy? We address this question using Bayesian inference, focusing on the context of statistical model selection and an interpretation of simplicity via the degree of freedoms of a model. We rebut claims to prove the epistemic value of simplicity by means of showing its particular role in Bayesian model selection strategies (e.g., the BIC or the MML). Instead, we show that Bayesian inference in the context of model selection is usually done in a philosophically eclectic, instrumental fashion that is more tuned to practical applications than to philosophical foundations. Thus, these techniques cannot justify a particular “appropriate weight of simplicity in model selection”.
