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Another look at forecast selection and combination
Forecast selection and combination are regarded as two competing alternatives. In the literature there is substantial evidence that forecast combination is beneficial, in terms of reducing the forecast errors, as well as...
Cross-validation Forecast combination Forecast pooling Forecasting model selection /dk/atira/pure/subjectarea/asjc/1400/1400 name=General Business,Management and Accounting /dk/atira/pure/subjectarea/asjc/2000/2002 name=Economics and Econometrics /dk/atira/pure/subjectarea/asjc/1800/1803 name=Management Science and Operations Research /dk/atira/pure/subjectarea/asjc/2200/2209 name=Industrial and Manufacturing Engineering
A data-based mathematical modelling study to quantify the effects of ciprofloxacin and ampicillin on the within-host dynamics of Salmonella enterica during treatment and relapse.
Myrto Vlazaki, Omar Rossi, David J Price, Callum McLean, Andrew J Grant, Pietro Mastroeni, Olivier Restif
Jun 18, 2020
On the disagreement of forecasting model selection criteria
Forecasters have been using various criteria to select the most appropriate model from a pool of candidate models. This includes measurements on the in-sample accuracy of the models, information criteria, and cross-validation...
M4 competition Exponential smoothing information criteria model selection time series /dk/atira/pure/subjectarea/asjc/1800/1801 name=Decision Sciences (miscellaneous) /dk/atira/pure/subjectarea/asjc/2000/2001 name=Economics, Econometrics and Finance (miscellaneous) /dk/atira/pure/subjectarea/asjc/1700/1706 name=Computer Science Applications /dk/atira/pure/subjectarea/asjc/1700/1703 name=Computational Theory and Mathematics
Forecast Selection and Representativeness
Effective approaches to forecast model selection are crucial to improve forecast accuracy and to facilitate the use of forecasts for decision-making processes. Information criteria or cross-validation are common approaches of...
Estimation and prediction for spatial generalized linear mixed models with parametric links via reparameterized importance sampling
Spatial generalized linear mixed models (SGLMMs) are popular for analyzing non-Gaussian spatial data. These models assume a prescribed link function that relates the underlying spatial field with the mean response. There are...
Geostatistics Laplace approximation Markov Chain Monte Carlo model selection Multiple importance sampling Reverse logistic regression /dk/atira/pure/subjectarea/asjc/2600/2613 name=Statistics and Probability /dk/atira/pure/subjectarea/asjc/1900/1903 name=Computers in Earth Sciences /dk/atira/pure/subjectarea/asjc/2300/2308 name=Management, Monitoring, Policy and Law
Bayesian context trees
We develop a new Bayesian modelling framework for the class of higher-order,
variable-memory Markov chains, and introduce an associated collection of
methodological tools for exact inference with discrete time series. We...
Critiquing Protein Family Classification Models Using Sufficient Input Subsets.
Brandon Carter, Maxwell Bileschi, Jamie Smith, Theo Sanderson, Drew Bryant, David Belanger, Lucy J Colwell
Oct 20, 2020
In many application domains, neural networks are highly accurate and have been deployed at large scale. However, users often do not have good tools for understanding how these models arrive at their predictions. This has...
Judgmental Selection of Forecasting Models
In this paper, we explored how judgment can be used to improve the selection of a forecasting model. We compared the performance of judgmental model selection against a standard algorithm based on information criteria. We also...
behavioral operations combination decomposition model selection /dk/atira/pure/subjectarea/asjc/1400/1408 name=Strategy and Management /dk/atira/pure/subjectarea/asjc/1800/1803 name=Management Science and Operations Research /dk/atira/pure/subjectarea/asjc/2200/2209 name=Industrial and Manufacturing Engineering
Estimation and prediction for spatial generalized linear mixed models with parametric links via reparameterized importance sampling
Spatial generalized linear mixed models (SGLMMs) are popular for analyzing non-Gaussian spatial data. These models assume a prescribed link function that relates the underlying spatial field with the mean response. There are...
Geostatistics Laplace approximation Markov Chain Monte Carlo model selection Multiple importance sampling Reverse logistic regression /dk/atira/pure/subjectarea/asjc/2600/2613 name=Statistics and Probability /dk/atira/pure/subjectarea/asjc/1900/1903 name=Computers in Earth Sciences /dk/atira/pure/subjectarea/asjc/2300/2308 name=Management, Monitoring, Policy and Law
Forecast Selection and Representativeness
Effective approaches to forecast model selection are crucial to improve forecast accuracy and to facilitate the use of forecasts for decision-making processes. Information criteria or cross-validation are common approaches of...
Another look at forecast selection and combination
Forecast selection and combination are regarded as two competing alternatives. In the literature there is substantial evidence that forecast combination is beneficial, in terms of reducing the forecast errors, as well as...
Cross-validation Forecast combination Forecast pooling Forecasting model selection /dk/atira/pure/subjectarea/asjc/1400/1400 name=General Business,Management and Accounting /dk/atira/pure/subjectarea/asjc/2000/2002 name=Economics and Econometrics /dk/atira/pure/subjectarea/asjc/1800/1803 name=Management Science and Operations Research /dk/atira/pure/subjectarea/asjc/2200/2209 name=Industrial and Manufacturing Engineering
Classification of molecular sequence data using Bayesian phylogenetic mixture models
Rate variation among the sites of a molecular sequence is commonly found in applications of phylogenetic inference. Several approaches exist to account for this feature but they do not usually enable the investigator to pinpoint...
Classification of molecular sequence data using Bayesian phylogenetic mixture models
Rate variation among the sites of a molecular sequence is commonly found in applications of phylogenetic inference. Several approaches exist to account for this feature but they do not usually enable the investigator to pinpoint...
On the disagreement of forecasting model selection criteria
Forecasters have been using various criteria to select the most appropriate model from a pool of candidate models. This includes measurements on the in-sample accuracy of the models, information criteria, and cross-validation...
M4 competition Exponential smoothing information criteria model selection time series /dk/atira/pure/subjectarea/asjc/1800/1801 name=Decision Sciences (miscellaneous) /dk/atira/pure/subjectarea/asjc/2000/2001 name=Economics, Econometrics and Finance (miscellaneous) /dk/atira/pure/subjectarea/asjc/1700/1706 name=Computer Science Applications /dk/atira/pure/subjectarea/asjc/1700/1703 name=Computational Theory and Mathematics
Recapture or precapture? Fallibility of standard capture-recapture methods in the presence of referrals between sources.
Hayley E Jones, Matthew Hickman, Nicky J Welton, Daniela De Angelis, Ross J Harris, AE Ades
Aug 16, 2018
Capture-recapture methods, largely developed in ecology, are now commonly used in epidemiology to adjust for incomplete registries and to estimate the size of difficult-to-reach populations such as problem drug users....
Bias log-linear models model selection parameter identifiability prevalence estimation problem drug use Bias Data Collection Data Interpretation Statistical England Epidemiologic Research Design Humans Linear Models Models Statistical Population Density Referral and Consultation Substance-Related Disorders
Judgmental Selection of Forecasting Models
In this paper, we explored how judgment can be used to improve the selection of a forecasting model. We compared the performance of judgmental model selection against a standard algorithm based on information criteria. We also...
behavioral operations combination decomposition model selection /dk/atira/pure/subjectarea/asjc/1400/1408 name=Strategy and Management /dk/atira/pure/subjectarea/asjc/1800/1803 name=Management Science and Operations Research /dk/atira/pure/subjectarea/asjc/2200/2209 name=Industrial and Manufacturing Engineering