It is quite common in statistical modeling to select a model and make inference as if the model had been known in advance;i.e. ignoring model selection uncertainty. The resulted estimator is called post-model selectio...It is quite common in statistical modeling to select a model and make inference as if the model had been known in advance;i.e. ignoring model selection uncertainty. The resulted estimator is called post-model selection estimator (PMSE) whose properties are hard to derive. Conditioning on data at hand (as it is usually the case), Bayesian model selection is free of this phenomenon. This paper is concerned with the properties of Bayesian estimator obtained after model selection when the frequentist (long run) performances of the resulted Bayesian estimator are of interest. The proposed method, using Bayesian decision theory, is based on the well known Bayesian model averaging (BMA)’s machinery;and outperforms PMSE and BMA. It is shown that if the unconditional model selection probability is equal to model prior, then the proposed approach reduces BMA. The method is illustrated using Bernoulli trials.展开更多
Sample size determination typically relies on a power analysis based on a frequentist conditional approach. This latter can be seen as a particular case of the two-priors approach, which allows to build four distinct ...Sample size determination typically relies on a power analysis based on a frequentist conditional approach. This latter can be seen as a particular case of the two-priors approach, which allows to build four distinct power functions to select the optimal sample size. We revise this approach when the focus is on testing a single binomial proportion. We consider exact methods and introduce a conservative criterion to account for the typical non-monotonic behavior of the power functions, when dealing with discrete data. The main purpose of this paper is to present a Shiny App providing a user-friendly, interactive tool to apply these criteria. The app also provides specific tools to elicit the analysis and the design prior distributions, which are the core of the two-priors approach.展开更多
In several instances of statistical practice, it is not uncommon to use the same data for both model selection and inference, without taking account of the variability induced by model selection step. This is usually ...In several instances of statistical practice, it is not uncommon to use the same data for both model selection and inference, without taking account of the variability induced by model selection step. This is usually referred to as post-model selection inference. The shortcomings of such practice are widely recognized, finding a general solution is extremely challenging. We propose a model averaging alternative consisting on taking into account model selection probability and the like-lihood in assigning the weights. The approach is applied to Bernoulli trials and outperforms Akaike weights model averaging and post-model selection estimators.展开更多
This research evaluates the effect of monetary policy rate and exchange rate on inflation across continents using both Frequentist and Bayesian General-ized Additive Mixed Models(GAMMs).Extending an earlier study that...This research evaluates the effect of monetary policy rate and exchange rate on inflation across continents using both Frequentist and Bayesian General-ized Additive Mixed Models(GAMMs).Extending an earlier study that em-ployed Frequentist and Bayesian Linear Mixed Model,continent-specific ran-dom slopes for exchange rate were incorporated to assess the variability in the rate at which changes in exchange rate influence inflation.Fixed effects cap-tured the overall impact of the predictors,while random effects accounted for regional differences.Results consistently showed that the monetary policy rate significantly affects inflation,whereas the exchange rate does not.Strong evi-dence supported variation in baseline inflation across continents(random in-tercepts),but findings on random slope variability were mixed,suggesting modest and model-dependent heterogeneity.Bayesian models offered a slightly better fit and predictive accuracy.These findings underscore the central role of monetary policy in inflation control,while exchange rate effects remain context-dependent.These results highlight the importance of accounting for regional heterogeneity when modelling global inflation dynamics.Policymak-ers should tailor inflation strategies to regional contexts and prioritize robust monetary policy tools over exchange rate management.These findings are as-sociational,not causal and future research should adopt a credible causal iden-tification strategy to establish causal relationships.展开更多
In applications, the traditional estimation procedure generally begins with model selection.Once a specific model is selected, subsequent estimation is conducted under the selected model withoutconsideration of the un...In applications, the traditional estimation procedure generally begins with model selection.Once a specific model is selected, subsequent estimation is conducted under the selected model withoutconsideration of the uncertainty from the selection process. This often leads to the underreportingof variability and too optimistic confidence sets. Model averaging estimation is an alternative to thisprocedure, which incorporates model uncertainty into the estimation process. In recent years, therehas been a rising interest in model averaging from the frequentist perspective, and some importantprogresses have been made. In this paper, the theory and methods on frequentist model averagingestimation are surveyed. Some future research topics are also discussed.展开更多
文摘It is quite common in statistical modeling to select a model and make inference as if the model had been known in advance;i.e. ignoring model selection uncertainty. The resulted estimator is called post-model selection estimator (PMSE) whose properties are hard to derive. Conditioning on data at hand (as it is usually the case), Bayesian model selection is free of this phenomenon. This paper is concerned with the properties of Bayesian estimator obtained after model selection when the frequentist (long run) performances of the resulted Bayesian estimator are of interest. The proposed method, using Bayesian decision theory, is based on the well known Bayesian model averaging (BMA)’s machinery;and outperforms PMSE and BMA. It is shown that if the unconditional model selection probability is equal to model prior, then the proposed approach reduces BMA. The method is illustrated using Bernoulli trials.
文摘Sample size determination typically relies on a power analysis based on a frequentist conditional approach. This latter can be seen as a particular case of the two-priors approach, which allows to build four distinct power functions to select the optimal sample size. We revise this approach when the focus is on testing a single binomial proportion. We consider exact methods and introduce a conservative criterion to account for the typical non-monotonic behavior of the power functions, when dealing with discrete data. The main purpose of this paper is to present a Shiny App providing a user-friendly, interactive tool to apply these criteria. The app also provides specific tools to elicit the analysis and the design prior distributions, which are the core of the two-priors approach.
文摘In several instances of statistical practice, it is not uncommon to use the same data for both model selection and inference, without taking account of the variability induced by model selection step. This is usually referred to as post-model selection inference. The shortcomings of such practice are widely recognized, finding a general solution is extremely challenging. We propose a model averaging alternative consisting on taking into account model selection probability and the like-lihood in assigning the weights. The approach is applied to Bernoulli trials and outperforms Akaike weights model averaging and post-model selection estimators.
文摘This research evaluates the effect of monetary policy rate and exchange rate on inflation across continents using both Frequentist and Bayesian General-ized Additive Mixed Models(GAMMs).Extending an earlier study that em-ployed Frequentist and Bayesian Linear Mixed Model,continent-specific ran-dom slopes for exchange rate were incorporated to assess the variability in the rate at which changes in exchange rate influence inflation.Fixed effects cap-tured the overall impact of the predictors,while random effects accounted for regional differences.Results consistently showed that the monetary policy rate significantly affects inflation,whereas the exchange rate does not.Strong evi-dence supported variation in baseline inflation across continents(random in-tercepts),but findings on random slope variability were mixed,suggesting modest and model-dependent heterogeneity.Bayesian models offered a slightly better fit and predictive accuracy.These findings underscore the central role of monetary policy in inflation control,while exchange rate effects remain context-dependent.These results highlight the importance of accounting for regional heterogeneity when modelling global inflation dynamics.Policymak-ers should tailor inflation strategies to regional contexts and prioritize robust monetary policy tools over exchange rate management.These findings are as-sociational,not causal and future research should adopt a credible causal iden-tification strategy to establish causal relationships.
基金supported by the National Natural Science Foundation of China under Grant Nos. 70625004, 10721101, and 70221001
文摘In applications, the traditional estimation procedure generally begins with model selection.Once a specific model is selected, subsequent estimation is conducted under the selected model withoutconsideration of the uncertainty from the selection process. This often leads to the underreportingof variability and too optimistic confidence sets. Model averaging estimation is an alternative to thisprocedure, which incorporates model uncertainty into the estimation process. In recent years, therehas been a rising interest in model averaging from the frequentist perspective, and some importantprogresses have been made. In this paper, the theory and methods on frequentist model averagingestimation are surveyed. Some future research topics are also discussed.
