Reference number : GC-2017-Q1-02653, amendment number : , agreement type : G, recipient legal name : Marchand, Éric (Université de Sherbrooke), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Discovery Grants Program - Individual, program name french : Programme de subventions à la découverte - individuelles, program purpose english : Prediction is at the heart of statistics and vital to many problems of science, engineering, medicine and health sciences, finance and economics, among other fields of study. A predictive density is a density estimate, based on past and current data that can be used as a surrogate density for future or missing observables. Prediction, and more precisely predictive densities, are central in statistical methodology and practice, and arise in linear regression models, non-parametric regression , generalized linear models, time series, stochastic processes; model selection, metrology, and data compression, and others. The main objectives and challenges of this proposal are methodological. They consist in making available and practicable efficient predictive densities for a wide array of situations. The proposed program of research will be concerned with deepening our understanding of statistical methods and foundations as related to the estimation of predictive densities. 
 
 They will also be motivated by applications, namely by problems with many parameters or more complex structures. In particular, the proposed research is directed towards problems of selective inference or prediction, where methods should arguably take into account a selection mechanism (e.g., of models, of populations to study). Problems with additional information, which are related to restricted parameter space problems will also be investigated. There exists many situations where such prior additional information is available, and for which methods of inference ought to capitalize on such information., program purpose french : Prediction is at the heart of statistics and vital to many problems of science, engineering, medicine and health sciences, finance and economics, among other fields of study. A predictive density is a density estimate, based on past and current data that can be used as a surrogate density for future or missing observables. Prediction, and more precisely predictive densities, are central in statistical methodology and practice, and arise in linear regression models, non-parametric regression , generalized linear models, time series, stochastic processes; model selection, metrology, and data compression, and others. The main objectives and challenges of this proposal are methodological. They consist in making available and practicable efficient predictive densities for a wide array of situations. The proposed program of research will be concerned with deepening our understanding of statistical methods and foundations as related to the estimation of predictive densities. 
 
 They will also be motivated by applications, namely by problems with many parameters or more complex structures. In particular, the proposed research is directed towards problems of selective inference or prediction, where methods should arguably take into account a selection mechanism (e.g., of models, of populations to study). Problems with additional information, which are related to restricted parameter space problems will also be investigated. There exists many situations where such prior additional information is available, and for which methods of inference ought to capitalize on such information., agreement title english : Efficient predictive density estimation, agreement title french : Efficient predictive density estimation, agreement number : RGPIN, agreement value : 150000.0, agreement start date : 2017-05-10, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada