Grant: Discovery Grants Program - Individual
HEC Montréal
$27,000.00
Statistical tests based on empirical processes
EF 2023-24
Résumé
In most applications of statistics, stochastic dynamic models are assumed to satisfy certain assumptions, mainly related to their probability distribution. For example, in time series, random error terms (innovations) are often assumed to be independent and/or have a density belonging to some parametric family. The general objective of the proposed research program is to further study empirical processes based on pseudo-observations, which are transformations of data from the same sample, and use them to develop tests of change-point, independence and goodness-of-fit test for models appearing mainly in actuarial science, econometrics, and statistics.
Programme
Discovery Grants Program - Individual
Bénéficiaire
Emplacement
Quebec, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
RGPIN-2020-06347
Name nom
Rémillard, Bruno
Department département
Sciences de la décision - Sciences de la décision
Organization id
65
Institution établissement
HEC Montréal
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2020
Award amount
27000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
DISCOVERY RESEARCH
Group fr
RECHERCHE AXÉE SUR LA DÉCOUVERTE
Committee code
1508
Committee name en
Mathematics and Statistics
Committee name fr
Mathématiques et statistique
Area of application code
1205
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Mathematical sciences
Area of application fr
Sciences mathématiques
Research subject code
9999
Research subject group en
Not available
Research subject group fr
Not available
Research subject en
Non disponible
Research subject fr
Non disponible
Application title
Statistical tests based on empirical processes
Keyword
Bootstrapping methods; Dependence models; Empirical processes; Goodness-of-fit; Mixed data
Application summary
In most applications of statistics, stochastic dynamic models are assumed to satisfy certain assumptions, mainly related to their probability distribution. For example, in time series, random error terms (innovations) are often assumed to be independent and/or have a density belonging to some parametric family. The general objective of the proposed research program is to further study empirical processes based on pseudo-observations, which are transformations of data from the same sample, and use them to develop tests of change-point, independence and goodness-of-fit test for models appearing mainly in actuarial science, econometrics, and statistics.
