Grant: Discovery Grants Program - Individual
Brock University
$43,000.00
Ensemble subspace, penalty, pretest, and shrinkage strategies for high dimensional data
FY 2021-22
Summary
Asyptotic validation; Bias and risk analysis; Ensemble subspace strategies; High dimensional sparse models; Integrating data; Monte Carlo simulation; Penalty, pretest and shrinkage strategies; Post selection and estimation; Relative prediction error; Screen and clean strategies
Program
Discovery Grants Program - Individual
Recipient
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
RGPIN-2017-05228
Name nom
Ahmed, Ejaz
Department département
Mathematics and Statistics
Organization id
22
Institution établissement
Brock University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2021
Competition year année de concours
2017
Award amount
43000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1508
Committee name en
Mathematics and Statistics
Committee name fr
Mathématiques et statistique
Area of application code
1202
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Physical sciences
Area of application fr
Sciences physiques
Research subject code
3001
Research subject group en
Statistics and probability
Research subject group fr
Statistique et probabilité
Research subject en
Statistical theory
Research subject fr
Théorie de statistique
Installment
inst:5
Partie
1
Nb partie
6
Application title
Ensemble subspace, penalty, pretest, and shrinkage strategies for high dimensional data
Keyword
NSERC
Application summary
Asyptotic validation; Bias and risk analysis; Ensemble subspace strategies; High dimensional sparse models; Integrating data; Monte Carlo simulation; Penalty, pretest and shrinkage strategies; Post selection and estimation; Relative prediction error; Screen and clean strategies
