Grant: Discovery Grants Program - Individual
Concordia University
$13,000.00
Asymmetric kernel and positive wavelet smoothing for size-biased data
FY 2016-17
Summary
I have developed through previous research grants some appropriate smoothing methods dealing with survival data. These methods have been useful for estimating the survival probability beyond the largest observation in the sample as well as other important characteristics of the population such as the expected life time conditional on a given age. Traditional methods such as the kernel methods require additional corrective measures to ward of the boundary bias that is avoided in the new smoothing techniques. The basic objective of the present proposal is to investigate extensions and modifications of such techniques to non-standard data such as weighted data, censored data and dependent data. These extensions are useful in practical settings of many applied fields such as reliability and engineering, clinical studies as well as agricultural studies.
Program
Discovery Grants Program - Individual
Recipient
Location
Quebec, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
3661-2011
Name nom
Chaubey, Yogendra
Department département
Mathematics and Statistics
Organization id
70
Institution établissement
Concordia University
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2016
Competition year année de concours
2011
Award amount
13000
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
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
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
Partie
5
Nb partie
5
Application title
Asymmetric kernel and positive wavelet smoothing for size-biased data
Keyword
NSERC
Application summary
I have developed through previous research grants some appropriate smoothing methods dealing with survival data. These methods have been useful for estimating the survival probability beyond the largest observation in the sample as well as other important characteristics of the population such as the expected life time conditional on a given age. Traditional methods such as the kernel methods require additional corrective measures to ward of the boundary bias that is avoided in the new smoothing techniques. The basic objective of the present proposal is to investigate extensions and modifications of such techniques to non-standard data such as weighted data, censored data and dependent data. These extensions are useful in practical settings of many applied fields such as reliability and engineering, clinical studies as well as agricultural studies.