Description
Classical nonparametric techniques; nonparametric density estimation; nonparametric regression analysis: kernel estimators, orthogonal series estimators, smoothing splines; estimation of statistical functionals; nonparametric bootstrap; jackknife; elements of high dimensional statistical inference; multiple testing and false discovery. Statistical software will be used.
Prerequisites
- Prerequisite(s): STAT 3559 or permission of the School.
Conditions and arrangements
- Prerequisite(s): STAT 3559 or permission of the School.
- Also offered at the graduate level, with different requirements, as STAT 5516 , for which additional credit is precluded.
- Lectures three hours a week.
Reference text in its original language
Classical nonparametric techniques; nonparametric density estimation; nonparametric regression analysis: kernel estimators, orthogonal series estimators, smoothing splines; estimation of statistical functionals; nonparametric bootstrap; jackknife; elements of high dimensional statistical inference; multiple testing and false discovery. Statistical software will be used.
- Prerequisite(s): STAT 3559 or permission of the School.
- Also offered at the graduate level, with different requirements, as STAT 5516 , for which additional credit is precluded.
- Lectures three hours a week.
Sources and references
Dates and sources are retained to help you verify the information. Translations are provided to facilitate reading; the official source governs conditions and requirements.
Source reference : https://calendar.carleton.ca/undergrad/courses/STAT/