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.
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.
