In this course we shall study Non-parameteric statistical inference. This is different from parametric Statistical Inference as here the underlying distribution is assumed to be unknown. Also, these work when the population is not Normally distributed. It has major applications in many practical situations. Also, is used in Data Science and Machine Learning.INTENDED AUDIENCE :B.Sc /B.Tech students in Statistics, maths& Computing/ Comp. Sc. PREREQUISITES : Basic understanding of Statistics and ProbabilityINDUSTRIES SUPPORT :Any company that deal with data will need this
Overview
Syllabus
Week 1:Introduction to Non-parametric Inference , Estimation of Location and Dispersion, Introduction to Linear Rank Statistics Week 2:Linear ranks tests for Scale Problem, Some results on Linear Rank Statistics Week 3:Tests of Goodness of Fit , Tests of Randomness The General Two-Sample Problem, Run Test, Median Test, Kolmogorov-Smirnov Test Week 4:Measures of Association of Bi-variate samples, Kendall’s Tau Coefficient, Spearman’s Rank Coefficient, Equality of k independent samples.
Taught by
Prof. Niladri Chatterjee
