Details of MA5121 (Autumn 2023)
| Level: 5 | Type: Theory | Credits: 4.0 |
| Course Code | Course Name | Instructor(s) |
|---|---|---|
| MA5121 | Nonparametric Statistics | Satyaki Mazumder |
| Syllabus |
|---|
| 1. A very short review of parametric inference, what is and why
do nonparametric statistics. 2. Empirical distribution function, estimating cdf, confidence interval for cdf, estimation of statistical functionals. 3. Estimation of density function; histograms, nearest neighbor and kernels. 4. Nonparametric regression; nearest neighbor, kernel & local regression, regularization and splines, variance estimation, confidence band and average coverage. 5. Introduction of order statistics and ranks and their distribution free property. 6. Goodness of fit problem: Chi-squared test, other rank and distance based tests, different plots. 7. One sample & two sample testing problems, several sample testing problems. |
| Prerequisite |
|---|
| Statistics I (MA3206) |
| References |
|---|
| References:
1. All of Nonparametric Statistics: Larry Wasserman 2. Density Estimation for Statistics & Data Analysis: B. W. Silverman 3. Local Polynomial Modelling & its Applications: J. Fan and I. Gijbels 4. Nonparametric Statistics: Theory & Methods: J. V. Deshpande, I. Dewan & U. N. Nimbalkar 5. Nonparametric Statistical Inference: J. D. Gibbons & S. Chakraborti |
Course Credit Options
| Sl. No. | Programme | Semester No | Course Choice |
|---|---|---|---|
| 1 | IP | 1 | Not Allowed |
| 2 | IP | 3 | Not Allowed |
| 3 | IP | 5 | Not Allowed |
| 4 | MP | 1 | Not Allowed |
| 5 | MP | 3 | Not Allowed |
| 6 | MR | 1 | Not Allowed |
| 7 | MR | 3 | Not Allowed |
| 8 | MS | 3 | Not Allowed |
| 9 | MS | 5 | Not Allowed |
| 10 | MS | 7 | Not Allowed |
| 11 | MS | 9 | Elective |
| 12 | RS | 1 | Elective |
| 13 | RS | 2 | Elective |