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Details of MA5109 (Autumn 2019)

Level: 5 Type: Theory Credits: 4.0

Course CodeCourse NameInstructor(s)
MA5109 Multivariate Analysis Asok Kumar Nanda

Syllabus
Distance between two random vectors, multivariate distribution function, generalized variance, properties of multivariate normal distribution and estimation of its parameters, distribution of quadratic forms, spherical and elliptical distributions, Wishart and Hotelling's $T^2$ distributions along with their properties, classification and discriminant analysis, multiple and partial correlation coefficients, principal component analysis, canonical correlations and canonical variables, clustering and factor analysis.

Prerequisite
Statistical Inference (MA4107)

References

Suggested Texts:



  1. Anderson, T.W., An Introduction to Multivariate Statistical Analysis, Wiley.

  2. Giri, N.C., Multivariate Statistical Analysis, Academic Press.

  3. Johnson, R.A. and Wichern, D.W., Applied Multivariate Statistical Analysis, Prentice-Hall of India.

  4. Jolliffe, I.T., Principal Component Analysis, Springer.

  5. Kshirsagar, A.M., Multivariate Analysis, Marcel Dekker.

  6. Rao, C.R., Linear Statistical Inference and Its Applications, Wiley.

  7. Rencher, L.C., Methods of Multivariate Analysis, Wiley.

Course Credit Options

Sl. No.ProgrammeSemester NoCourse Choice
1 IP 1 Not Allowed
2 IP 3 Not Allowed
3 IP 5 Not Allowed
4 MR 1 Not Allowed
5 MR 3 Not Allowed
6 MS 9 Elective
7 RS 1 Not Allowed
8 RS 2 Not Allowed

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