Page 134 - Academic Handbook FoS+29june

6. G.S. Maddala, and K Lahiri, Introduction to Econometrics, Wiley, 2009.
MA3141: MODELLING & SIMULATION [2 1 0 3]
Introduction to Modelling and Simulation: System analysis, classification of systems, system theory basics, its relation to simulation,
model classification, conceptual, abstract, and simulation models. heterogeneous models, methodology of model building,
simulation systems and languages, means for model and experiment description, Principles of simulation system design, parallel
process modelling, using petri nets and finite automata in simulation models of queuing systems, discrete simulation models,
model time, simulation experiment control; Continuous System Modelling: Overview of numerical methods used for continuous
simulation, combined simulation, role of simulation in digital systems design, special model classes, models of heterogeneous
systems; Checking Model Validity: verification of models, analysis of simulation results, simulation results visualization, interactive
simulation, design and control of simulation experiments; Model Optimization: Generating, transformation, and testing of
pseudorandom numbers, stochastic models, Monte Carlo method, overview of commonly used simulation systems.
References:
th
1. S. Ross, Simulation, Academic Press, 5 edition, 2012.
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2. A. Law and D. Kelton, Simulation Modelling and Analysis, McGraw-Hill, 5 edition, 2014.
3. P. Fishwick, Simulation Model Design and Execution, Prentice Hall, 1995.
rd
4. B. P. Zeigler, Theory of modelling and simulation, 3 edition, 2018.

DSE – III (B)
MA3142: RELIABILITY MODELING AND ANALYSIS [2 1 0 3]
Reliability Basic Concepts: Concept of reliability, early age failures, wear-out failures and chance failures, derivation of general
reliability function failure rate, failure density function and mean time between failures; System Reliability Evaluation: series
system, parallel system, partially redundant system, standby system with perfect switching / imperfect switching, effect of spare
components (identical / non- identical) on the system reliability, Wear-out and Component reliability, combined effect of wear-out
and chance failures, reliability of a two component system with single repair facility; Reliability Evaluation Techniques: Conditional
probability approach, cut set method, approximation evaluation, deducing the minimal cut sets, tie set method, connection matrix
technique.
References:
1. J. Medli, Stochastic Processes, New Age International Publisher, 1996.
2. E. Balagurusamy, Reliability Engineering, Tata McGraw-Hill, 2010.
3. S. Zack, Introduction to Reliability Analysis: Probability Model and Statistical Methods, Springer Verlag, 1992.
4. B.K. Kale and S.K. Sinha, Life Testing and Reliability Estimation, 1980.

MA3143: PORTFOLIO OPTIMIZATION [2 1 0 3]
Financial Markets: Investment objectives, measures of return and risk, types of risks, risk free assets, mutual funds, portfolio of
assets, expected risk and return of portfolio; Diversification: Mean-variance portfolio optimization- the Markowitz model and the
two-fund theorem, risk-free assets and one fund theorem, efficient frontier, portfolios with short sales; Capital Market Theory:
Capital assets pricing model- the capital market line, beta of an asset, beta of a portfolio, security market line; Index Tracking
Optimization Models: Portfolio performance evaluation measures.
References:
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1. F. K. Reilly, Keith C. Brown, Investment Analysis and Portfolio Management, 10 edition, South-Western Publishers, 2011.
2. H.M. Markowitz, Mean-Variance Analysis in Portfolio Choice and Capital Markets, Blackwell, New York, 1987.
3. M.J. Best, Portfolio Optimization, Chapman and Hall, CRC Press, 2010.
nd
4. D.G. Luenberger, Investment Science, 2 edition, Oxford University Press, 2013.

DSE – IV (A)
MA3241: MULTIVARIATE ANALYSIS [2 1 0 3]
Multivariate Normal Distribution: Introduction to multivariate statistical methods, matrix algebra, multiple regression formulated in
matrix terms, multivariate normal distribution, maximum likelihood estimators of mean vector and covariance Matrix; Wishart
2
2
Distribution: its matrix and properties, Hotelling's T statistic, derivation and its distribution uses of T statistic, Beheran -Fisher’s
problem. Multivariate Linear Regression Model: Estimation of parameters and their properties, distribution of the matrix of sample
regression coefficients, test of linear hypothesis about regression coefficients, multivariate analysis of variance (MANOVA) of one
way classified data. Wilk’s lambda criterion, likelihood ratio test criteria for testing independence of sets of variables, likelihood
ratio criteria for testing equality of covariance matrices and identity of several multivariate normal populations, Fisher’s
discriminant function, Mahalanobis’ distance; Principle Component Analysis: Principal components, its uses and importance,
canonical variables and canonical correlations.
References:
1. T.W. Anderson, An Introduction to Multivariate Statistical Analysis, John Wiley, 2016.
2. C. R. Rao, Linear Statistical Inference and its Applications, John Wiley, 2015.
3. R. A. Johnson and D. W. Wichern, Applied Multivariate Statistical Analysis, Prentice Hall of India, 2001.
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