The following are lists of books as references for each subjects

STA-6101 Statistical Methods

  • Heiberger, R.M. and B Holland. 2004. Statistical Analysis and Data Display. An Intermediate Course with Examples in S-Plus, R and SAS. Springer Science+Business Media, New York.
  • Wilcox, R.R. 2010. Fundamentals of Modern Statistical Methods. Substantially Improving Power and Accuracy. 2nd ed. Springer Science+Business Media, New York.
  • Freund, R.J. and W.J. Wilson. 2003. Statistical Methods. 2nd ed. Academic Press. San Diego.
  • Hardle, W., Y. Mori and P. Vieu. 2007. Statistical Methods for Biostatistics and Related Fields. Springer Science+Business Media, New York.

STA-6102 Linear Models

  • Christensen, R. 2011. Plane Answers to Complex Questions. The Theory of Linear Models. 4th ed. Springer Science+Business Media, New York.
  • Christensen, R. 2016. Analysis of Variance, Design and Regression. Linear Modeling for Unbalanced Data. 2nd ed. CRC Press. Taylor and Francis. Boca Raton, Florida.
  • Christensen, R. 2019. Advanced Linear Modeling. Statistical Learning and Dependent Data. 3rd ed. Springer Nature. Switzerland.

STA-6103 Probability Theory

  • Gnedenko, B.V. and A.Y. Khinchin. 1961. Elementary Introduction to The Theory of Probability. W.H. Freeman and Co., San Francisco.
  • Ash, R.B. 2000. Probability and Measure Theory. 2nd ed. Harcourt Academy Press. San Diego.
  • Billingsley, P. 1995. Probability and Measure. 3rd ed. John Wiley & Sons. New York.

STA-6204 Theory of Statistical Inference I

  • Lehmann, E.L. and G. Casella. 1998. Theory of Point Estimation. 2nd ed. Springer Verlag. New York.
  • Almudevar, A. 2022. Theory of Statistical Inference. CRC Press. Taylor and Francis. Boca Raton, Florida.
  • Kiefer, J.C. 1987. Introduction to Statistical Inference. Springer-verlag. New York.

STA-6205 Time Series Analysis

  • Box, G.E.P., G.M. Jenkins, G.C. Reinsel and G.M. Ljung. 2016. Time Series Analysis. Forecasting and Control. 5th ed. John Wiley & Sons. Hoboken, New Jersey.
  • Makridakis, S.G., S.C. Wheelwright and V.E. McGee. 1997. Forecasting: Methods and Applications. 3rd ed. John Wiley & Sons.
  • Kirchgässner, G., J. Wolters and U Hassler. 2013. Introduction to Modern Time Series. 2nd ed. Springer-Verlag. Berlin.

STA-6206 Applied Stochastic Processes

  • Brémaud, P. 2020. Point Process Calculus in Time and Space. An Introduction with Applications. Springer Nature. Switzerland.
  • Basu, A.K. 2007. Introduction to Stochastic Process. Alpha Science International, Ltd. Harrow UK.
  • Ross, S.M. 1996. Stochastic Processes. John Wiley & Sons. New York.
  • Korosteleva, O. 2022. Stochastic Processes with R: An Introduction. CRC Press. Taylor & Francis. Boca Raton, Florida.

STA-6307 Theory of Statistical Inference II

  • Lehmann, E.L. and J.P. Romano. 2022. Testing Statistical Hypotheses. 4th ed. Springer Science+Business Media. Switzerland.
  • Koch, K-R. 1999. Parameter Estimation and Hypothesis Testing in Linear Models. 2nd ed, Springer-Verlag. Berlin.
  • Good, P. 2005. Permutation, Parametric and Bootstrap Tests of Hypotheses. 3rd ed. Springer Science+Business Media. Switzerland.

STA-6308 Advanced Experimental Design

  • Milliken, G.A. and D.E. Johnson. 2009. Analysis of Messy Data vol 1 Designed Experiments. CRC Press. Taylor & Francis. Boca Raton, Florida.
  • Milliken, G.A. and D.E. Johnson. 1989. Analysis of Messy Data vol 2 Nonreplicated Experiments. CRC Press. Taylor & Francis. Boca Raton, Florida.
  • Milliken, G.A. and D.E. Johnson. 2002. Analysis of Messy Data vol 3 Analysis of Covariance. CRC Press. Taylor & Francis. Boca Raton, Florida.
  • Hinkelmann, K. and O. Kempthorne. 2008. Design and Analysis of Experiments vol 1 Introduction to Experimental Design. 2nd ed. John Wiley & Sons. Hoboken. New Jersey.
  • Hinkelmann, K. and O. Kempthorne. 2005. Design and Analysis of Experiments vol 2 Advanced Experimental Design. John Wiley & Sons. Hoboken. New Jersey.

STA-6309 Theory of Nonparametric Statistics

  • Deshpande, J.V., U. Naik-Nimbakar and I. Dewan. 2018. Nonparametric Statistics : Theory and Methods. World Scintific Publishing. Singapore.
  • Govindarajulu, Z. 2007. Nonparametric Inference. World Scintific Publishing. Singapore.
  • Gibbons, J.D. and S. Chakraborti. 2011. Nonparametric Statistical Inference. 5th ed. CRC Press. Taylor & Francis. Boca Raton, Florida.

STA-6511 Survival Analysis

  • Huber, C., N. Limnios, M. Mesbah and M. Nikulin. 2008. Mathematical Methods in Survival Analysis, Reliability and Quality of Life. ISTE Ltd., London.
  • Kleinbaum, D.G. and M. Klein. 2005. Survival Analysis. A Self-Learning Text. 2nd ed.  Springer Science+Business Media, New York.
  • Lee, E.T. and J.W. Wang. 2003. Statistical Methods for Survival Data Analysis. 3rd ed. John Wiley & Sons. Hoboken, New Jersey.
  • Klein, J.P. and M.L. Moeschberger. 2003. Survival Analysis Techniques for Censored and Truncated Data. 2nd ed.  Springer-Verlag. New York.
  • Zhou, M. 2016. Empirical Likelihood Method in Survival Analysis. CRC Press. Taylor & Francis. Boca Raton, Florida.

STA-6512 Survey Sampling Theory

  • Pitard, F.F. 2019. Theory and Sampling and Sampling Practice. 3rd ed. CRC Press. Taylor & Francis. Boca Raton, Florida.
  • Arnab, R. 2017. Survey Sampling Theory and Applications. Academic Press, Elsevier. Oxford.
  • Singh, S., S.A. Sedory, M.M. Rueda, A. Arcos and R. Arnab. 2016. A New Concept for Tuning Design Weights in Survey Sampling. Jackknifing in Theory and Practice. Academic Press, Elsevier. Oxford.
  • Sampath, S. 2001. Sampling Theory and Methods. Narosa Publishing House. New Delhi.
  • Chauduri, A. and H. Stenger. 2005. Survey Sampling. Theory and Methods. 2nd ed. Chapman & Hall/ CRC Press. Taylor & Francis. Boca Raton, Florida.

STA-6513 Decision Making Theory

  • Kochenderfer, M.J. 2015. Decision Making Under Uncertainty. Theory and Application. The MIT Press. Massachusetts.
  • Marchau, V.A.W.J, W.A. Walker, P.J.T.M. Bloemen and S.W. Popper. 2019. Decision Making Under Deep Uncertainty. Springer Nature Switzerland.
  • Kahraman, C and Ö. Kabak. 2016. Fuzzy Statistical Decision Making. Theory and Applications. Springer International Publishing. Switzerland.

STA-6514 Spatial Statistics

  • Anselin, L. 1988. Spatial Econometrics: Methods and Models. Studies in Operational Regional Science. Kluwer Academic Publishers.
  • Fotheringham, A.S., C. Brunsdon and M. Charlton. 2002. Geographically Weighted Regression. The Analysis of Spatially Varying Relationships. John Wiley & Sons Ltd. Chichester, West Sussex.
  • Anselin, L. and S.J. Rey. 2010. Perspectives on Spatial Data Analysis. Springer-Verlag. Berlin.

STA-6621 Categorical Data Analysis

  • Azen, R. and C.M. Walker. 2021. Categorical Data Analysis for the Behavioral and Social Sciences. 2nd ed. Routledge. Taylor & Francis. New York.
  • Hosmer, D.W. and S. Lemeshow. 2000. Applied Logistic Regression. 2nd ed. John Wiley & Sons. Hoboken, New Jersey.
  • Agresti, A. 2019. An Introduction to Categorical Data Analysis. 3rd ed. John Wiley & Sons. Hoboken, New Jersey.
  • Agresti, A. 2010. Analysis of Ordinal Categorical Data. 2nd ed. John Wiley & Sons. Hoboken, New Jersey.
  • Sutradhar, B.C. 2014. Longitudinal Categorical Data Analysis. Springer Science+Business Media. Switzerland.
  • Bilder, C.R. and T.M. Loughin. 2015. Analysis of Categorical Data with R. CRC Press. Taylor & Francis. Boca Raton, Florida.

STA-6622 Multivariate Analysis

  • Rencher, A.C. and W.F. Christensen. 2012. Methods of Multivariate Analysis. 3rd ed. John Wiley & Sons. Hoboken, New Jersey.
  • Johnson, R. and D. Wichern. 2014. Applied Multivariate Statistical Analysis. 6th ed. Pearson Education Limited. Essex.
  • Afifi, A., S. May, R.A. Donatello and V.A. Clark. 2020. Practical Multivariate Analysis. 6th ed. CRC Press. Taylor & Francis. Boca Raton, Florida.
  • Kaufman, L. and P.J. Rousseeuw. 2005. Finding Groups in Data. An Introduction to Cluster Analysis. John Wiley & Sons. Hoboken, New Jersey.
  • Byrne, B.M. 2016. Structural Equation Modeling with AMOS. Basic Concepts, Applications and Programming. 3rd ed. Routledge. Taylor & Francis. New York.
  • Härdle, W.K. and L. Simar. 2019. Applied Multivariate Statistical Analysis. 5th ed. Springer-Verlag. Berlin.
  • Bishop, Y.M., S.E. Fienberg and P.W. Holland. 2007. Discrete Multivariate Analysis. Theory and Applications. Springer Science+Business Media. Switzerland.

STA-6623 Queueing Theory

  • Gnedenko, B.V. and I.N. Kovalenko. 1989. Introduction to Queueing Theory. 2nd ed. Birkhauser. Boston.
  • Gross, D., J.F. Shortle, J.M. Thompson and C.M. Harris. 2008. Fundamentals of Queueing Theory. 4th ed. John Wiley & Sons. Hoboken, New Jersey.
  • Khinchin, A.Y. 2013. Mathematical Methods in the Theory of Queuing. Dover Publications, Inc. Mineola, New York.
  • Dudin, A.N., V.I. Klimenok and V.M. Vishnevsky. 2020. The Theory of Queuing Systems with Correlated Flows. Springer Nature. Switzerland.

STA-6624 Computational Statistics / Statistical Computing

  • Eubank, R.L. and A. Kupresanin. 2011. Statistical Computing in C++ and R. CRC Press. Taylor & Francis. Boca Raton, Florida.
  • Wills, G. 2012. Visualizing Times. Designing Graphical Representations for Statistical Data. Springer Science+Business Media. Switzerland.
  • Kuhnert, P and B. Venables. 2005. An Introduction to R: Software for Statistical Modeling & Computing. Course Materials and Exercises. CSIRO. Australia.
  • Lemmon, D.R. and J.L. Schafer. 2005. Developing Statistical Software in Fortran 95. Springer Science+Business Media. Switzerland.
  • Baragona, R., F. Battaglia and I. Poli. 2011. Evolutionary Statistical Procedures. An Evolutionary Computation Approach to Statistical Procedures Designs and Applications. Springer Science+Business Media. Switzerland.