Design of Experiments and Statistical Process Control
1. Chowdhury, S.; Lukemire , J. & Mandal, A. (2019), A-ComVar: A Flexible Extension of Common Variance Designs, submitted.
2. Kane, A. & Mandal, A. (2019), A new analysis strategy for designs with complex aliasing. The American Statistician, accepted. (Codes)
3. Lukemire , J.; Mandal, A. & Wong, W. K. (2019), d-QPSO: A Quantum-Behaved Particle Swarm Technique for Finding D-Optimal Designs for Models with Mixed Factors and a Binary Response Technometrics, 26, 87-105.
4. Zhang, W., Mandal, A. & Stufken, J. (2017), Approximations of the information matrix for a panel mixed logit model. Journal of Statistical Theory and Practice, 11, 269-295.
5. Yang, J.; Tong, L. & Mandal, A. (2017), D-optimal designs with ordered categorical data. Statistica Sinica, 27, 1879-1902.
6. Yang, J.; Mandal, A. & Majumdar, D. (2016), Optimal Designs for 2k Factorial Experiments with Binary Response. Statistica Sinica, 26, 385-411.
7. Yang, J. & Mandal, A. (2015), D-Optimal Factorial Designs under Generalized Linear Models. Communications in Statistics, 44, 2264-2277.
8. Yang, J.; Mandal, A. & Majumdar, D. (2012), Optimal Designs for Two-level Factorial Experiments with Binary Response. Statistica Sinica, 22, 885-907.
9. Dasgupta, T. & Mandal, A. (2008), Estimation of Process Parameters to Determine the Optimum Diagnosis Interval for Control of Defective Items, Technometrics, 50, 167-181.
10. Mandal, A. & Mukerjee, R. (2005), Design Efficiency under Model Uncertainty for Nonregular Fractions of General Factorials, Statistica Sinica, 15, 697-707.
11. Mandal, A. (2005), A Friendly Approach to Studying Aliasing Relations of Mixed Factorials in the Form of Product Arrays, Stat. Prob. Letters, 75, 203-210.
Small Area Estimation
1. Goyal, S.; Datta, G. and Mandal, A. (2019), A Hierarchical Bayes Unit-Level Small Area Estimation Model for Normal Mixture Populations, submitted.
2. Chakraborty, A.; Datta, G. and Mandal, A. (2019), Robust hierarchical Bayes small area estimation for nested error regression model, International Statistical Review, DOI: 10.1111/insr.12283.
3. Chakraborty, A.; Datta, G. and Mandal, A. (2016), A two-component normal mixture alternative to the Fay-Herriot model, Joint issue of Statistics in Transition new series and Survey Methodology Part II, 17, 67-90.
4. Datta, G. and Mandal, A. (2015), Small Area Estimation with Uncertain Random Effects, Journal of the American Statistical Association - Theory and Methods, 110, 1735-1744.
5. Datta, G.; Hall, P.; & Mandal, A. (2011), Model selection by testing for the presence of small-area effects in area-level data. (Supplementary materials) Journal of the American Statistical Association - Theory and Methods, 2011, 362-374.
Functional Magnatic Resonance Imaging (fMRI)
1. Kao, M. H.; Majumdar, D.; Mandal, A & Stufken, J. (2013) Maximin and Maximin-Efficient Event-Related fMRI Designs under a Nonlinear Model. Annals of Applied Statistics, 7, 1940-1959. (supplementary materials)
2. Kao, M. H.; Mandal, A & Stufken, J. (2012) Constrained Multi-objective Designs for Functional MRI Experiments via A Modified NSGA-II. Journal of the Royal Statistical Society: Series C (Applied Statistics), 61, 515-534. Matlab Codes
3. Kao, M. H.; Mandal, A & Stufken, J. (2009), Efficient Designs for Event-Related Functional Magnetic Resonance Imaging with Multiple Scanning Sessions, Communications in Statistics - Theory and Methods: Celebrating 50 Years in Statistics Honoring Professor Shelley Zacks, 38, 3170-3182. Matlab Codes
4. Kao, M. H.; Mandal, A; Lazar, N; & Stufken, J. (2009), Multi-objective Optimal Experimental Designs for Event-Related fMRI Studies, NeuroImage, 44, 849-856. Technical Report Matlab Codes
5. Kao, M. H.; Mandal, A & Stufken, J. (2008), Optimal Design for Event-related Functional Magnetic Resonance Imaging Considering Both Individual Stimulus Effects and Pairwise Contrasts, Special Volume of Statistics and Applications in Honour of Professor Aloke Dey, 6, 225-241.
1. Mandal, A.; Ranjan, P; & Wu, C. F. J. (2009), G-SELC: Optimization by Sequential Elimination of Level Combinations using Genetic Algorithms and Gaussian Processes, Annals of Applied Statistics, 3, 398-421.
2. Johnson, K; Mandal, A; & Ding, T. (2008), Software for Implementing the Sequential Elimination of Level Combinations Algorithm, Journal of Statistical Software, 25, 6, 1-13. Matlab codes, SAS codes, R codes, Initial Design, Forbidden Array.
3. Mandal, A; Johnson, K; Wu, C. F. J.; & Bornemeier, D. (2007), Identifying Promising Compounds in Drug Discovery: Genetic Algorithms and Some New Statistical Techniques, Journal of Chemical Information and Modeling, 47, 981-988. DDN News
4. Mandal, A.; Wu, C.F.J. & Johnson, K. (2006), SELC : Sequential Elimination of Level Combinations by means of Modified Genetic Algorithms, Technometrics, 48, 273-283. (slides)
1. Banik, P.; Mandal, A. & Rahaman, S. (2002), Markov Chain Analysis of Weekly Rainfall Data in Determining Drought-proneness, Discrete Dynamics in Nature and Society, 7, 231-239.
3. Jones, A.; Mandal, A. & Sharma, S. (2015), Protein based bioplastics and their antibacterial potential, Journal of Applied Polymer Science, 132, 41931.
4. Jones, A.; Mandal, A. & Sharma, S. (2017), Antibacterial and drug elution performance of thermo-plastic blends, Journal of Polymers and the Environment, https://doi.org/10.1007/s10924-016-0924-y.
5. Jones, A., Pant, J., Lee, E., Goudie, M., Gruzd, A., Mansfield, J., Mandal, A., Sharma, S. & Handa, H. (2018) Nitric oxide-releasing antibacterial albumin plastic for biomedical applications, Journal of Biomedical Materials Research, 106, 1535-1542.
6. Bhattacharjeea, N.; Ranjan, P.; Mandal, A. & Tollner, E. W. (2019), A history matching approach for calibrating hydrological models, Environmental and Ecological Statistics, 26, 87-105.
7. Chakraborty, J.; Mandal, A. & Finkelman, R. B. (2019), Association between geogenic organic contaminants in groundwater from Carrizo-Wilcox aquifer and the incidence of renal diseases: a preliminary study in east Texas, submitted.
1. Meng, C., Wang, Y., Zhang, X., Mandal, A. & Ma, P. (2016) Effective Statistical Methods for Big Data Analytics, in Handbook of Research on Applied Cybernetics and Systems Science, IGI Global.
2. Mandal, A.; Wong, W. K. & Yu, Y. (2014) Algorithmic Searches for Optimal Designs, in Handbooks on Modern Statistical Methods, Chapman & Hall/CRC.
3. Wang, K., Mandal, A., Ayton, E., Hunt, R., Zeller, A. & Sharma, S. (2015) Modification of protein rich algal-biomass to form bio-plastics and odor removal, to appear in "Modification of waste derived proteins products for high value applications", In: Waste-derived proteins: Transformation from environmental burden into value-added products, Ed. Dhillon, G.S., Elsevier publishers.
1. Mandal, A. (2008), Matrix Algebra: Theory, Computations, and Applications in Statistics by James E. Gentle, Journal of the American Statistical Association, 103, 1716-1717.
1. Bargo, A.; Mandal, A.; Seymour, L.; McDowell, J.; & Lazar, A. (2011), Social network models for identifying active brain regions from fMRI data.
2. Chakraborty, A.; Mandal, A.; & Johnson, K. (2013), In Search of Desirable Compounds.