1. Xiao, Q.; Wang, Y.; Mandal, A. & Deng, X. (2024), ``Modelling and active learning for experiments with quantitative-sequence factors'', Journal of American Statistical Association  --  Theory and Methods. DOI: 10.1080/01621459.2022.2123335
2. Ranjan, P.; Resch, J. &  Mandal, A.  (2023), ``Solving an inverse problem for time series valued computer simulators via multiple contour estimation'', Journal of Statistical Theory and Practice. 17, 23. DOI: 10.1007/s42519-022-00312-5
3. Jankar, J.; Wang, H.; Wilkes, L. R.; Xiao, Q. & Mandal, A. (2022), ``Design and Analysis of Complex Computer Models'', in Advances in Computational Modeling and Simulation, Eds  Srinivas, R.; Kumar, R. and Dutta, M., Springer Nature Singapore, Series: Lecture Notes in Mechanical Engineering.
4. Lukemire, J.; Xiao, Q.; Mandal, A. & Wong, W. K. (2021), ``Statistical analysis of complex computer models in astronomy'', The European Physical Journal Special Topics, 230, 2253 -- 2263.
5. Xiao, Q.; Mandal, A.; Lin, C. D. & Deng, X. (2021) ``EzGP: Easy-to-interpret Gaussian Process models for computer experiments with both quantitative and qualitative factors'', SIAM / ASA Journal on Uncertainty Quantification, 9(2), 333 -- 353.
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. Mandal, A.; Ranjan, P; & Wu, C. F. J. (2009), ``D-SELC: Optimization by Sequential Elimination of Level Combinations using Genetic Algorithms and Gaussian Processes'', Annals of Applied Statistics, 3, 398-421.

Crossover Designs

1. Jankar, J.; Yang, J. & Mandal, A. (2023), ``A general equivalence theorem for crossover designs under generalized linear models'',  Sankhya  --  Series B.
2. Jankar, J. & Mandal, A. (2021), ``Optimal crossover designs for generalized linear models: an application to work environment experiment'', Statistics and Applications,  19(1), 319 -- 336.
3. Jankar, J.; Mandal, A. & Yang, J. (2020), ``Optimal cross-over designs for generalized linear models'',  Journal of Statistical Theory and Practice, 14:23, DOI: 10.1007/s42519-020-00089-5.

Big Data Analytics

1. Meng, C., Xie, R., Mandal, A., Zhang, X., Zhong, W. & Ma, P. (2021), ``LowCon: A design-based subsampling approach in a misspecified linear model'', Journal of Computational and Graphical Statistics, 30(3), 694 -- 708.
2. Meng, C.; Wang, Y.; Zhang, X.; Mandal, A.; Zhong, W.; & Ma, P. (2017) ``Effective Statistical Methods for Big Data Analytics'', in Handbook of Research on Applied Cybernetics and Systems Science, Eds. Saha, S.; Mandal, A.; Narasimhamurthy, A.; Sarasvathi, V. and  Sangam, S. , IGI Global, DOI: 10.4018/978-1-5225-2498-4.ch014.

Algorithmic Searches for Designs

1. Lukemire, J.; Mandal, A. & Wong, W. K. (2020), ``Optimal Experimental Designs for Ordinal Models with Mixed Factors for Industrial and Healthcare Applications'', Journal of Quality Technology, DOI:  10.1080/00224065.2020.1829215.
2. Stokes, Z.; Mandal, A. & Wong, W. K. (2020), ``Using differential evolution to design optimal experiments'',  Chemometrics and Intelligent Laboratory Systems, 199, 103955, DOI: 10.1016/j.chemolab.2020.103955.
3. Lukemire, J.; Mandal, A. & Wong, W. K. (2019), ``D-QPSO: A quantum particle swarm technique for finding D-Optimal designs with mixed factors and a binary response'', Technometrics, 26, 87 -- 105.
4. Mandal, A.; Yu, Y. & Wong, W.-K. (2015), ``Algorithmic Searches for Optimal Designs'', in Handbook of Design and Analysis of Experiments, Eds  Dean, A., Morris, M., Stufken, J. and Bingham, D., Chapman and Hall/CRC, Series: Chapman & Hall/CRC Handbooks of Modern Statistical Methods, 755 -- 783.
5. Johnson, K.; Mandal, A. & Ding, T. (2008) ``Software for Implementing the Sequential Elimination of Level Combinations Algorithm'', Journal of Statistical Software, 25, 1-13.      

Generalized Linear Models and More

1. Yang, J.; Tong, L. & Mandal, A. (2017), ``D-optimal designs with ordered categorical data'', Statistica Sinica,  27, 1879 -- 1902.
2. Yang, J.; Mandal, A. & Majumdar, D. (2016), ``Optimal Designs for 2^k factorial experiments with binary response'',   Statistica Sinica, 26,  385 -- 411.
3. Yang, J. & Mandal, A. (2015), ``D-optimal Designs under Generalized Linear Models'',  Communications in Statistics  --  Simulation and Computation, 44, 2264 -- 2277.
4. Yang, J.; Mandal, A. & Majumdar, D. (2012), ``Optimal Designs for Two-level Factorial Experiments with Binary Response'', Statistica Sinica, 22,  885 -- 907.      

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.
2. Kao, M. H.; Mandal, A & Stufken, J. (2012), ``Constrained Multiobjective Designs for Functional Magnetic Resonance Imaging Experiments via a Modified Non-Dominated Sorting Genetic Algorithm'', Journal of the Royal Statistical Society: Series C (Applied Statistics), 61, 1-20.
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.
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.      
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.      

Choice Experiments

1. 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, $269-295$.

Process Control

1. Dasgupta, T. & Mandal, A. (2008), ``Estimation of process parameters to determine the optimum diagnosis interval for control of defective items'', Technometrics, 50, 167-181.      

Misc. Designs

1. Chowdhury, S.; Lukemire, J. & Mandal, A. (2020), ``A-ComVar: A Flexible Extension of Common Variance Designs'', Journal of Statistical Theory and Practice, 14:16, DOI: 10.1007/s42519-019-0079-y.
2. Kane, A. & Mandal, A. (2020), ``A new analysis strategy for designs with complex aliasing'', The American Statistician, 74 (3), 274 -- 281.
3. Mandal, A. & Mukerjee, R. (2005), ``Design Efficiency under Model Uncertainty for Nonregular Fractions of General Factorials'',  Statistica Sinica, 15, 697-707.      
4. Mandal, A. (2005), ``An Approach for Studying Aliasing Relations of Mixed Fractional Factorials Based on Product Arrays'',  Stat. & Prob. Letters, 75, 203-210.      

Applications in Textile Engineering and Materials Research

1. Nandy, A., Lee, E., Mandal, A., Saremi, R. & Sharma, S. (2020), ``Microencapsulation of retinyl palmitate by melt dispersion for cosmetic application'', Journal of Microencapsulation, 37 (3), 205 -- 219.
2. Lee, B. J.; Daubenmire, S.; Lee, E.; Saremi, R.; Rai, S.; Sriram, T. N.; Mandal, A. and Sharma, S. (2019) ``The optimization of novel nanocellulose gel-reactive dye coating for textile applications'', Colourage, 66 (6), 32 -- 41.
3. Jones, A.; Pant, J.; Lee, A.; 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: Part A, 106, 1535 -- 1542.
4. Jones, A.; Mandal, A. & Sharma, S. (2018), ``Antibacterial and drug elution performance of thermoplastic blends'', Journal of Polymers and the Environment, 26(1), 132 -- 144.
5. Wang, K.; Mandal, A., Ayton, E., Hunt, R., Zeller, A. & Sharma, S. (2016) ``Modification of protein rich algal-biomass to form bio-plastics and odor removal'',  In: Protein Byproducts: Transformation from Environmental Burden Into Value-Added Products, Ed. Dhillon, G.S., Elsevier publishers, 107 -- 117.

6. Jones, A.; Mandal, A. & Sharma, S.  (2015), ``Protein based bioplastics and their antibacterial potential'', Journal of Applied Polymer Science, 132, 41931.

    

1.  Goyal, S.; Datta, G. & Mandal, A. (2021), ``Hierarchical Bayes unit-level small area estimation model for normal mixture populations'',  Sankhya  --  Series B, 83, 215 -- 241.
2. Chakraborty, A.; Datta, G. & Mandal, A. (2019), ``Robust hierarchical Bayes small area estimation for nested error regression model'',  International Statistical Review, 87, 158 -- 176.
3. Chakraborty, A.; Datta, G. & 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. & 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'',  Journal of the American Statistical Association - Theory and Methods, 106, 362-374.      

Drug Discovery

1. 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.      
2. 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.      

Other Applications

1. Wang, H.; Baker, E. W.; Mandal, A.; Pidaparti, R. A.; West, F. D. & Kinder, H. A. (2021), ``Identification of predictive MRI and functional biomarkers in a pediatric piglet traumatic brain injury model'', Neural Regeneration Research, 16(2), 338 -- 344.
2. Kaimal, A.; Al Mansi, M.;  Bou Dagher, J.;  Pope, C.; Varghese, M.; Rudi, T.; Almond, A.; Cagle, L.; Beyene, H.; Bradford, W.; Whisnant, B.; Bougouma, B.; Rifai, K. J.,  Chuang, Y-J.;   Campbell, E.; Mandal, A.; MohanKumar, P. & MohanKumar, S. (2021), ``Prenatal exposure to bisphenols affects pregnancy outcomes and offspring development in rats'', Chemosphere, 276, 130118.
3. Bou Dagher, J.; Hahn-Townsend, C.; Kaimal, A.;  Al Mansi, M.; Henriquez, J.; Tran, D.; Laurent, C.; Bacak, C.; Buechter, H.; Cambric, C.;  Spivey, J.;  Chuang, Y-J.;
   Campbell, E.; Mandal, A.; MohanKumar, P. & MohanKumar, S. (2021), ``Independent and combined effects of Bisphenol A and Diethylhexyl Phthalate on gestational outcomes and offspring development in Sprague-Dawley rats'', Chemosphere, 263, 128307.
4. 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.      
5. Mandal, A. & Sengupta, D. (2000), ``Fatal accidents in Indian Coal Mines'',  Calcutta Statistical Association Bulletin,  50,  95-120.

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. M.; Mandal, A.; Seymour, L.; McDowell, J. & Lazar, N. A., ``Social Network Models for Identifying Active Brain Regions from fMRI Data''.
2. Chakraborty, A.; Lukemire, J.; Mandal, A. & Johnson, K., ``In Search of Desirable Compounds''. 7. Software
3. Li, J; Xiao, Q.; Mandal, A.; Lin, C. D. & Deng, X. (2023), EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments, R Library \urlhttps://cran.r-project.org/web/packages/EzGP/index.html.
4. Wang H.; Xiao, Q. & Mandal, A. (2021), LHD: Latin Hypercube Designs (LHDs), R Library \urlhttps://cran.r-project.org/web/packages/LHD/index.html,  22,776 cumulative downloads as of August 23, 2023.
5. Wang H.; Xiao, Q. & Mandal, A. (2021), LA: Lioness Algorithm (LA), R Library \urlhttps://cran.r-project.org/web/packages/LA/index.html,  3,843 cumulative downloads as of 3/20/2022.

1.  Li, J; Xiao, Q.; Mandal, A.; Lin, C. D. & Deng, X. (2023), EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments, R Library https://cran.r-project.org/web/packages/EzGP/index.html.
2. Wang H.; Xiao, Q. & Mandal, A. (2021), LHD: Latin Hypercube Designs (LHDs), R Library https://cran.r-project.org/web/packages/LHD/index.html,  22,776 cumulative downloads as of August 23, 2023.
3. Wang H.; Xiao, Q. & Mandal, A. (2021), LA: Lioness Algorithm (LA), R Library https://cran.r-project.org/web/packages/LA/index.html,  3,843 cumulative downloads as of March 20, 2022.