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Department of Mathematics

Department of Mathematics

Scholarly Activity Publications

Faculty: Featured work and Honors

Please click on the faculty name to learn more about each faculty's featured work and honors

Bibliography And Select Publications

Books

  • Ahangar, R. and Wang, R. “Algebra Lab Projects, for Schools, Colleges and Universities, Using Integrated Technology”, 2010, Kendall Hunt Publishing Company. ISBN-978-0-7575—7763-5 
     
  • Ahangar, Reza, “Hands on Learning Approach for Differential and Integral Calculus Using Integrated Technology Projects: Spreadsheet, Graphing Calculator, and Computer Algebra Systems”, Linus Publishing Company, Jan, 2016, ISBN-10 1-60797-559-9, ISBN-13 978-1-60797-595-5 

Master Thesis  

  • Erik J. Harwell, Complex Matter Space: A New Perspective on Mass, Charge, and Energy”, Mathematics Department, Texas A & M University Kingsville, Aug. 2016 

Refereed Journal Articles

  • Reza R. Ahangar, “Stability and Asymptotic Behavior of the Causal Operator Dynamical Systems Using Nonlinear Variation of Parameters”, Journal of Progressive Research in Mathematics (JPRM) ISSN: 2395-0218, Volume 13, Issue1, Jan. 30, 2018. See www.scitecresearch.com/journals/index.php/jprm 2170, and  
      http://www.scitecresearch.com/journals/index.php/jprm/article/view/1364/996 
     
  • Reza R. Ahangar,  (2017), “Variation of Parameters for Causal Operator Differential Equations.”, Applied Mathematics, 8, 1883-1902. https://doi.org/10.4236/am.2017.812134 and http://www.scirp.org/journal/AM/ 
  • Reza R. Ahangar, “The Relativistic Geometry of the Complex Matter Space”, Journal of Applied Mathematics and Physics, Vol.05 No.02(2017), Article ID:74199,17 pages, 10.4236/jamp.2017.52037 
  • Reza Ahangar, Jeong Yang, Young Lee, Sung Park, Monica Wong-Ratcliff, Marie Ann Mundy,  "Discovering the Needs Assessment of Qualified STEM Teachers for the High Need Schools in South Texas",  Journal of STEM Education, Volume 16, issue 4, October-December 2015. 
  •  Ahangar R. R. and Wang R., “Computational Approach to Control Laws of Strict-Feedback Nonlinear Systems” Open Access Library Journal, 2016, Volume 3, e2936. http://dx.doi.org/10.4236/oalib.110293
  • Ahangar, Reza;  "Foundation of Complex Matter Space and Special Theory of Relativity, a Unifying Approach", Journal of Nuclear and Particle Physics, Paper ID: 102900116, p-ISSN: 2167-6895,    e-ISSN: 2167-6909, 2014;  4(5): 147-153 
  • Ahangar Reza; "Quantum Complex Matter Space",  International Journal of Theoretical and Mathematical Physics, 2167-6852, 2014, 4(4): 159-163. Paper ID: 107000150 
  • Ahangar, Reza; "Complex Matter Space and Relativistic Quantum Mechanics", Applied Mathematics, www.scirp.org/journal/am, Paper ID: 740-2454, Journal ID:29. 
    http://file.scirp.org/Html/11-7402454_52220.htm  
  • Ahangar Reza: “Computation and Simulation of Langevin Stochastic Differential Equation”, The Journal of Combinatorial Mathematics and Combinatorial Computing, JCMCC 86, (2013), pp. 183-198.  
  • Ahangar Reza, “Harmonic Probability Density Function, Modeling, and Regression”, European International Journal of Science and Technology”,  ISSN:2304-9693, (2013), Vol. 2, pp.164-172.  
  •  Ahangar, R., Wang, R., “Symbolic Computation for Integrator Back-stepping Control Laws”, JMCC 74, (2010), pp. 33-42. 
  •  Ahangar, R, Singh S., Wang, R. “Dynamic Behavior of Perturbed Logistic Model”, The Journal of Combinatorial Mathematics and Combinatorial Computing, (JCMCC 74, (2010), pp.295-311.  
  • R. Ahangar (KWU), K. Iqbal (UALR), and A..M. Mughal (UALR)  “Finite Difference Simulation of a Multistage Carcinogenesis Model”,   “Transaction on Mathematics and Computers in Biology and Medicine”, World Scientific and Engineering Academy and Society (WSEAS), 2006. 
  •  R. Ahangar, Nawab Ali, K. Altmayer, and K. Iqbal “Biodynamic Computation, Simulation and Modeling of Multistage Mutations”. The Journal of DNA and Cell Biology, Volume 23, Number 10, 2004, Mary Ann Liebert, Inc. Pp. 625-633.    
  • R. Ahangar: "Non-anticipating Dynamical Model and Optimal Control", Applied Mathematics Letter, Vol.2, No.1, pp.15-18, 1989. 
  •  R. Ahangar and E. Salehi (Dept of Math UNLV, Las Vegas, Nevada): "Automatic Controls for Nonlinear Dynamical Systems with Lipschitzian Trajectories”  Journal of Mathematical Analysis and Application (JMAA), 268, pp. 400-405 (2002). 

Refereed Proceedings 

  •  Ahangar Reza: "Symmetric Group Structures of Genetic Transformation", Proceedings of WorldComp 2013, Las Vegas, July 22-25, 2013, BIOCOMP 2013. 
  •  “Engineering Mathematics Using Integrated Technology”, Linus Publishing Company, August 2013. 
  •  Ahangar R. R. “Introduction to the Statistical Geometry of Space”, Proceedings of WorlComp 2010 Conference, Las Vegas, June 12-15, 2010. 
  • R. Ahangar, “Optimal Control Solution to Operator Differential Equations Using Dynamic Programming”, Proceedings of the 2005 International Conference on Scientific Computing, Las Vegas, Nevada, June 20-23, 2005, pp. 16-22.
  • R. Ahangar and K. Iqbal “Biodynamic Modeling and Simulation of Multistage Carcinogenesis”, Proceeding of the 26th Annual International of the IEEE EMBS, San Francisco, CA USA, September 1-5, 2004, pp. 3023-3026. 
  • R. Ahangar and X. B. Lin (NCSU, Raleigh, NC), “Multistage Evolutionary Model for Carcinogenesis Mutations”, Proceeding of Electronic Journal of Differential Equations, The Fifth Mississippi State Conference, on Differential Equations and Computational Simulations, Conference 10,2003, pp. 33-53. 
  • R. Ahangar “Computer Simulation and Modeling of Multistage Carcinogenesis Mutations”.  Dynamic Publishers, Inc., USA. Proceedings of Dynamic Systems and Applications 4 (2004) 519-527. 
  • R. Ahangar and E. Salehi (UNLV, Las Vegas, Nevada):  "Optimal Automatic Controls Solution to Nonlinear Operator Dynamical Systems", Proceeding of The International Conference on Scientific Computing, (CSC-06), June 26-29, 2006, Las Vegas, Nevada.
  • R. Ahangar, “Optimal Control Solution to Nonlinear Causal Operator Systems with Target State”, FCS (Foundations of Computer Science), WORLD COMP 2008, pp. 218-223. 

Areas Of Research In Progress

  • Control Systems: Deterministic and Stochastic Optimal Control, Automatic Controls of Nonlinear Dynamical Systems. 
  • Stability of Dynamical Systems: Stability of the Systems of Operator Differential Equations, and Stochastic Variation of Parameters. 
  • Computational Mathematics: Numerical Methods for solutions to Dynamical Systems and Computer Simulation Modeling. 
  • Mathematical Biology and Modeling: Population Dynamics, Diabetes, Epidemics, and Stochastic Simulation of Multistage of Carcinogenesis.  
  • Mathematics Education:  Undergraduate Research Projects in Math  Modeling and Geometry, Educational Technology, Curriculum  Enhancement, and  Experimental Projects for learning mathematics on the web.  

Collaborative Research

  • Victor Bogdan: (Dissertation Advisor) Professor Emeritus, The Catholic University of America, Washington D.C. 20064, (w) 202-319-5221, bogdan@cua.edu (H): 163 Jasmine Court, Mountain View, CA 94043, (650)-960-1759.    
  • Jay Jahangiri: Department of Mathematics, Kent State University, Geauga Campus, 14111,  Claridon-Troy Road, Burton, Ohio 4401. (440)951-1477, (440)834-4187. E-mail:  Jay@geauga.kent.edu, Topic: Geometry of Bipolar System 
  • Xiao-Biao Lin: Math Department, North Carolina State University, Raleigh, NC 27695-8205, (919)-515-7440 or (919)-515-2382. xblin@math.ncsu.edu. Topic: Multistage Carcinogenesis Mutations. 
  • Iqbal Kamran: Associate Professor, Systems Engineering Department, University of Arkansas at Little Rock, 2801 South University, Little Rock, AR, 72204-1099. Phone:(501)-371-7617
    e-mail:kxiqbal@ualr.edu
  • R. Ahangar and W. Rush:  (St. Petersburg College, St. Petersburg, FL), “Introduction to Statistical Geometry”, Presented AMS Annual Meeting, New Orleans, Jan 10-14, 2001.  
  • R. Ahangar: "Stochastic Variation of Parameters for Random Operator Differential Equations", to be presented/submitted.
  • R. Ahangar and M. J. Jahangiri:  "Geometries of Bipolar Coordinate Systems in Real and Complex Plane", To be presented / Submitted.  
  • R. Ahangar: "Damped Hyperbolic Operator Differential Equations", To be presented/submitted.
  • R. Ahangar: “Statistical Logic”, a new area of research interest which bridges the gap between the formal Aristotelian logic and uncertainty principle. This is a probabilistic view of induction, deduction, and development of the statistical syllogism

Selected Publications:

  • A.O. Ahmed, 2013: Quantum Games and Quaternionic Strategies, Quantum Information Processing, Springer, DOI 10.1007/s11128-013-0553-5.
  • A.O. Ahmed, 2013: Equilibria in Quantum Three-Player Dilemma Game, British Journal of Mathematics & Computer Science, Science Domain International.
  • A. Atangana, A.O.Ahmed, and N. Bildik, 2013: A Generalized Version of a Low Velocity Impact between a Rigid Sphere and a Transversely Isotropic Strain-Hardening Plate Supported by a Rigid Substrate Using the Concept of Non-Integer Derivatives, Abstract and Applied Analysis.

Research Book/Monograph:

  • A. O. Ahmed, 2011: On Quaternions, Octonions, and the Quantization of Games: A Text on Quantum Games, Lambert Academic Publishing, ISBN: 978-3-8433-9145-0.

Supervised MS Thesis

Shaik Azharuddin (2012). Best Response Analysis in Two-Player Two Strategy Maximally Entangled Quantum Games. M.Sc. thesis submitted to the Department of Mathematics, Texas A&M University-Kingsville, Kingsville, TX 

Selected Publications:

  • S. Hodis, 2018. Correlation of flow complexity parameter with aneurysm rupture status, International Journal for Numerical Methods in Biomedical Engineering, https://doi.org/10.1002/cnm.3131
  • S. Hodis, 2018. Pulse wave velocity as a diagnostic index: The effect of wall thickness, Physical Review E 97,062401.
  • S. Hodis, S. Kargar, D.F. Kallmes, and D. Dragomir-Daescu, 2015 Artery length sensitivity in patient specific cerebral aneurysms simulations, Amerian Journal of Neuroradiology 36: 737-743.
  • S. Hodis, D.F. Kallmes and D. Dragomir-Daescu, 2013. Adaptive grid generation in a patient-specific cerebral aneurysm, Physical Review E 88, 052720.
  • S. Hodis and M. Zamir, 2011. Mechanical events within the arterial wall under the forces of pulsatile flow: A review, Journal of Mechanical Behavior of Biomedical Materials 4(8): 1595-1602.
  • S. Hodis and M. Zamir, 2008. Solutions of the Maxwell Viscoelastic Model Equations for displacement and stress distributions within the arterial wall, Physical Review E 78, 021914.

Book

  • R. Agarwal, S. Hodis, and D.O' Regan, 2019. 500 Examples and Problems of Applied Differential Equations (Springer, New York)

Selected Publications

Kim D, Shin D-H, Sung CK. The Optimal Balance between Oncolytic Viruses and Natural Killer Cells: A Mathematical Approach. Mathematics. 2022; 10(18):3370. https://doi.org/10.3390/math10183370 

Dong-Hoon Shin, Dongwook Kim, Changki Kim,  Application of an Optimal Pairs Trading Strategy under Geometric Brownian Motions and Cointegration test to the S&P100, Journal of Business and Social Science Review. Vol. 2, No. 3, March 2021, pp. 1-13. Doi: 10.48150/jbssr.v2no3.2021.a1 

Dongwook Kim, Haeyoung Kim, Hui Wu, Dong-Hoon Shin, The effect of the Infection Rate on Oncolytic Virotherapy, Computational Biology and Bioinformatics. Vol. 8, No. 1, 2020, pp. 20-28. Doi:10.11648/j.cbb.20200801.14.

Jun K, Kim D (2018) Alignment theory of parallel-beam computed tomography image reconstruction for elastic-type objects using virtual focusing method. PLoS ONE 13(6): e0198259

Dong-Hoon Shin and Dongwook Kim, Rumor Effects Modeling of a Mean Reverting Asset and Options Pricing, Far East Journal of Mathematical Sciences, Volume 102, Issue 9, 1831 - 1856 (November 2017)

Craig Schindewolf, Dongwook Kim, Andrea Bel and Horacio G.Rotstein, Complex patterns in networks of hyperexcitable neurons, Theoretical Computer Science C - Natural Computing, Volume 633,20 June 2016, Pages 71-82.

Selected Publications:

  • Muzheve, M. T. & Capraro, R. M. (In Press). An exploration of the role natural language and idiosyncratic representations in teaching how to convert among fractions, decimals, and percents. Journal of Mathematical Behavior, 31, 1-14.
  • Muzheve, M. T. (2011). Transformations in PowerPoint. Learning and Teaching Mathematics, 11, 42-43.
  • Fleischner, H., Hobbs, A., & Muzheve, M. T. (2009). Hamiltonicity in vertex envelopes of plane cubic graphs. Discrete Mathematics, 309(14), 4793-4809.

Book chapters:

  • Muzheve, M. T. (2014). Cooperative grouping, Mathematics. In C. R. Reynolds, K. J. Vannest, & E. Fletcher-Janzen (Eds.), Encyclopedia of special education: A reference for the education of children, adolescents, and adults with disabilities and other exceptional individuals (4th ed., pp. XXX-XXX). Hoboken, NJ: John Wiley & Sons. 
  • Capraro, M. M., & Capraro, R. M., Muzheve, M. T. (2013).  The private sector, building STEM partnerships, and moving models forward. In M. M. Capraro, R. M. Capraro, & C. W. Lewis (Eds.). Improving urban schools: Equity and access in k-16 STEM education for all students (pp. 25-38). Charlotte, NC: Information Age.

Selected Publications:

  • Singh, S. (2013). A dual problem of calibration of design weights. Statistics: A Journal of Theoretical and Applied Statistics 47 (3), 566-574.
  • Singh, S. (2012). On the calibration of design weights using a displacement function. Metrika, 75, 85-107.

Research books/monographs:

  • A New Concept for Tuning Design Weights in Survey Sampling: Jackknifing in Theory and Practice Authors: Sarjinder Singh, Stephen A. Sedory, Maria Rueda, Antonio Arcos and Raghunath Arnab Accepted: Feb 2015 by the publisher ELSEVIER (In press).
  • Singh, S. (2003). Advanced Sampling Theory with Applications: How Michael “selected” Amy Vol. 1 & 2, pp. 1-1247, Kluwer Academic Publisher, The Netherlands.

Textbook:

  • Singh, S. (2006). Thinking Statistically: Elephants Go to School pp.1-676, Kendall/Hunt Publishing Company, Iowa, USA.

Other Publications:

  • Singh, H.P., Tailor, R. and Singh, S. (2012). General procedure for estimating the population mean using ranked set sampling. Journal of Simulation and Computation Statistics, iFirst, 2012, 1–15
  • Singh, H.P., Chandra, P., Grewal, I.S., Singh, S., Chen, C.C. Sedory, S.A., and Kim, J.-M. (2012). Estimation of population ratio, product, and mean using multi-auxiliary information with random non-response. Rivista Statistica (In press)
  • Singh, S., Sedory, S.A, and Kim, Jong-Min (2012). An empirical likelihood estimate of the finite population correlation coefficient. Communications in Statistics: Simulation and Computation (In press).
  • Singh, H.P., Solanki, R.S. and Singh, S. (2012). Estimation of Bowley’s coefficient of Skewness in the presence of auxiliary information. Communications in Statistics: Theory and Methods (In press)
  • Singh, S. and Sedory, S.A. (2012). A true simulation study of three estimators at equal protection of respondents in randomized response sampling. Statistica Neerlandica, 66 (4), 442-451.
  • Arnab, R., Singh, S. and North, D. (2012). Use of two decks of cards in randomized response techniques for complex survey designs. Communications in Statistics-Theory and Methods, 41:16-17, 3198-3210.
  • Singh, H.P., Singh, S. and Kim, J.-M. (2012). Some Alternative Classes of Shrinkage Estimators for Scale Parameter of the Exponential Distribution. The Korean Journal of Applied Statistics. (Accepted)
  • Abdelfatah, S., Mazloum, R and Singh, S. (2012). Efficient use of two-stage randomized response procedure. Brazilian Journal of Probability and Statistics (In press).
  • Verma, M.R., Singh, S. And Pandey, R. (2012). Optimum stratification for sensitive quantitative variables using auxiliary information. Journal of the Indian Society of Agricultural Statistics (Accepted).
  • Chen, C.C. and Singh, S. (2012). Estimation of Multinomial Proportions Using Higher Order Moments of Scrambling Variables in Randomized Response Sampling. . J. of Modern Applied Statistical Meth (In press)
  • Singh, S. and Grewal, I.S. (2012). Estimation of finite population variance using partial jackknifing. Journal of the Indian Society of Agricultural Statistics (Accepted).
  • Ahangar, R., Wang, R., Perez, J. and Singh, S. (2010). Extensive study of logistic regression using randomized response sampling. AMSE Journals (In press)
  • Rueda, M., Arcos, A, Arnab, R and Singh, S. (2011). The Rao, Hartley and Cochran scheme with dubious random non-response in survey sampling. Sankhya (In press)
  • Singh, S. (2011). A dual problem of calibration of design weights. Statistics: A Journal of Theoretical and Applied Statistics (In press)
  • Singh, S. and Arnab, R. (2011). On the calibration of design weights. Metron vol. LXIX, n. 2, pp. 185-205
  • Arnab, R. and Singh, S. (2011). Estimation of Mean of Sensitive Characteristics for Successive Sampling. Communications in Statistics- Theory and Methods (In press)
  • Singh, S. and Sedory, S. A. (2011). Cramer-Rao lower bound of variance in randomized response sampling. Sociological Methods and Research, 40(3) 536–546.
  • Singh, H.P., Tailor, R, Singh, S. and Kozak, M. (2011). A generalized method of estimation of a population parameter in two-phase and successive sampling. Qual-Quant (DOI: 10.1007/s11135-011-9623-x)
  • Singh, S. and Kim, J.K. (2011). A pseudo-empirical log-likelihood estimator using scrambled responses. Statistics and Probability Letters, 81, 345-351.
  • Singh, S. and Sedory, S. A. (2011). Sufficient Bootstrapping. Computational Statistics and Data Analysis 55(1), 1629-1637
  • Singh, S. (2012). On the calibration of design weights using a displacement function. Metrika 75:85–107.
  • Pal, S. and Singh, S. (2012). A new unrelated question randomized response model. Statistics: A Journal of Theoretical and Applied Statistics (In press)
  • Land, M., Singh, S, and Sedory, S.A. (2012). Estimation of a rare sensitive attribute using Poisson distribution. Statistics: A Journal of Theoretical and Applied Statistics, 46(3), 351-360.
  • Abdelfatah, A., Mazloum, R. and Singh, S. (2011). An alternative randomized response model using two decks of cards. Statistica, LXXI (3), 381-390.
  • Singh, S. (2010). Proposed optimal orthogonal new additive model. Statistica, LXX, 1, 73-81
  • Ahangar, R., Singh, S. and Wang, R. (2010). Dynamic behavior of perturbed logistic model. Journal of Combinatorial Mathematics and Combinatorial Computing (JCMCC) 74, 295-311.
  • Vishwakarma, G.K., Singh, H.P. and Singh, S. (2010). A family of estimators of population mean using multi-auxiliary variate and post-stratification. Nonlinear Analysis: Modelling and Control, 15, 2, 233-253.
  • Singh, H.P., Tailor, R., Singh, S. and Kim, J.-M. (2011). Estimation of population variance in successive sampling. Quality and Quality, 45(3), pp. 477494
  • Farrell, P.J. and Singh, S. (2010). Some contributions to Jackknifing two-phase sampling estimators. Survey Methodology, 36, 1, 57-68.
  • Arnab, R. and Singh, S. (2010). Variance estimation of a generalized regression predictor. Journal of the Indian Society of Agricultural Statistics, 64(2), 273-288.
  • Singh, S. and Arnab, R. (2010). Bias-adjustment and calibration of Jackknife variance estimator in the presence of non-response Journal of Statistical Planning and Inference, 140(4), 862-871.
  • Arnab, R. and Singh, S. (2010). Randomized response techniques: An application to the Botswana AIDS impact survey. Journal of Statistical Planning and Inference, 140(4) 941–953
  • Singh, H.P., Singh, S. and Kim, J.-M. (2010). Efficient Use of Auxiliary Variables in Estimating Finite Population Variance in Two-Phase Sampling. Commun. of the Korean Statistical Society,17(2), 165–181.
  • Singh, S., Rueda, Mari Del Mar and Sanchez-Borrego, Ismael (2010).Random non-response in multi-character surveys. Quality& Quantity, 44, 345-356.
  • Singh, S. (2009). Saddlestrapping. Nonlinear Analysis: Modelling and Control, 14 (3), 357–388
  • Singh, S. and Chen, C. (2009). Utilization of higher order moments of scrambling variables in randomized response sampling. Journal of Statistical Planning and Inference, 139, 3377-3380.
  • Singh, S., Singh, H.P., Tailor, R., Allen, J. and Kazak, M. (2009) Estimation of ratio of two finite -population means in the presence of non-response. Commun in Stat-Theory and Methods,38, 3608-21
  • Singh, S. (2009). A new method of imputation in survey sampling. Statistics: A Journal of Theoretical and Applied Statistics, 43(5),499 - 511
  • Sidhu, S.S., Bansal, M.L., Kim, J.M. and Singh, S. (2009). Unrelated question model in sensitive multi-character surveys. Korean Communication Journal of Statistics, 16( 1 ), 169–183.
  • Singh, S. and Valdes, S. (2009). Optimum method of imputation in survey sampling. Applied Mathematical Sciences,3(35),1727-1737.
  • Sidhu, S.S., Tailor, R and Singh, S. (2009). On the estimation of population proportion. Applied Mathematical Sciences, 3(35),1739-1744.
  • Singh, G.N., Priyanka, K., Kim, J.M. and Singh, S. (2009). Estimation of population mean using imputation techniques in sample surveys. Journal of the Korean Statistical Society, (In press).
  • Singh, S., Kim, J.-M. and Grewal, I.S. (2008). Imputing and Jackknifing scrambled responses. Metron, LXVI (2), 183-204.
  • Singh, H.P., Tailor, R. and Singh, S. and Kim, J.-M. (2008). A modified estimator of population mean using power transformation” Statistical Papers, 49: 37-58.
  • Kozak, M., Zielinski, A. and Singh, S. (2008). Stratified two-stage sampling in domains: Sample allocation between domains, strata and sampling stages. Statistics and Probability Letters, 78: 970-974.
  • Singh, H.P., Singh, S. and Kozak, M. (2008). A family of estimators of finite-population distribution function using auxiliary information. Acta Appl Math. 104: 115–130.

Book chapters jointly with MS students:

  • Lee, C.S., Sedory, S.A. and Singh, S. (2015).Cramer-Rao lower bounds of variance for estimating two proportions and their overlap by using two-decks of cards. Accepted by ELSEVIER.
  • Su, Cing-Shu, Sedory, S.A. and Singh, S. (2015). Estimation of means of two rare sensitive characteristics. Accepted by ELSEVIER.
  • Johnson, M.L, Sedory, S. and Singh, S. (2015). Incredibly efficient use of a Negative Hypergeometric distribution in randomized response sampling. Accepted by ELSEVIER.
  • Mohamed, C., Sedory, S.A. and Singh, S. (2015). Comparison of different imputing methods for scrambled responses. Accepted by ELSEVIER.

Peer-Reviewed Publications from MS theses:

  • Lee, Cheon-Sig, Su, Ching-Su, Mondragon, K., Salinas, V.I., Zamora, M.L., Sedory. S.A. and Singh, S. (2015). Comparison of Cramer-Rao lower bounds of variances for at least equal protection of respondents. Statistica Neerlandica (Accepted)
  • Su, Ching-Su, Sedory, S.A. and Singh, S. (2015). Adjusted Kuk’s model using two non-sensitive characteristics unrelated to the sensitive characteristic. Communications in Statistics-Theory and Methods (Accepted)
  • Su, Shu-Ching, Sedory, S.A. and Singh, S. (2015). Kuk’s model adjusted for protection and efficiency. Sociological Methods and Research, 44(3) 534-551
  • Lee, Cheon-Sig, Sedory, S.A. and Singh, S. (2013). Simulated minimum sample sizes for various randomized response models. Communications in Statistics: Sim. and Comp, 42(4), 771-789.
  • Lee, Cheon-Sig, Sedory, S.A. and Singh, S. (2013). Estimating at least seven measures for qualitative variables using randomized response sampling. Statistics and Probability Letters, 83,399-409.

Peer-Reviewed Publications from Undergraduate Projects:

  • Dykes, Lee, Singh, S., Sedory, S.A. and Luis, V. (2014). Calibrated estimators of population mean for a mail-survey design. Communications in Statistics: Theory and Methods (In press).
    Gjestvang, C.R and Singh, S. (2006). A new randomized response model. J. R. Statist. Soc., B, 68, 523-530.
  • Gjestvang, C. and Singh, S. (2007). Forced Quantitative Randomized Response Model: A new device. Metrika, 66, 2, 243-257.
  • Gestavang, C. and Singh, S. (2009). An improved randomized response model: Estimation of mean. Journal of Applied Statistics,36(12),1361–1367
  • Odumade, O. and Singh, S. (2008). Generalized forced quantitative randomized response model: A unified approach. Journal of the Indian Society of Agricultural Statistics, 62(3), 244-252.
  • Odumade, O. and Singh, S. (2009).Efficient use of two decks of cards in randomized response sampling. Commun. Statist.-Theory Meth., 38: 439–446.
  • Odumade, O. and Singh, S. (2009). Improved Bar-lev, Bobovitch and Boukai randomized response models. Commun. Statist.-Simulation, 38: 473–502.
  • Odumade, O. and Singh, S. (2010). An alternative to the Bar-lev, Bobovitch and Boukai randomized response model. Sociological Methods and Research, 39: 206-221
  • Odumade, O. and Singh, S. (2010). Use of two variables having common mean to improve the Bar-Lev, Bobovitch and Boukai Randomized Response Model. J. of Modern Applied Statistical Meth, 9(2), 414-442.
  • Stearns, M. and Singh, S. (2008). On the estimation of the general parameter. Computational
    Statistics and Data Analysis, 52, 4253-4271.

Joint Statistical Meeting Conference Presentations from MS Theses and Undergraduate Projects:

  • Mohamed, C., Sedory, S.A. and Singh, S. (2015). A fresh imputing survey methodology using sensible constraints on study and auxiliary variables. Presented at the JSM 2015, Seattle.
  • Lee, C.S., Sedory, S.A. and Singh, S. (2015). On estimating at least seven measures using randomized response sampling: Cramer-Rao lower bounds of variances. Presented at the JSM 2015, Seattle.
  • Jayaraj, A., Odumade, O., Sedory, S.A. and Singh, S. (2015). A forced odds ratio (to be equal to one) leads to a new estimator for randomized response sampling. Presented at the JSM 2015, Seattle.
  • Lee, Cheon-Sig, Sedory, S.A. and Singh, S. (2014). Black magic using randomized response techniques. Presented at JSM 2014, Boston.
  • Jayaraj, A., Odumade, O. and Singh, S. (2014). A New Quasi-Empirical Bayes Estimate in Randomized Response Technique. Presented at JSM 2014, Boston.
  • Su, Shu-Ching, Sedory, S.A. and Singh, S. (2013). Kuk’s model adjusted for efficiency and protection using two non-sensitive questions unrelated to the characteristic of interest. Presented at the JSM 2013, Montreal, Canada, August 3-8, 2013.
  • Odumade, O., Arnab, R. and Singh, S. (2012). Post-Stratification Based on the Choice of Use of a Quantitative Randomization Device. Presenting at the JSM 2012, San Diogo, CA.
  • Odumade, O. and Singh, S. (2011). A new optimal estimator of population proportion in randomized response sampling. Presented at the Joint Statistical Meeting, Miami Beach, FL.
  • Odumade, O. and Singh, S. (2006). Generalized forced quantitative randomized response model. Presented at the Joint Statistical Meeting, Seattle, USA.
  • Gjestvang, Chris and Singh, S. (2005). A new randomized response model: Estimation of Mean. Presented at the Joint Statistical Meeting, Minneapolis, USA.
  • Stearns, M. and Singh, S. (2005). A new model assisted chi-square distance function for the calibration of design weights. Presented at Joint Statistical Meeting, MN, USA.

MS students supervised at TAMUK:

Lee, Cheon-Sig; Su, Ching-Su; Johnson, M.L and Mohamed, C.


Undergraduate students supervised from the US:

Dykes, Lee; Odumade, O; Gestavang, C. and Stearns, M.