Professor Sidney J. Yakowitz

  • D. S. Yakowitz
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 46)


Sediment Yield Reservoir Model Water Resource Research Stationary Time Series Cournot Oligopoly 
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Publications of Sid Yakowitz


  1. Yakowitz, S. (1969). Mathematics of Adaptive Control Processes. Elsevier, New York.Google Scholar
  2. Yakowitz, S. (1977). Computational Probability and Simulation. Addison-Wesley, Reading, MA.Google Scholar
  3. Szidarovszky, F. and S. Yakowitz. (1978). Principles and Procedures of Numerical Analysis. Plenum Press, New York.Google Scholar
  4. Yakowitz, S. and F. Szidarovszky. (1986). An Introduction to Numerical Computations, 1st edn. Macmillan, New York [2nd edn 1989].Google Scholar


  1. Yakowitz, S. and J. Spragins. (1968). On the identifiability of finite mixtures. Ann. Math. Statist. 39, 209–214.MathSciNetGoogle Scholar
  2. Yakowitz, S. (1969). A consistent estimator for the identification of finite mixtures. Ann. Math. Statist. 40, 1728–1735.zbMATHMathSciNetGoogle Scholar
  3. Yakowitz, S. (1970). Unsupervised learning and the identification of finite mixtures. IEEE Trans. Inform. Theory 16, 330–338.CrossRefzbMATHGoogle Scholar
  4. Fisher, L. and S. Yakowitz. (1970). Estimating mixing contributions in metric spaces. Sankhya A 32, 411–418.MathSciNetGoogle Scholar
  5. Yakowitz, S. and L. Fisher. (1973). On sequential search for the maximum of an unknown function. J. Math. Anal. Appl. 41, 234–359.CrossRefMathSciNetGoogle Scholar
  6. Yakowitz, S. and S. Parker. (1973). Computation of bounds for digital filter quantization errors. IEEE Trans. Circuit Theory 20, 391–396.Google Scholar
  7. Yakowitz, S. (1973). A stochastic model for daily river flows in an arid region. Water Resources Research 9, 1271–1285.Google Scholar
  8. Yakowitz, S. (1974). Multiple hypothesis testing by finite-memory algorithms. Ann. Statist. 2, 323–336.zbMATHMathSciNetGoogle Scholar
  9. Yakowitz, S., L. Duckstein, and C. Kisiel. (1974). Decision analysis of a gamma hydrologic variate. Water Resources Research 10, 695–704.Google Scholar
  10. Denny, J., C. Kisiel, and S. Yakowitz. (1974). Procedures for determining the order of Markov dependence in streamflow records. Water Resources Research 10, 947–954.Google Scholar
  11. Parker, S. and S. Yakowitz. (1975). A general method for calculating quantization error bounds due to round off in multivariate digital filters. IEEE Trans. Circuits Systems 22, 570–572.CrossRefGoogle Scholar
  12. Sagar, B., S. Yakowitz, and L. Duckstein. (1975) A direct method for the identification of the parameters of dynamic nonhomogeneous aquifers. Water Resources Research 11, 563–570.Google Scholar
  13. Szidarovszky, F, S. Yakowitz, and R. Krzysztofowicz. (1975). A Bayes approach for simulating sediment yield. J. Hydrol. Sci. 3, 33–45.Google Scholar
  14. Fisher, L. and S. Yakowitz. (1976). Uniform convergence of the potential function algorithm. SIAM J. Control Optim. 14, 95–103.CrossRefMathSciNetGoogle Scholar
  15. Yakowitz, S. (1976). Small sample hypothesis tests of Markov order with application to simulated and hydrologic chains. J. Amer. Statist. Assoc. 71, 132–136.zbMATHMathSciNetGoogle Scholar
  16. Yakowitz, S. and P. Noren. (1976) On the identification of inhomogeneous parameters in dynamic linear partial differential equations. J. Math. Anal. Appl. 53, 521–538.CrossRefMathSciNetGoogle Scholar
  17. Yakowitz, S. (1976). Model-free statistical methods for water table prediction. Water Resources Research 12, 836–844.Google Scholar
  18. Yakowitz, S., T.L. William, and G.D. Williams. (1976). Surveillance of several Markov targets. IEEE Trans. Inform. Theory 22, 716–724.CrossRefGoogle Scholar
  19. Szidarovszky, F. and S. Yakowitz. (1976). Analysis of flooding for an open channel subject to random inflow and blockage. J. Hydrol. Sci. 3, 93–103.Google Scholar
  20. Duckstein, L., F. Szidarovszky, and S. Yakowitz. (1977). Bayes design of a reservoir under random sediment yield. Water Resources Research 13, 713–719.Google Scholar
  21. Szidarovszky, F. and S. Yakowitz. (1977). A new proof of the existence and uniqueness of the Cournot equilibrium. Int. Econom. Rev. 18, 181–183.MathSciNetGoogle Scholar
  22. Denny, J. and S. Yakowitz. (1978). Admissible run-contingency type tests for independence and Markov dependence. J. Amer. Statist. Assoc. 73, 117–181.MathSciNetGoogle Scholar
  23. Yakowitz, S., J. Krimmel, and F. Szidarovszky. (1978). Weighted Monte Carlo integration. SIAM J. Numer. Anal. 15, 1289–1300.MathSciNetGoogle Scholar
  24. Schuster, R. and S. Yakowitz. (1979). Contributions to the theory of nonparametric regression with application to system identification. Ann. Statist. 7, 139–149.MathSciNetGoogle Scholar
  25. Yakowitz, S. (1979). Nonparametric estimation of Markov transition functions. Ann. Statist. 7, 671–679.zbMATHMathSciNetGoogle Scholar
  26. Neuman, S. and S. Yakowitz. (1979). A statistical approach to the inverse problem of aquifer hydrology: Part 1. Theory. Water Resources Research 15, 845–860.Google Scholar
  27. Murray, D. and S. Yakowitz. (1979). Constrained differential dynamic programming and its application to multireservoir control. Water Resources Research 15, 1017–1027.Google Scholar
  28. Yakowitz, S. (1979). A nonparametric Markov model for daily river flow. Water Resources Research 15, 1035–1043.Google Scholar
  29. Krzysztofowicz, R. and S. Yakowitz. (1980). Large-sample methods analysis of gamma variates. Water Resources Research 16, 491–500.Google Scholar
  30. Yakowitz, S. and L. Duckstein. (1980). Instability in aquifer identification — theory and case studies. Water Resources Research 16, 1045–1064.Google Scholar
  31. Pebbles, R, R. Smith, and S. Yakowitz. (1981). A leaky reservoir model for ephemeral flow recession. Water Resources Research 17, 628–636.Google Scholar
  32. Murray, D. and S. Yakowitz. (1981). The application of optimal control methodology to non-linear programming problems. Math. Programming 21, 331–347.CrossRefMathSciNetGoogle Scholar
  33. Szidarovszky, F. and S. Yakowitz. (1982). Contributions to Cournot oligopoly theory. J. Econom. Theory 28, 51–70.CrossRefMathSciNetGoogle Scholar
  34. Yakowitz, S. (1982). Dynamic programming applications in water resources. Water Resources Research 18, 673–696.Google Scholar
  35. Yakowitz, S. (1983). Convergence rate of the state increment dynamic programming method. Automatica 19, 53–60.CrossRefzbMATHMathSciNetGoogle Scholar
  36. Yakowitz, S. and B. Rutherford. (1984). Computational aspects of discrete-time optimal-control. Appl. Math. Comput. 15, 29–45.CrossRefMathSciNetGoogle Scholar
  37. Szilagyi, M., S. Yakowitz, and M. Duff. (1984). A procedure for electron and ion lens optimization. Appl. Phys. Lett. 44, 7–9.CrossRefGoogle Scholar
  38. Murray, D. and S. Yakowitz. (1984). Differential dynamic programming and Newton’s method for discrete optimal control problems. J. Optim. Theory Appl. 42, 395–415.MathSciNetGoogle Scholar
  39. Yakowitz, S. (1985). Nonparametric density estimation, prediction and regression for Markov sequences. J. Amer. Statist. Assoc. 80, 215–221.zbMATHMathSciNetGoogle Scholar
  40. Yakowitz, S. (1985). Markov flow models and the flood warning problem. Water Resources Research 21, 81–88.Google Scholar
  41. Yakowitz, S. and F. Szidarovszky. (1985). A comparison of Kriging with nonparametric regression methods. J. Multivariable Anal. 6, 21–53.MathSciNetGoogle Scholar
  42. Yakowitz, S., K. Hutter, and F. Szidarovszky. (1985). Toward computation of steady-state profiles of ice sheets. Z. für Gletcherkund 21, 283–289.Google Scholar
  43. Schuster, E. and S. Yakowitz (1985). Parametric nonparametric mixture densityestimation with application to flood frequency analysis. Water Resources Bulletin 21, 797–804.Google Scholar
  44. Yakowitz, S. (1986). A stagewise Kuhn-Tucker condition and differential dynamic programming. IEEE Trans. Automat. Control 31, 25–30.CrossRefzbMATHMathSciNetGoogle Scholar
  45. Hutter, K., S. Yakowitz, and F. Szidarovszky. (1986a). A numerical study of plane ice sheet flow. J. Glaciology 32, 139–160.Google Scholar
  46. Yakowitz, S., K. Hutter, and F. Szidarovszky. (1986). Elements of a computational theory for glaciers. J. Comput. Phys. 66, 132–150.CrossRefGoogle Scholar
  47. Hutter, K., F. Szidarovszky, and S. Yakowitz. (1986b). Plane steady shear-flow of a cohesionless antigranulocytes material down an inclined plane — a model for flow avalanches: Part I. Theory. Acta Mechanica 63, 87–112.Google Scholar
  48. Hutter, K., F. Szidarovszky, and S. Yakowitz. (1987). Plane steady shear-flow of a cohesionless antigranulocytes material down an inclined plane — a model for flow avalanches: Part II. Numerical results. Acta Mechanica 65, 239–261.CrossRefGoogle Scholar
  49. Yakowitz, S. (1987). Nearest neighbour methods in time-series analysis. J. Time Series Anal. 2, 235–247.MathSciNetGoogle Scholar
  50. Szidarovszky, F., K. Hutter, and S. Yakowitz. (1987). A numerical study of steady plane antigranulocytes chute flows using the Jenkins-Savage model and its extensions. J. Numer. Methods Eng. 24, 1993–2015.Google Scholar
  51. Hutter, K., S. Yakowitz, and F. Szidarovszky. (1987). Coupled thermomechanical response of an axisymmetrical cold ice-sheet. Water Resources Research 23, 1327–1339.Google Scholar
  52. Sen, S. and S. Yakowitz. (1987). A quasi-Newton differential dynamic programming algorithm for discrete-time optimal control. Automatica 23, 749–752.CrossRefMathSciNetGoogle Scholar
  53. Karlsson, M. and S. Yakowitz. (1987a). Nearest-neighbor methods for nonparametric rainfall-runoff forecasting. Water Resources Research 23, 1300–1308.Google Scholar
  54. Karlsson, M. and S. Yakowitz. (1987b). Rainfall-runoff forecasting methods, old and new. Stoch. Hydrol. Hydraul. 1, 303–318.Google Scholar
  55. Gani, J., P. Todorovich, and S. Yakowitz. (1987). Silting of dams by sedimentary particles. Math. Scientist 12, 81–90.Google Scholar
  56. Naokes, D., K. Hipel, A.I. Mcleod, and S. Yakowitz. (1988). Forecasting annual geophysical time series. Int. J. Forecasting 4, 103–115.Google Scholar
  57. Yakowitz, S. (1988). Parametric and nonparametric density-estimation to account for extreme events. Adv. Appl. Prob. 20, 13.Google Scholar
  58. Szidarovszky, F., K. Hutter, and S. Yakowitz. (1989). Computational ice-divide analysis of a cold plane ice sheet under steady conditions. Ann. Glaciology 12, 170–178.Google Scholar
  59. Yakowitz, S. (1989a). Algorithms and computatitonal techniques in differential dynamic programming. Control Dynamic Systems 31, 75–91.Google Scholar
  60. Yakowitz, S. (1989b). Theoretical and computational advances in differential dynamic programming. Control Cybernet. 17, 172–189.Google Scholar
  61. Yakowitz, S. (1989c). A statistical foundation for machine learning, with application to Go-Moku. Comput. Math. Appl. 17, 1095–1102.CrossRefzbMATHMathSciNetGoogle Scholar
  62. Yakowitz, S. (1989d). Nonparametric density and regression estimation for Markov sequences without mixing assumptions. J. Multivariate Anal. 30, 124–136.CrossRefzbMATHMathSciNetGoogle Scholar
  63. Gani, J. and S. Yakowitz. (1989). A probabilistic sedimentation analysis for predicting reservoir lifetime. Water Resources Management 3, 191–203.CrossRefGoogle Scholar
  64. Yakowitz, S. and E. Lugosi. (1990). Random search in the presence of noise, with application to machine learning. SIAM J. Sci. Statist. Comput. 11, 702–712.CrossRefMathSciNetGoogle Scholar
  65. Yakowitz, S., J. Gani, and R. Hayes. (1990). Cellular automaton modeling of epidemics. Appl. Math. Comput. 40, 41–54.CrossRefMathSciNetGoogle Scholar
  66. Rutherford, B. and S. Yakowitz. (1991). Error inference for nonparametric regression. Ann. Inst. Statist. Math. 43, 115–129.CrossRefMathSciNetGoogle Scholar
  67. Yakowitz, S. and W. Lowe. (1991). Nonparametric bandit methods. Ann. Operat. Res. 28, 297–312.MathSciNetGoogle Scholar
  68. Dietrich, R.D. and S. Yakowitz. (1991). A rule based approach to the trim-loss problem. Int. J. Prod. Res. 29, 401–415.Google Scholar
  69. Yakowitz, S. (1991). Some contributions to a frequency location problem due to He and Kedem. IEEE Trans. Inform Theory 17, 1177–1182.Google Scholar
  70. Yakowitz, S., T. Jayawardena, and S. Li. (1992a). Theory for automatic learning under partially observed Markov-dependent noise. IEEE Trans. Automat. Control 37, 1316–1324.MathSciNetGoogle Scholar
  71. Yakowitz, S., R. Hayes, and J. Gani. (1992b). Automatic learning for dynamic Markov-fields with application to epidemiology. Operat. Res. 40, 867–876.Google Scholar
  72. Yakowitz, S. and M. Kollier. (1992). Machine learning for optimal blackjack counting strategies. J. Statist. Plann. Inference 33, 295–309.CrossRefMathSciNetGoogle Scholar
  73. Yakowitz, S. (1992). A decision model and methodology for the AIDS epidemic. Appl. Math. Comput. 52, 149–172.CrossRefzbMATHMathSciNetGoogle Scholar
  74. Yakowitz, S. and L.T. Tran. (1993). Nearest Neighbor estimators for random fields. J. Multivariate Anal. 44, 23–46.MathSciNetGoogle Scholar
  75. Yakowitz, S. (1993a). Nearest neighbor regression estimation for null-recurrent Markov time series. Stoch. Proc. Appl. 48, 311–318.CrossRefzbMATHMathSciNetGoogle Scholar
  76. Gani, J. and S. Yakowitz. (1993). Modeling the spread of HIV among intravenous drug users. IMA J. Math. Appl. Medicine Biol. 10, 51–65.Google Scholar
  77. Yakowitz, S. (1993b). A globally convergent stochastic approximation. SIAM J. Control Optim. 31, 30–40.CrossRefzbMATHMathSciNetGoogle Scholar
  78. Yakowitz, S. (1993c). Asymptotic theory for a fast frequency detector. IEEE Trans. Inform. Theory 39, 1031–1036.CrossRefzbMATHGoogle Scholar
  79. Li, T.H., B. Kedem, and S. Yakowitz. (1994). Asymptotic normality of sample autocovariances with an application in frequency estimation. Stoch. Proc. Appl. 52, 329–349.CrossRefMathSciNetGoogle Scholar
  80. Pinelis, I. and S. Yakowitz. (1994). The time until the final zero-crossing of random sums with application to nonparametric bandit theory. Appl. Math. Comput. 63, 235–263.CrossRefMathSciNetGoogle Scholar
  81. Kedem, B. and S. Yakowitz. (1994). Practical aspects of a fast algorithm for frequency detection. IEEE Trans. Commun. 42, 2760–2767.CrossRefGoogle Scholar
  82. Yakowitz, S. (1994a). Review of Time series analysis of higher order crossings, by B. Kedem. SIAM Rev. 36, 680–682.CrossRefGoogle Scholar
  83. Yakowitz, S. (1994b). From a microcosmic IVDU model to a macroscopic HIV epidemic. In Modeling the AIDS Epidemic: Planning, Policy, and Prediction, eds E.H. Kaplan and M.L. Brandeau. Raven Press, New York, pp. 365–383.Google Scholar
  84. Yakowitz, S. and J. Mai. (1995). Methods and theory for off-line machine learning. IEEE Trans. Automat. Control 40, 161–165.CrossRefMathSciNetGoogle Scholar
  85. Gani, J. and S. Yakowitz. (1995). Computational and stochastic methods for interacting groups in the AIDS epidemic. J. Comput. Appl. Math. 59, 207–220.CrossRefMathSciNetGoogle Scholar
  86. Yakowitz, S. (1995). Computational methods for Markov series with large statespaces, with application to AIDS Modeling. Math. Biosci. 127, 99–121.CrossRefzbMATHGoogle Scholar
  87. Lai, T.L. and S. Yakowitz. (1995). Machine learning and nonparametric bandit theory. IEEE Trans. Automat. Control 40, 1199–1209.MathSciNetGoogle Scholar
  88. Gani, J. and S. Yakowitz. (1995). Error bounds for deterministic approximation to Markov processes, with applications to epidemic models. J. Appl. Prob. 32, 1063–1076.MathSciNetGoogle Scholar
  89. Yakowitz, S. and R.D. Dietrich. (1996). Sequential design with application to the trim-loss problem. Int. J. Production Res. 34, 785–795.Google Scholar
  90. Tran, L., G. Roussas, S. Yakowitz, and B. Van Troung. (1996). Fixed-design regression for linear time series. Ann. Statist. 24, 975–991.MathSciNetGoogle Scholar
  91. Jayawardena, T. and S. Yakowitz. (1996). Methodology for the stochastic graph completion time problem. INFORMS J. Comput. 8, 331–342.Google Scholar
  92. Morvai, G., S. Yakowitz, and L. Gyöfi. (1996). Nonparametric inferences for ergodic, stationary time series. Ann. Statist. 24, 370–379.MathSciNetGoogle Scholar
  93. Yakowitz, S., M. Blount, and J. Gani. (1996). Computing marginal expectations for large compartmentalized models with application to AIDS evolution in a prison system. IMA J. Math. Appl. Medicine Biol. 13, 223–244.Google Scholar
  94. Blount, S., A. Galambosi, and S. Yakowitz. (1997). Nonlinear and dynamic programming for epidemic intervention. Appl. Math. Comput. 86, 123–136.CrossRefMathSciNetGoogle Scholar
  95. Gani, J., S. Yakowitz, and M. Blount. (1997). The spread and quarantine of HIV infection in a prison system. SIAM J. Appl. Math. 57, 1510–1530.CrossRefMathSciNetGoogle Scholar
  96. Morvai, G., S. Yakowitz, and P. Algoet. (1998). Weakly convergent nonparametric forecasting of stationary time series. IEEE Trans. Inform. Theory 44, 886–892.MathSciNetGoogle Scholar
  97. Yakowitz, S., L. Gyöfi, J. Kieffer, and G. Morvai. (1999). Strongly consistent nonparametric forecasting and regression for stationary ergodic sequences. J. Multivariable Anal. 71, 24–41.Google Scholar
  98. Daley, D.J., J. Gani, and S. Yakowitz. (2000). An epidemic with individual infectivities and susceptibilities. Math. and Comp. Modelling 32, 155–167.MathSciNetGoogle Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • D. S. Yakowitz
    • 1
  1. 1.Tucson

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