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Obituary: Sidney Jesse Yakowitz

Published online by Cambridge University Press:  14 July 2016

J. Gani ,
Benjamin Kedem  and
Ferenc Szidarovszky
J. Gani
Affiliation:
Australian National University, Canberra
Benjamin Kedem
Affiliation:
University of Maryland
Ferenc Szidarovszky
Affiliation:
University of Arizona
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Abstract

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Type
Short Communications
Information
Journal of Applied Probability , Volume 37 , Issue 2 , June 2000 , pp. 606 - 611
Copyright
Copyright © by the Applied Probability Trust 2000 

References

Books

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

Papers

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