Advances in Stochastic Simulation Methods

Herausgegeben:Balakrishnan, N.; Melas, V. B.; Ermakov, S.

• Broschiertes Buch

Produktbeschreibung

This is a volume consisting of selected papers that were presented at the 3rd St. Petersburg Workshop on Simulation held at St. Petersburg, Russia, during June 28-July 3, 1998. The Workshop is a regular international event devoted to mathematical problems of simulation and applied statistics organized by the Department of Stochastic Simulation at St. Petersburg State University in cooperation with INFORMS College on Simulation (USA). Its main purpose is to exchange ideas between researchers from Russia and from the West as well as from other coun tries throughout the World. The 1st Workshop was held during May 24-28, 1994, and the 2nd workshop was held during June 18-21, 1996. The selected proceedings of the 2nd Workshop was published as a special issue of the Journal of Statistical Planning and Inference. Russian mathematical tradition has been formed by such genius as Tchebysh eff, Markov and Kolmogorov whose ideas have formed the basis for contempo rary probabilistic models. However, for many decades now, Russian scholars have been isolated from their colleagues in the West and as a result their mathe matical contributions have not been widely known. One of the primary reasons for these workshops is to bring the contributions of Russian scholars into lime light and we sincerely hope that this volume helps in this specific purpose.

Produktdetails

• Statistics for Industry and Technology
• Verlag: Birkhäuser / Birkhäuser Boston / Springer, Berlin
• Artikelnr. des Verlages: 80102933, 978-1-4612-7091-1
• Softcover reprint of the original 1st ed. 2000
• Seitenzahl: 416
• Erscheinungstermin: 8. Oktober 2012
• Englisch
• Abmessung: 254mm x 178mm x 23mm
• Gewicht: 798g
• ISBN-13: 9781461270911
• ISBN-10: 146127091X
• Artikelnr.: 37466398

Autorenporträt

N. Balakrishnan is an Associate Director and Professor at Department of Aerospace Engineering and Supercomputer Edu- cation and Research Centre, Indian Institute of Science. His research interests include numerical electromagnetic, multi-parameter radars, and signal processing. His publications include 19 books and many peer-reviewed journal papers.

Inhaltsangabe

I: Simulation Models.- 1 Solving the Nonlinear Algebraic Equations with Monte Carlo Method.- 2 Monte Carlo Algorithms For Neumann Boundary Value Problem Using Fredholm Representation.- 3 Estimation Errors for Functionals on Measure Spaces.- 4 The Multilevel Method of Dependent Tests.- 5 Algebraic Modelling and Performance Evaluation of Acyclic Fork-Join Queueing Networks.- II: Experimental Designs.- 6 Analytical Theory of E-Optimal Designs for Polynomial Regression.- 7 Bias Constrained Minimax Robust Designs for Misspecified Regression Models.- 8 A Comparative Study of MV- and SMV-Optimal Designs for Binary Response Models.- 9 On the Criteria for Experimental Design in Nonlinear Error-In-Variables Models.- 10 On Generating and Classifying all q71-m-1Regularly Blocked Factional Designs.- 11 Locally Optimal Designs in Non-Linear Regression: A Case Study of the Michaelis-Menten Function.- 12 D-Optimal Designs for Quadratic Regression Models.- 13 On the Use of Symmetry in Optimal Design of Experiments.- III: Statistical Inference.- 14 Higher Order Moments of Order Statistics from the Pareto Distribution and Edgeworth Approximate Inference.- 15 Higher Order Moments of Order Statistics from the Power Function Distribution and Edgeworth Approximate Inference.- 16 Selecting from Normal Populations the One with the Largest Absolute Mean: Comon Unknown Variance Case.- 17 Conditional Inference for the Parameters of Pareto Distributions when Observed Samples are Progressively Censored.- IV: Applied Statistics and Related Topics.- 18 On Randomizing Estimators in Linear Regression Models.- 19 Nonstationary Generalized Automata with Periodically Variable Parameters and Their Optimization.- 20 Power of Some Asymptotic Tests for Maximum Entropy.- 21 Partially Inversion of Functions forStatistical Modelling of Regulatory Systems.- 22 Simple Efficient Estimation for Three-Parameter Lognormal Distributions with pplications to Emissions Data and State Traffic Rate Data.