Advances in Stochastic Simulation Methods / Edition 1

Advances in Stochastic Simulation Methods / Edition 1

by N. Balakrishnan
     
 

ISBN-10: 0817641076

ISBN-13: 9780817641078

Pub. Date: 05/31/2001

Publisher: Birkhauser Verlag

This book presents 22 carefully edited chapters providing an up-to-dat e survey of new results and trends in the important areas of statistic al modeling, experimental design, and related issues of mathematical s tatistics and computational mathematics. The book shows the connection s between different trends of fundamental research and presents new al gorithms and

Overview

This book presents 22 carefully edited chapters providing an up-to-dat e survey of new results and trends in the important areas of statistic al modeling, experimental design, and related issues of mathematical s tatistics and computational mathematics. The book shows the connection s between different trends of fundamental research and presents new al gorithms and models on this basis. The chapters are thematically organ ized into four parts: Simulation Models, Experimental Designs, Statist ical inference, Applied Statistics and Related Topics.

Product Details

ISBN-13:
9780817641078
Publisher:
Birkhauser Verlag
Publication date:
05/31/2001
Series:
Statistics for Industry and Technology Series
Edition description:
2000
Pages:
386
Product dimensions:
10.00(w) x 7.00(h) x 0.94(d)

Table of Contents

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 for Statistical Modelling of Regulatory Systems.- 22 Simple Efficient Estimation for Three-Parameter Lognormal Distributions with pplications to Emissions Data and State Traffic Rate Data.

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