Advances in Stochastic Simulation Methods / Edition 1

Advances in Stochastic Simulation Methods / Edition 1

by N. Balakrishnan
     
 

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

See more details below

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

Preface
Contributors
List of Tables
List of Figures
Pt. ISimulation Models
1Solving the Nonlinear Algebraic Equations with Monte Carlo Method3
2Monte Carlo Algorithms For Neumann Boundary Value Problem Using Fredholm Representation17
3Estimation Errors for Functionals on Measure Spaces29
4The Multilevel Method of Dependent Tests47
5Algebraic Modelling and Performance Evaluation of Acyclic Fork-Join Queueing Networks63
Pt. IIExperimental Designs
6Analytical Theory of E-Optimal Designs for Polynomial Regression85
7Bias Constrained Minimax Robust Designs for Misspecified Regression Models117
8A Comparative Study of MV - and SMV-Optimal Designs for Binary Response Models135
9On the Criteria for Experimental Design in Nonlinear Error-In-Variables Models153
10On Generating and Classifying all a[superscript n-m-1] Regularly Blocked Factional Designs165
11Locally Optimal Designs in Non-Linear Regression: A Case Study of the Michaelis-Menten Function177
12D-Optimal Designs for Quadratic Regression Models189
13On the Use of Symmetry in Optimal Design of Experiments197
Pt. IIIStatistical Inference
14Higher Order Moments of Order Statistics from the Pareto Distribution and Edgeworth Approximate Inference207
15Higher Order Moments of Order Statistics from the Power Function Distribution and Edgeworth Approximate Inference245
16Selecting from Normal Populations the One with the Largest Absolute Mean: Comon Unknown Variance Case283
17Conditional Inference for the Parameters of Pareto Distributions when Observed Samples are Progressively Censored293
Pt. IVApplied Statistics and Related Topics
18On Randomizing Estimators in Linear Regression Models305
19Nonstationary Generalized Automata with Periodically Variable Parameters and Their Optimization315
20Power of Some Asymptotic Tests for Maximum Entropy337
21Partially Inversion of Functions for Statistical Modelling of Regulatory Systems355
22Simple Efficient Estimation for Three-Parameter Lognormal Distributions with Applications to Emissions Data and State Traffic Rate Data373
Subject Index385

Read More

Customer Reviews

Average Review:

Write a Review

and post it to your social network

     

Most Helpful Customer Reviews

See all customer reviews >