Advances in Stochastic Simulation Methods Review
This text presents 22 carefully edited chapters providing an up-to-date survey of results and trends in areas of statistical modelling, experimental design, and related issues of mathematical statistics and computational mathematics.
Annals of Operations Research (Stochastic Models and their Applications, Volume 32) Review
Modelling and Decisions in Economics: Essays in Honor of Franz Ferschl Review
This book provides insights that are useful for people with interests in Operations Research, statistics and econometrics. Each contribution offers a snapshot out from interesting topics. The relationships and interconnectivities between decisions and models from different economic fields lead to a better understanding of the modern field of Operations Research. The book focuses on modelling as an activity rather than on techniques and programming. Divided into three parts, part one provides decision theoretical models, part two provides several theoretical and empirical papers concerning statistics and econometrics. Finally, part three provides an up-to-date account of Operations Research.
Markov Processes for Stochastic Modeling (Stochastic Modeling Series) Review
This book presents an algebraic development of the theory of countable state space Markov chains with discrete and continuous time parameters.
Nonparametric Analysis of Univariate Heavy-Tailed Data: Research and Practice (Wiley Series in Probability and Statistics) Review
Heavy-tailed distributions are typical for phenomena in complex multi-component systems such as biometry, economics, ecological systems, sociology, web access statistics, internet traffic, biblio-metrics, finance and business. The analysis of such distributions requires special methods of estimation due to their specific features. These are not only the slow decay to zero of the tail, but also the violation of Cramer’s condition, possible non-existence of some moments, and sparse observations in the tail of the distribution.
The book focuses on the methods of statistical analysis of heavy-tailed independent identically distributed random variables by empirical samples of moderate sizes. It provides a detailed survey of classical results and recent developments in the theory of nonparametric estimation of the probability density function, the tail index, the hazard rate and the renewal function.
Both asymptotical results, for example convergence rates of the estimates, and results for the samples of moderate sizes supported by Monte-Carlo investigation, are considered. The text is illustrated by the application of the considered methodologies to real data of web traffic measurements.
Advances in Data Analysis: Theory and Applications to Reliability and Inference, Data Mining, Bioinformatics, Lifetime Data, and Neural Networks (Statistics for Industry and Technology) Review
This unified volume is a collection of invited chapters presenting recent developments in the field of data analysis, with applications to reliability and inference, data mining, bioinformatics, lifetime data, and neural networks. The book is a useful reference for graduate students, researchers, and practitioners in statistics, mathematics, engineering, economics, social science, bioengineering, and bioscience.
Designing Public Policies: An Approach Based on Multi-Criteria Analysis and Computable General Equilibrium Modeling (Lecture Notes in Economics and Mathematical Systems) Review
This book presents a methodological approach for the joint design of economic and environmental policies. The starting point is the observation that, in practice, policy makers do not usually have a well-defined objective, but they are typically concerned about a number of economic and environmental indicators that conflict with each other. In view of this, policy making is addressed by combining two separate analytical approaches: multiple criteria decision making (MCDM) and computable general equilibrium (CGE) modeling. The aim is to come up with a methodological framework for policy design which is both operational and consistent with economic theory. In short, this book offers a unified view of this novel approach, paying special attention to the connections between economic and environmental objectives. The methodological foundations are presented as well as some real applications that illustrate the pragmatic value of the theoretical proposal.
Stochastic Models in Reliability Theory: Proceedings of a Symposium Held in Nagoya, Japan, April 23-24, 1984 (Lecture Notes in Economics and Mathematical Systems) Review
Optimization of Stochastic Models: The Interface Between Simulation and Optimization (The Springer International Series in Engineering and Computer Science) Review
Stochastic models are everywhere. In manufacturing, queuing models are used for modeling production processes, realistic inventory models are stochastic in nature. Stochastic models are considered in transportation and communication. Marketing models use stochastic descriptions of the demands and buyer's behaviors. In finance, market prices and exchange rates are assumed to be certain stochastic processes, and insurance claims appear at random times with random amounts.
To each decision problem, a cost function is associated. Costs may be direct or indirect, like loss of time, quality deterioration, loss in production or dissatisfaction of customers. In decision making under uncertainty, the goal is to minimize the expected costs. However, in practically all realistic models, the calculation of the expected costs is impossible due to the model complexity. Simulation is the only practicable way of getting insight into such models. Thus, the problem of optimal decisions can be seen as getting simulation and optimization effectively combined.
The field is quite new and yet the number of publications is enormous. This book does not even try to touch all work done in this area. Instead, many concepts are presented and treated with mathematical rigor and necessary conditions for the correctness of various approaches are stated.
Optimization of Stochastic Models: The Interface Between Simulation and Optimization is suitable as a text for a graduate level course on Stochastic Models or as a secondary text for a graduate level course in Operations Research.
Stochastic Models in Operations Research 2 Volumes Review
