Statistical Modeling and Analysis for Complex Data Problems treats some of today's more complex problems and it reflects some of the important research directions in the field.
Peter Kall and Janos Mayer are distinguished scholars and professors of Operations Research and their research interest is particularly devoted to the area of stochastic optimization.
Applied probability is a broad research area that is of interest to scientists in diverse disciplines in science and technology, including: anthropology, biology, communication theory, economics, epidemiology, finance, geography, linguistics, medicine, meteorology, operations research, psychology, quality control, sociology, and statistics.
Separation of signal from noise is the most fundamental problem in data analysis, and arises in many fields, for example, signal processing, econometrics, acturial science, and geostatistics.
Applying Generalized Linear Models describes how generalized linear modelling procedures can be used for statistical modelling in many different fields, without becoming lost in problems of statistical inference.
The conference on Random Dynamical Systems took place from April 28 to May 2, 1997, in Bremen and was organized by Matthias Gundlach and Wolfgang Kliemann with the help of th'itz Colonius and Hans Crauel.
Our initial motivation for writing this book was the observation from various students that the subject of design and analysis of experiments can seem like "e;a bunch of miscellaneous topics.
Our object in writing this book is to present the main results of the modern theory of multivariate statistics to an audience of advanced students who would appreciate a concise and mathematically rigorous treatment of that material.
This book will serve as a reference book for graduate students and researchers in probability theory or partial differential equations who want to learn more about the interplay of these two areas.
The aim of the present book is to give a comprehensive up-to-date presentation of some of the classical areas of reliability, based on a more advanced probabilistic framework using the modern theory of stochastic processes.
This book covers the impact of noise on models that are widely used in science and engineering, and applies perturbed methods which assume noise changes on a faster time or space scale than the system being studied.
The basic stochastic approximation algorithms introduced by Robbins and MonroandbyKieferandWolfowitzintheearly1950shavebeenthesubject of an enormous literature, both theoretical and applied.
This volume deals primarily with the classical question of how to draw conclusions about the population mean of a variable, given a sample with observations on that variable.
Stochastic Process Limits are useful and interesting because they generate simple approximations for complicated stochastic processes and also help explain the statistical regularity associated with a macroscopic view of uncertainty.
Created to teach students many of the most important techniques used for constructing combinatorial designs, this is an ideal textbook for advanced undergraduate and graduate courses in combinatorial design theory.
Mathematics is playing an ever more important role in the physical and biological sciences, provoking a blurring of boundaries between scientific disciplines and a resurgence of interest in the modern as well as the clas- sical techniques of applied mathematics.
Stochastic petri nets have proven to be a useful tool for modelling and performance analysis of complex discrete-event stochastic systems such as those in telecommunications, manufacturing, transportation.
Advances in Queueing Theory and Network Applications presents several useful mathematical analyses in queueing theory and mathematical models of key technologies in wired and wireless communication networks such as channel access controls, Internet applications, topology construction, energy saving schemes, and transmission scheduling.
