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Parametric and Nonparametric Inference from Record-Breaking Data
By providing a comprehensive look at statistical inference from record-breaking data in both parametric and nonparametric settings, this book treats the area of nonparametric function estimation from such data in detail. Its main purpose is to fill this void on general inference from record values. Statisticians, mathematicians, and engineers will find the book useful as a research reference. It can also serve as part of a graduate-level statistics or mathematics course.
Nonparametric Monte Carlo Tests and Their Applications
Monte Carlo approximation to the null distribution of the test provides a convenient means of testing model fit. This book proposes a Monte Carlo-based methodology to construct this type of approximation when the model is semistructured. It addresses both applied and theoretical aspects of nonparametric Monte Carlo tests.
Random Integral Equations with Applications to Stochastic Systems
The authors have two main objectives in these notes. First, they wish to give a complete presentation of the theory of existence and uniqueness of random solutions of the most general random Volterra and Fredholm equations which have been studied heretofore. Second, to emphasize the application of their theory to stochastic systems which have not been extensively studied before due to mathematical difficulties that arise. These notes will be of value to mathematicians, probabilists, and engineers who are working in the area of systems theory or to those who are interested in the theory of random equations.
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Random Integral Equations with Applications to Life Sciences and Engineering
In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;methods for low-rank.
