Bøger af Yan Liu

This book compiles theoretical developments on statistical inference for time series and related models in honor of Masanobu Taniguchi's 70th birthday. It covers models such as long-range dependence models, nonlinear conditionally heteroscedastic time series, locally stationary processes, integer-valued time series, Lévy Processes, complex-valued time series, categorical time series, exclusive topic models, and copula models. Many cutting-edge methods such as empirical likelihood methods, quantile regression, portmanteau tests, rank-based inference, change-point detection, testing for the goodness-of-fit, higher-order asymptotic expansion, minimum contrast estimation, optimal transportation, and topological methods are proposed, considered, or applied to complex data based on the statistical inference for stochastic processes.The performances of these methods are illustrated by a variety of data analyses. This collection of original papers provides the reader with comprehensive and state-of-the-art theoretical works on time series and related models. It contains deep and profound treatments of the asymptotic theory of statistical inference. In addition, many specialized methodologies based on the asymptotic theory are presented in a simple way for a wide variety of statistical models. This Festschrift finds its core audiences in statistics, signal processing, and econometrics.

Many models have been devised to analyze urbanization and the physical, socioeconomic, and institutional factors impacting urban development. This book presents a simulation model of urban development based on cellular automata principles and provides theoretical context. The first part of the text supplies a practical review of modeling techniq

The book is the first thorough study of the role of phosphorus chemistry in the origin of life. This book starts with depiction of the phosphorus role in life creation and evolution. Then it outlines in vital processes how different phosphorus-containing compounds participate as biomarker in life evolution. Written by renowned scientists, it is suitable for researchers and students in organic phosphorus chemistry, biochemistry and etc.

Demonstrates how to solve reliability problems using practical applications of Bayesian modelsThis self-contained reference provides fundamental knowledge of Bayesian reliability and utilizes numerous examples to show how Bayesian models can solve real life reliability problems. It teaches engineers and scientists exactly what Bayesian analysis is, what its benefits are, and how they can apply the methods to solve their own problems. To help readers get started quickly, the book presents many Bayesian models that use JAGS and which require fewer than 10 lines of command. It also offers a number of short R scripts consisting of simple functions to help them become familiar with R coding.Practical Applications of Bayesian Reliability starts by introducing basic concepts of reliability engineering, including random variables, discrete and continuous probability distributions, hazard function, and censored data. Basic concepts of Bayesian statistics, models, reasons, and theory are presented in the following chapter. Coverage of Bayesian computation, Metropolis-Hastings algorithm, and Gibbs Sampling comes next. The book then goes on to teach the concepts of design capability and design for reliability; introduce Bayesian models for estimating system reliability; discuss Bayesian Hierarchical Models and their applications; present linear and logistic regression models in Bayesian Perspective; and more.* Provides a step-by-step approach for developing advanced reliability models to solve complex problems, and does not require in-depth understanding of statistical methodology* Educates managers on the potential of Bayesian reliability models and associated impact* Introduces commonly used predictive reliability models and advanced Bayesian models based on real life applications* Includes practical guidelines to construct Bayesian reliability models along with computer codes for all of the case studies* JAGS and R codes are provided on an accompanying website to enable practitioners to easily copy them and tailor them to their own applicationsPractical Applications of Bayesian Reliability is a helpful book for industry practitioners such as reliability engineers, mechanical engineers, electrical engineers, product engineers, system engineers, and materials scientists whose work includes predicting design or product performance.

This book systematically explains the fundamentals of system-level electromagnetic compatibility and introduces the basic concept of system-level electromagnetic compatibility quantification design. The topics covered include the critical technologies in the top-down quantification design of electromagnetic compatibility, quantification design of system-level electromagnetic compatibility, evaluation methods and application examples, quality control and application examples of electromagnetic compatibility development process, and real-world engineering example analysis of electromagnetic compatibility.The book proposes a top-down system-level electromagnetic compatibility quantification design method and is the first book to describe in detail how to quantitatively evaluate and predict system-level electromagnetic compatibility performance. It includes abundant engineering examples and experimental data demonstrating the usage and results of the top-down quantification design methods of system-level electromagnetic compatibility.It enables readers to obtain a thorough understanding of the theory and methods of system-level electromagnetic compatibility quantification design as well as the methodologies for engineering practice.

The first retrospective of a true master of Chinese Art, published to coincide with an exhibition at the Ashmolean which runs from 7 November 2017 to 15 April 2018.

This book integrates the fundamentals of asymptotic theory of statistical inference for time series under nonstandard settings, e.g., infinite variance processes, not only from the point of view of efficiency but also from that of robustness and optimality by minimizing prediction error.

Many models have been devised to analyze urbanization and the physical, socioeconomic, and institutional factors impacting urban development. This book presents a simulation model of urban development based on cellular automata principles and provides theoretical context.