This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.

This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.

Reliability concerns and the limitations of process technology can sometimes restrict the innovation process involved in designing nano-scale analog circuits. The success of nano-scale analog circuit design requires repeat experimentation, correct analysis of the device physics, process technology, and adequate use of the knowledge database. Starting with the basics, Nano-Scale CMOS Analog Circuits: Models and CAD Techniques for High-Level Design introduces the essential fundamental concepts for designing analog circuits with optimal performances. This book explains the links between the physics and technology of scaled MOS transistors and the design and simulation of nano-scale analog circuits. It also explores the development of structured computer-aided design (CAD) techniques for architecture-level and circuit-level design of analog circuits. The book outlines the general trends of technology scaling with respect to device geometry, process parameters, and supply voltage. It describes models and optimization techniques, as well as the compact modeling of scaled MOS transistors for VLSI circuit simulation. • Includes two learning-based methods: the artificial neural network (ANN) and the least-squares support vector machine (LS-SVM) method • Provides case studies demonstrating the practical use of these two methods • Explores circuit sizing and specification translation tasks • Introduces the particle swarm optimization technique and provides examples of sizing analog circuits • Discusses the advanced effects of scaled MOS transistors like narrow width effects, and vertical and lateral channel engineering Nano-Scale CMOS Analog Circuits: Models and CAD Techniques for High-Level Design describes the models and CAD techniques, explores the physics of MOS transistors, and considers the design challenges involving statistical variations of process technology parameters and reliability constraints related to circuit design.

Uncertainties in Modern Power Systems combines several aspects of uncertainty management in power systems at the planning and operation stages within an integrated framework. This book provides the state-of-the-art in electric network planning, including time-scales, reliability, quality, optimal allocation of compensators and distributed generators, mathematical formulation, and search algorithms. The book introduces innovative research outcomes, programs, algorithms, and approaches that consolidate the present status and future opportunities and challenges of power systems. The book also offers a comprehensive description of the overall process in terms of understanding, creating, data gathering, and managing complex electrical engineering applications with uncertainties. This reference is useful for researchers, engineers, and operators in power distribution systems. - Includes innovative research outcomes, programs, algorithms, and approaches that consolidate current status and future of modern power systems - Discusses how uncertainties will impact on the performance of power systems - Offers solutions to significant challenges in power systems planning to achieve the best operational performance of the different electric power sectors

This book primarily aims to discuss emerging topics in statistical methods and to booster research, education, and training to advance statistical modeling on interval-censored survival data. Commonly collected from public health and biomedical research, among other sources, interval-censored survival data can easily be mistaken for typical right-censored survival data, which can result in erroneous statistical inference due to the complexity of this type of data. The book invites a group of internationally leading researchers to systematically discuss and explore the historical development of the associated methods and their computational implementations, as well as emerging topics related to interval-censored data. It covers a variety of topics, including univariate interval-censored data, multivariate interval-censored data, clustered interval-censored data, competing risk interval-censored data, data with interval-censored covariates, interval-censored data from electric medical records, and misclassified interval-censored data. Researchers, students, and practitioners can directly make use of the state-of-the-art methods covered in the book to tackle their problems in research, education, training and consultation.

This Monograph explains the statistical theory behind the National Forest Inventory (NFI) data collection and compares different methods for modelling and inventory design. The author also explains how natural uncertainty in measurement and modelling can affects the results. Forests, as dynamic systems, are influenced by many unpredictable factors over time. Therefore, readers can use this book to develop the right framework of expectations, when using NFI data. The chapters give an outlook on traditional methods like sample plots, but also consider newer approaches like remote sensing. By merging these different techniqes, NFI datasets can become more reliable and facetted. One of the most contemporary developments in the field, is the use of continuous plots that offer live data at all times. Whether this data should be open to the public, is another discussion point that the author addresses. Offering a perspective from Estonia, readers will find practical examples for all discussed methods. This bridge from theory to practice, makes the volume a useful resource for scientists and decision makers in the forestry sector.

Advances in computational power have facilitated the development of simulations unprecedented in their computational size, scope of technical issues, spatial and temporal resolution, complexity and comprehensiveness. As a result, complex structures from airplanes to bridges can be almost completely based on model-based simulations. This book gives a state-of-the-art account of modeling and simulation of the life cycle of engineered systems, covering topics of design, fabrication, maintenance and disposal. Providing comprehensive coverage of this rapidly emerging field, Modeling and Simulation-Based Life Cycle Engineering is essential reading for civil, mechanical and manufacturing engineers. It will also appeal to students and academics in this area.

In today’s financial market, portfolio and risk management are facing an array of challenges. This is due to increasing levels of knowledge and data that are being made available that have caused a multitude of different investment models to be explored and implemented. Professionals and researchers in this field are in need of up-to-date research that analyzes these contemporary models of practice and keeps pace with the advancements being made within financial risk modelling and portfolio control. Recent Applications of Financial Risk Modelling and Portfolio Management is a pivotal reference source that provides vital research on the use of modern data analysis as well as quantitative methods for developing successful portfolio and risk management techniques. While highlighting topics such as credit scoring, investment strategies, and budgeting, this publication explores diverse models for achieving investment goals as well as improving upon traditional financial modelling methods. This book is ideally designed for researchers, financial analysts, executives, practitioners, policymakers, academicians, and students seeking current research on contemporary risk management strategies in the financial sector.

Taking the topics of a quantitative methodology course and illustrating them through Monte Carlo simulation, this book examines abstract principles, such as bias, efficiency, and measures of uncertainty in an intuitive, visual way. Instead of thinking in the abstract about what would happen to a particular estimator "in repeated samples," the book uses simulation to actually create those repeated samples and summarize the results. The book includes basic examples appropriate for readers learning the material for the first time, as well as more advanced examples that a researcher might use to evaluate an estimator he or she was using in an actual research project. The book also covers a wide range of topics related to Monte Carlo simulation, such as resampling methods, simulations of substantive theory, simulation of quantities of interest (QI) from model results, and cross-validation. Complete R code from all examples is provided so readers can replicate every analysis presented using R.

Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum Geosciences provides a practical guide to many of the classical and modern statistical techniques that have become established for oil and gas professionals in recent years. It serves as a "how to" reference volume for the practicing petroleum engineer or geoscientist interested in applying statistical methods in formation evaluation, reservoir characterization, reservoir modeling and management, and uncertainty quantification. Beginning with a foundational discussion of exploratory data analysis, probability distributions and linear regression modeling, the book focuses on fundamentals and practical examples of such key topics as multivariate analysis, uncertainty quantification, data-driven modeling, and experimental design and response surface analysis. Data sets from the petroleum geosciences are extensively used to demonstrate the applicability of these techniques. The book will also be useful for professionals dealing with subsurface flow problems in hydrogeology, geologic carbon sequestration, and nuclear waste disposal. - Authored by internationally renowned experts in developing and applying statistical methods for oil & gas and other subsurface problem domains - Written by practitioners for practitioners - Presents an easy to follow narrative which progresses from simple concepts to more challenging ones - Includes online resources with software applications and practical examples for the most relevant and popular statistical methods, using data sets from the petroleum geosciences - Addresses the theory and practice of statistical modeling and data analytics from the perspective of petroleum geoscience applications