Author : Anonim
Publisher : Unknown
Page : 128 pages
File Size : 52,9 Mb
Release : 19??
Category : Electronic
ISBN : 9535124943
Empirical Modeling and Its Applications
Anonim
Empirical Modeling And Its Applications
Empirical Modeling And Its Applications Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Empirical Modeling And Its Applications book. This book definitely worth reading, it is an incredibly well-written.
Empirical Modeling and Its Applications
Dr. Md. Mamun Habib
Author : Dr. Md. Mamun Habib
Publisher : BoD – Books on Demand
Page : 150 pages
File Size : 41,6 Mb
Release : 2016-07-20
Category : Computers
ISBN : 9789535124931
Empirical Modeling and Its Applications by Dr. Md. Mamun Habib Pdf
Empirical modeling has been a useful approach for the analysis of different problems across numerous areas/fields of knowledge. As it is known, this type of modeling is particularly helpful when parametric models, due to various reasons, cannot be constructed. Based on different methodologies and approaches, empirical modeling allows the analyst to obtain an initial understanding of the relationships that exist among the different variables that belong to a particular system or process. In some cases, the results from empirical models can be used in order to make decisions about those variables, with the intent of resolving a given problem in the real-life applications. This book entitled Empirical Modeling and Its Applications consists of six (6) chapters.
Empirical Model Building
James R. Thompson
Author : James R. Thompson
Publisher : John Wiley & Sons
Page : 460 pages
File Size : 51,8 Mb
Release : 2011-11-30
Category : Mathematics
ISBN : 9781118109625
Empirical Model Building by James R. Thompson Pdf
Praise for the First Edition "This...novel and highly stimulating book, which emphasizes solving real problems...should be widely read. It will have a positive and lasting effect on the teaching of modeling and statistics in general." - Short Book Reviews This new edition features developments and real-world examples that showcase essential empirical modeling techniques Successful empirical model building is founded on the relationship between data and approximate representations of the real systems that generated that data. As a result, it is essential for researchers who construct these models to possess the special skills and techniques for producing results that are insightful, reliable, and useful. Empirical Model Building: Data, Models, and Reality, Second Edition presents a hands-on approach to the basic principles of empirical model building through a shrewd mixture of differential equations, computer-intensive methods, and data. The book outlines both classical and new approaches and incorporates numerous real-world statistical problems that illustrate modeling approaches that are applicable to a broad range of audiences, including applied statisticians and practicing engineers and scientists. The book continues to review models of growth and decay, systems where competition and interaction add to the complextiy of the model while discussing both classical and non-classical data analysis methods. This Second Edition now features further coverage of momentum based investing practices and resampling techniques, showcasing their importance and expediency in the real world. The author provides applications of empirical modeling, such as computer modeling of the AIDS epidemic to explain why North America has most of the AIDS cases in the First World and data-based strategies that allow individual investors to build their own investment portfolios. Throughout the book, computer-based analysis is emphasized and newly added and updated exercises allow readers to test their comprehension of the presented material. Empirical Model Building, Second Edition is a suitable book for modeling courses at the upper-undergraduate and graduate levels. It is also an excellent reference for applied statisticians and researchers who carry out quantitative modeling in their everyday work.
Extracting Knowledge From Time Series
Boris P. Bezruchko,Dmitry A. Smirnov
Author : Boris P. Bezruchko,Dmitry A. Smirnov
Publisher : Springer Science & Business Media
Page : 416 pages
File Size : 48,5 Mb
Release : 2010-09-03
Category : Science
ISBN : 9783642126017
Extracting Knowledge From Time Series by Boris P. Bezruchko,Dmitry A. Smirnov Pdf
Mathematical modelling is ubiquitous. Almost every book in exact science touches on mathematical models of a certain class of phenomena, on more or less speci?c approaches to construction and investigation of models, on their applications, etc. As many textbooks with similar titles, Part I of our book is devoted to general qu- tions of modelling. Part II re?ects our professional interests as physicists who spent much time to investigations in the ?eld of non-linear dynamics and mathematical modelling from discrete sequences of experimental measurements (time series). The latter direction of research is known for a long time as “system identi?cation” in the framework of mathematical statistics and automatic control theory. It has its roots in the problem of approximating experimental data points on a plane with a smooth curve. Currently, researchers aim at the description of complex behaviour (irregular, chaotic, non-stationary and noise-corrupted signals which are typical of real-world objects and phenomena) with relatively simple non-linear differential or difference model equations rather than with cumbersome explicit functions of time. In the second half of the twentieth century, it has become clear that such equations of a s- ?ciently low order can exhibit non-trivial solutions that promise suf?ciently simple modelling of complex processes; according to the concepts of non-linear dynamics, chaotic regimes can be demonstrated already by a third-order non-linear ordinary differential equation, while complex behaviour in a linear model can be induced either by random in?uence (noise) or by a very high order of equations.
Empirical Modeling and Data Analysis for Engineers and Applied Scientists
Scott A. Pardo
Author : Scott A. Pardo
Publisher : Springer
Page : 247 pages
File Size : 50,7 Mb
Release : 2016-07-19
Category : Mathematics
ISBN : 9783319327686
Empirical Modeling and Data Analysis for Engineers and Applied Scientists by Scott A. Pardo Pdf
This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions. While science is about discovery, the primary paradigm of engineering and "applied science" is design. Scientists are in the discovery business and want, in general, to understand the natural world rather than to alter it. In contrast, engineers and applied scientists design products, processes, and solutions to problems. That said, statistics, as a discipline, is mostly oriented toward the discovery paradigm. Young engineers come out of their degree programs having taken courses such as "Statistics for Engineers and Scientists" without any clear idea as to how they can use statistical methods to help them design products or processes. Many seem to think that statistics is only useful for demonstrating that a device or process actually does what it was designed to do. Statistics courses emphasize creating predictive or classification models - predicting nature or classifying individuals, and statistics is often used to prove or disprove phenomena as opposed to aiding in the design of a product or process. In industry however, Chemical Engineers use designed experiments to optimize petroleum extraction; Manufacturing Engineers use experimental data to optimize machine operation; Industrial Engineers might use data to determine the optimal number of operators required in a manual assembly process. This text teaches engineering and applied science students to incorporate empirical investigation into such design processes. Much of the discussion in this book is about models, not whether the models truly represent reality but whether they adequately represent reality with respect to the problems at hand; many ideas focus on how to gather data in the most efficient way possible to construct adequate models. Includes chapters on subjects not often seen together in a single text (e.g., measurement systems, mixture experiments, logistic regression, Taguchi methods, simulation) Techniques and concepts introduced present a wide variety of design situations familiar to engineers and applied scientists and inspire incorporation of experimentation and empirical investigation into the design process. Software is integrally linked to statistical analyses with fully worked examples in each chapter; fully worked using several packages: SAS, R, JMP, Minitab, and MS Excel - also including discussion questions at the end of each chapter. The fundamental learning objective of this textbook is for the reader to understand how experimental data can be used to make design decisions and to be familiar with the most common types of experimental designs and analysis methods.
Empirical Agent-Based Modelling - Challenges and Solutions
Alexander Smajgl,Olivier Barreteau
Author : Alexander Smajgl,Olivier Barreteau
Publisher : Springer Science & Business Media
Page : 254 pages
File Size : 54,6 Mb
Release : 2013-09-12
Category : Mathematics
ISBN : 9781461461340
Empirical Agent-Based Modelling - Challenges and Solutions by Alexander Smajgl,Olivier Barreteau Pdf
This instructional book showcases techniques to parameterise human agents in empirical agent-based models (ABM). In doing so, it provides a timely overview of key ABM methodologies and the most innovative approaches through a variety of empirical applications. It features cutting-edge research from leading academics and practitioners, and will provide a guide for characterising and parameterising human agents in empirical ABM. In order to facilitate learning, this text shares the valuable experiences of other modellers in particular modelling situations. Very little has been published in the area of empirical ABM, and this contributed volume will appeal to graduate-level students and researchers studying simulation modeling in economics, sociology, ecology, and trans-disciplinary studies, such as topics related to sustainability. In a similar vein to the instruction found in a cookbook, this text provides the empirical modeller with a set of 'recipes' ready to be implemented. Agent-based modeling (ABM) is a powerful, simulation-modeling technique that has seen a dramatic increase in real-world applications in recent years. In ABM, a system is modeled as a collection of autonomous decision-making entities called “agents.” Each agent individually assesses its situation and makes decisions on the basis of a set of rules. Agents may execute various behaviors appropriate for the system they represent—for example, producing, consuming, or selling. ABM is increasingly used for simulating real-world systems, such as natural resource use, transportation, public health, and conflict. Decision makers increasingly demand support that covers a multitude of indicators that can be effectively addressed using ABM. This is especially the case in situations where human behavior is identified as a critical element. As a result, ABM will only continue its rapid growth. This is the first volume in a series of books that aims to contribute to a cultural change in the community of empirical agent-based modelling. This series will bring together representational experiences and solutions in empirical agent-based modelling. Creating a platform to exchange such experiences allows comparison of solutions and facilitates learning in the empirical agent-based modelling community. Ultimately, the community requires such exchange and learning to test approaches and, thereby, to develop a robust set of techniques within the domain of empirical agent-based modelling. Based on robust and defendable methods, agent-based modelling will become a critical tool for research agencies, decision making and decision supporting agencies, and funding agencies. This series will contribute to more robust and defendable empirical agent-based modelling.
Air Pollution Modeling and Its Application IX
H. Van Dop,George Kallos
Author : H. Van Dop,George Kallos
Publisher : Springer Science & Business Media
Page : 820 pages
File Size : 47,7 Mb
Release : 1992-11-30
Category : Gardening
ISBN : 0306442485
Air Pollution Modeling and Its Application IX by H. Van Dop,George Kallos Pdf
Proceedings of the 19th NATO/CCMS International Technical Meeting on Air Pollution Modeling and its Application held in Crete, Greece, September 29-October 4, 1991
Empirical Model Building
James R. Thompson
Author : James R. Thompson
Publisher : John Wiley & Sons
Page : 264 pages
File Size : 46,5 Mb
Release : 2009-09-25
Category : Mathematics
ISBN : 9780470317457
Empirical Model Building by James R. Thompson Pdf
A hands-on approach to the basic principles of empirical model building. Includes a series of real-world statistical problems illustrating modeling skills and techniques. Covers models of growth and decay, systems where competition and interaction add to the complexity of the model, and discusses both classical and nonclassical data analysis methods.
Process Modelling and Model Analysis
Ian T. Cameron,Katalin Hangos
Author : Ian T. Cameron,Katalin Hangos
Publisher : Elsevier
Page : 561 pages
File Size : 42,6 Mb
Release : 2001-05-23
Category : Technology & Engineering
ISBN : 9780080514925
Process Modelling and Model Analysis by Ian T. Cameron,Katalin Hangos Pdf
Process Modelling and Model Analysis describes the use of models in process engineering. Process engineering is all about manufacturing--of just about anything! To manage processing and manufacturing systematically, the engineer has to bring together many different techniques and analyses of the interaction between various aspects of the process. For example, process engineers would apply models to perform feasibility analyses of novel process designs, assess environmental impact, and detect potential hazards or accidents. To manage complex systems and enable process design, the behavior of systems is reduced to simple mathematical forms. This book provides a systematic approach to the mathematical development of process models and explains how to analyze those models. Additionally, there is a comprehensive bibliography for further reading, a question and answer section, and an accompanying Web site developed by the authors with additional data and exercises. Introduces a structured modeling methodology emphasizing the importance of the modeling goal and including key steps such as model verification, calibration, and validation Focuses on novel and advanced modeling techniques such as discrete, hybrid, hierarchical, and empirical modeling Illustrates the notions, tools, and techniques of process modeling with examples and advances applications
Age-Period-Cohort Analysis
Yang Yang,Kenneth C. Land
Author : Yang Yang,Kenneth C. Land
Publisher : CRC Press
Page : 352 pages
File Size : 51,9 Mb
Release : 2016-04-19
Category : Mathematics
ISBN : 9781466507531
Age-Period-Cohort Analysis by Yang Yang,Kenneth C. Land Pdf
This book explores the ways in which statistical models, methods, and research designs can be used to open new possibilities for APC analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three research designs: age-by-time period tables of population rates or proportions, repeated cross-section sample surveys, and accelerated longitudinal panel studies. They show how the empirical application of the models to various problems leads to many fascinating findings on how outcome variables develop along the age, period, and cohort dimensions.
Response Modeling Methodology
Haim Shore
Author : Haim Shore
Publisher : World Scientific
Page : 460 pages
File Size : 51,7 Mb
Release : 2005-04-26
Category : Technology & Engineering
ISBN : 9789814481342
Response Modeling Methodology by Haim Shore Pdf
This book introduces a new approach, denoted RMM, for an empirical modeling of a response variation, relating to both systematic variation and random variation. In the book, the developer of RMM discusses the required properties of empirical modeling and evaluates how current approaches conform to these requirements. In addition, he explains the motivation for the development of the new methodology, introduces in detail the new approach and its estimation procedures, and shows how it may provide an excellent alternative to current approaches for empirical modeling (like Generalized Linear Modeling, GLM). The book also demonstrates that a myriad of current relational models, developed independently in various engineering and scientific disciplines, are in fact special cases of the RMM model, and so are many current statistical distributions, transformations and approximations. Contents: Current Models and Modeling Methodologies:Relational Models in Engineering and the Sciences (Monotone Convex/Concave Relationships)Shared Features and “The Ladder”Approaches to Model Systematic VariationApproaches to Model Random VariationThe Requirements and Evaluation of ComplianceRMM — Developing and Evaluating the General Approach:The RMM ModelEstimating the Relational ModelThe RMM Error DistributionFitting Procedures (for the Error Distribution)Estimating the Error DistributionSpecial Cases of the RMM ModelEvaluating RMM for ComplianceModeling Systematic Variation — Applications:Comparative Solutions for Relational ModelsReliability Engineering (with Censoring)Software Reliability-Growth ModelsModeling a Chemo-ResponseForecasting S-Shaped Diffusion ProcessesModeling Random Variation — Applications:RMM Distributional ApproximationsInverse Normalizing TransformationsPiece-Wise Linear ApproximationsGeneral Control ChartsInventory Analysis Readership: Graduate students, researchers and other professionals employing empirical modeling in areas like Quality and Reliability, Operations Research, Operations Management and Applied Statistics. Keywords:Box-Cox Transformation;Chemical Engineering;Distribution;Fitting Empirical Modeling;Generalized Linear Models;Nonlinear Regression Analysis;Operations Management;Operations Research;Quality and Reliability Engineering;Response Modeling MethodologyKey Features:Demonstrates how the new approach (RMM) differs from current approaches in that both the structure of the model and its parameters are determined via data-driven proceduresDemonstrates that a single comprehensive methodology may provide a good platform for empirical modeling of both systematic variation (relational modeling) and random variation (variation that is captured by a statistical distribution with stable parameters)Provides handy procedures to apply to the new methodology, accompanied by detailed numerical examples for the implementation of these procedures
Empirical Modeling of Exchange Rate Dynamics
Francis X. Diebold
Author : Francis X. Diebold
Publisher : Springer Science & Business Media
Page : 153 pages
File Size : 44,6 Mb
Release : 2012-12-06
Category : Business & Economics
ISBN : 9783642456411
Empirical Modeling of Exchange Rate Dynamics by Francis X. Diebold Pdf
Structural exchange rate modeling has proven extremely difficult during the recent post-1973 float. The disappointment climaxed with the papers of Meese and Rogoff (1983a, 1983b), who showed that a "naive" random walk model distinctly dominated received theoretical models in terms of predictive performance for the major dollar spot rates. One purpose of this monograph is to seek the reasons for this failure by exploring the temporal behavior of seven major dollar exchange rates using nonstructural time-series methods. The Meese-Rogoff finding does not mean that exchange rates evolve as random walks; rather it simply means that the random walk is a better stochastic approximation than any of their other candidate models. In this monograph, we use optimal model specification techniques, including formal unit root tests which allow for trend, and find that all of the exchange rates studied do in fact evolve as random walks or random walks with drift (to a very close approximation). This result is consistent with efficient asset markets, and provides an explanation for the Meese-Rogoff results. Far more subtle forces are at work, however, which lead to interesting econometric problems and have implications for the measurement of exchange rate volatility and moment structure. It is shown that all exchange rates display substantial conditional heteroskedasticity. A particularly reasonable parameterization of this conditional heteroskedasticity, which captures the observed clustering of prediction error variances, is developed in Chapter 2.
Empirical Asset Pricing Models
Jau-Lian Jeng
Author : Jau-Lian Jeng
Publisher : Springer
Page : 268 pages
File Size : 53,7 Mb
Release : 2018-03-19
Category : Business & Economics
ISBN : 9783319741925
Empirical Asset Pricing Models by Jau-Lian Jeng Pdf
This book analyzes the verification of empirical asset pricing models when returns of securities are projected onto a set of presumed (or observed) factors. Particular emphasis is placed on the verification of essential factors and features for asset returns through model search approaches, in which non-diversifiability and statistical inferences are considered. The discussion reemphasizes the necessity of maintaining a dichotomy between the nondiversifiable pricing kernels and the individual components of stock returns when empirical asset pricing models are of interest. In particular, the model search approach (with this dichotomy emphasized) for empirical model selection of asset pricing is applied to discover the pricing kernels of asset returns.
Handbook of Research Methods and Applications in Empirical Macroeconomics
Nigar Hashimzade,Michael A. Thornton
Author : Nigar Hashimzade,Michael A. Thornton
Publisher : Edward Elgar Publishing
Page : 627 pages
File Size : 49,6 Mb
Release : 2013-01-01
Category : Business & Economics
ISBN : 9780857931023
Handbook of Research Methods and Applications in Empirical Macroeconomics by Nigar Hashimzade,Michael A. Thornton Pdf
This comprehensive Handbook presents the current state of art in the theory and methodology of macroeconomic data analysis. It is intended as a reference for graduate students and researchers interested in exploring new methodologies, but can also be employed as a graduate text. The Handbook concentrates on the most important issues, models and techniques for research in macroeconomics, and highlights the core methodologies and their empirical application in an accessible manner. Each chapter is largely self-contained, whilst the comprehensive introduction provides an overview of the key statistical concepts and methods. All of the chapters include the essential references for each topic and provide a sound guide for further reading. Topics covered include unit roots, non-linearities and structural breaks, time aggregation, forecasting, the Kalman filter, generalised method of moments, maximum likelihood and Bayesian estimation, vector autoregressive, dynamic stochastic general equilibrium and dynamic panel models. Presenting the most important models and techniques for empirical research, this Handbook will appeal to students, researchers and academics working in empirical macro and econometrics.
Expert Knowledge and Its Application in Landscape Ecology
Ajith H. Perera,C. Ashton Drew,Chris J. Johnson
Author : Ajith H. Perera,C. Ashton Drew,Chris J. Johnson
Publisher : Springer Science & Business Media
Page : 313 pages
File Size : 46,9 Mb
Release : 2011-10-21
Category : Science
ISBN : 9781461410348
Expert Knowledge and Its Application in Landscape Ecology by Ajith H. Perera,C. Ashton Drew,Chris J. Johnson Pdf
Typically, landscape ecologists use empirical observations to conduct research and devise solutions for applied problems in conservation and management. In some instances, they rely on advice and input of experienced professionals in both developing and applying knowledge. Given the wealth of expert knowledge and the risks of its informal and implicit applications in landscape ecology, it is necessary to formally recognize and characterize expert knowledge and bring rigor to methods for its applications. In this context, the broad goal of this book is to introduce the concept of expert knowledge and examine its role in landscape ecological applications. We plan to do so in three steps: First we introduce the topic to landscape ecologists, explore salient characteristics of experts and expert knowledge, and describe methods used in capturing and formalizing that knowledge. Second, we present examples of research in landscape ecology from a variety of ecosystems and geographic locations that formally incorporate expert knowledge. These case studies address a range of topics that will interest landscape ecologists and other resource management and conservation professionals including the specific roles of expert knowledge in developing, testing, parameterizing, and applying models; estimating the uncertainty in expert knowledge; developing methods of formalizing and incorporating expert knowledge; and using expert knowledge as competing models and a source of alternate hypotheses. Third, we synthesize the state of knowledge on this topic and critically examine the advantages and disadvantages of incorporating expert knowledge in landscape ecological applications. The disciplinary subject areas we address are broad and cover much of the scope of contemporary landscape ecology, including broad-scale forest management and conservation, quantifying forest disturbances and succession, conservation of habitats for a range of avian and mammal species, vulnerability and conservation of marine ecosystems, and the spread and impacts of invasive plants. This text incorporates the collective experience and knowledge of over 35 researchers in landscape ecology representing a diverse range of disciplinary subject areas and geographic locations. Through this text, we will catalyze further thought and investigations on expert knowledge among the target readership of researchers, practitioners, and graduate students in landscape ecology.