Multivariate Statistical Modeling In Engineering And Management

Author : Jhareswar Maiti
Publisher : CRC Press
Page : 421 pages
File Size : 50,6 Mb
Release : 2022-10-25
Category : Business & Economics
ISBN : 9781000618426

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Multivariate Statistical Modeling in Engineering and Management by Jhareswar Maiti Pdf

The book focuses on problem solving for practitioners and model building for academicians under multivariate situations. This book helps readers in understanding the issues, such as knowing variability, extracting patterns, building relationships, and making objective decisions. A large number of multivariate statistical models are covered in the book. The readers will learn how a practical problem can be converted to a statistical problem and how the statistical solution can be interpreted as a practical solution. Key features: Links data generation process with statistical distributions in multivariate domain Provides step by step procedure for estimating parameters of developed models Provides blueprint for data driven decision making Includes practical examples and case studies relevant for intended audiences The book will help everyone involved in data driven problem solving, modeling and decision making.

Author : Jhareswar Maiti
Publisher : CRC Press
Page : 637 pages
File Size : 41,6 Mb
Release : 2022-10-25
Category : Mathematics
ISBN : 9781000618396

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Multivariate Statistical Modeling in Engineering and Management by Jhareswar Maiti Pdf

The book focuses on problem solving for practitioners and model building for academicians under multivariate situations. This book helps readers in understanding the issues, such as knowing variability, extracting patterns, building relationships, and making objective decisions. A large number of multivariate statistical models are covered in the book. The readers will learn how a practical problem can be converted to a statistical problem and how the statistical solution can be interpreted as a practical solution. Key features: Links data generation process with statistical distributions in multivariate domain Provides step by step procedure for estimating parameters of developed models Provides blueprint for data driven decision making Includes practical examples and case studies relevant for intended audiences The book will help everyone involved in data driven problem solving, modeling and decision making.

Author : Kai Yang,Jayant Trewn
Publisher : McGraw Hill Professional
Page : 319 pages
File Size : 42,9 Mb
Release : 2004-02-25
Category : Technology & Engineering
ISBN : 9780071432085

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Multivariate Statistical Methods in Quality Management by Kai Yang,Jayant Trewn Pdf

Multivariate statistical methods are an essential component of quality engineering data analysis. This monograph provides a solid background in multivariate statistical fundamentals and details key multivariate statistical methods, including simple multivariate data graphical display and multivariate data stratification. * Graphical multivariate data display * Multivariate regression and path analysis * Multivariate process control charts * Six sigma and multivariate statistical methods

Author : RONG. RIGDON PAN (STEVEN E.. CHAMP, CHARLES.)
Publisher : Unknown
Page : 128 pages
File Size : 50,6 Mb
Release : 2019
Category : Electronic
ISBN : 1138197823

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MULTIVARIATE STATISTICAL PROCESS CONTROL by RONG. RIGDON PAN (STEVEN E.. CHAMP, CHARLES.) Pdf

Author : Beata Akselsen
Publisher : Unknown
Page : 268 pages
File Size : 54,5 Mb
Release : 2016-04-01
Category : Electronic
ISBN : 1681174626

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Multivariate Analysis in Management, Engineering and the Sciences by Beata Akselsen Pdf

"Many statistical techniques focus on just one or two variables; Multivariate analysis (MVA) techniques allow more than two variables to be analysed at once. Recently statistical knowledge has become an important requirement and occupies a prominent position in the exercise of various professions. In the real world, the processes have a large volume of data and are naturally multivariate and as such, require a proper treatment. For these conditions it is difficult or practically impossible to use methods of univariate statistics. Researchers use multivariate procedures in studies that involve more than one dependent variable (also known as the outcome or phenomenon of interest), more than one independent variable (also known as a predictor) or both. This type of analysis is desirable because researchers often hypothesize that a given outcome of interest is effected or influenced by more than one thing. Uses for multivariate analysis include: design for capability (also known as capability-based design); inverse design, where any variable can be treated as an independent variable; analysis of alternatives (AoA), the selection of concepts to fulfil a customer need; analysis of concepts with respect to changing scenarios; identification of critical designdrivers and correlations across hierarchical levels. Multivariate Analysis in Management, Engineering and the Sciences presents significant topics on fundamental theoretical aspects of the field as well as on other aspects concerned with significant applications of new theoretical methods. Through real-life applications of statistical methodology, this book elucidates the implications of behavioural science studies for statistical analysis. In addition to helping to stimulate research in multivariate analysis, the book aims to bring about interactions among mathematical statisticians, probabilists, and scientists in other disciplines broadly interested in the area. "

Author : Jiuping Xu,John A. Fry,Benjamin Lev,Asaf Hajiyev
Publisher : Springer Science & Business Media
Page : 770 pages
File Size : 40,6 Mb
Release : 2013-09-20
Category : Business & Economics
ISBN : 9783642400780

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Proceedings of the Seventh International Conference on Management Science and Engineering Management by Jiuping Xu,John A. Fry,Benjamin Lev,Asaf Hajiyev Pdf

This book presents the proceedings of the Seventh International Conference on Management Science and Engineering Management (ICMSEM2013) held from November 7 to 9, 2013 at Drexel University, Philadelphia, Pennsylvania, USA and organized by the International Society of Management Science and Engineering Management, Sichuan University (Chengdu, China) and Drexel University (Philadelphia, Pennsylvania, USA). The goals of the Conference are to foster international research collaborations in Management Science and Engineering Management as well as to provide a forum to present current research findings. The selected papers cover various areas in management science and engineering management, such as Decision Support Systems, Multi-Objective Decisions, Uncertain Decisions, Computational Mathematics, Information Systems, Logistics and Supply Chain Management, Relationship Management, Scheduling and Control, Data Warehousing and Data Mining, Electronic Commerce, Neural Networks, Stochastic Models and Simulation, Fuzzy Programming, Heuristics Algorithms, Risk Control, Organizational Behavior, Green Supply Chains, and Carbon Credits. The proceedings introduce readers to novel ideas on and different problem-solving methods in Management Science and Engineering Management. We selected excellent papers from all over the world, integrating their expertise and ideas in order to improve research on Management Science and Engineering Management.

Author : Marius Hofert,Ivan Kojadinovic,Martin Mächler,Jun Yan
Publisher : Springer
Page : 267 pages
File Size : 45,5 Mb
Release : 2019-01-09
Category : Business & Economics
ISBN : 9783319896359

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Elements of Copula Modeling with R by Marius Hofert,Ivan Kojadinovic,Martin Mächler,Jun Yan Pdf

This book introduces the main theoretical findings related to copulas and shows how statistical modeling of multivariate continuous distributions using copulas can be carried out in the R statistical environment with the package copula (among others). Copulas are multivariate distribution functions with standard uniform univariate margins. They are increasingly applied to modeling dependence among random variables in fields such as risk management, actuarial science, insurance, finance, engineering, hydrology, climatology, and meteorology, to name a few. In the spirit of the Use R! series, each chapter combines key theoretical definitions or results with illustrations in R. Aimed at statisticians, actuaries, risk managers, engineers and environmental scientists wanting to learn about the theory and practice of copula modeling using R without an overwhelming amount of mathematics, the book can also be used for teaching a course on copula modeling.

Author : Srikanta Mishra,Akhil Datta-Gupta
Publisher : Elsevier
Page : 250 pages
File Size : 55,6 Mb
Release : 2017-10-27
Category : Science
ISBN : 9780128032800

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Applied Statistical Modeling and Data Analytics by Srikanta Mishra,Akhil Datta-Gupta Pdf

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

Author : V.V. Rykov,N. Balakrishnan,M.S. Nikulin
Publisher : Springer Science & Business Media
Page : 465 pages
File Size : 55,9 Mb
Release : 2010-11-02
Category : Technology & Engineering
ISBN : 9780817649715

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Mathematical and Statistical Models and Methods in Reliability by V.V. Rykov,N. Balakrishnan,M.S. Nikulin Pdf

The book is a selection of invited chapters, all of which deal with various aspects of mathematical and statistical models and methods in reliability. Written by renowned experts in the field of reliability, the contributions cover a wide range of applications, reflecting recent developments in areas such as survival analysis, aging, lifetime data analysis, artificial intelligence, medicine, carcinogenesis studies, nuclear power, financial modeling, aircraft engineering, quality control, and transportation. Mathematical and Statistical Models and Methods in Reliability is an excellent reference text for researchers and practitioners in applied probability and statistics, industrial statistics, engineering, medicine, finance, transportation, the oil and gas industry, and artificial intelligence.

Author : Fouzi Harrou,Abdelhafid Zeroual,Mohamad Mazen Hittawe,Ying Sun
Publisher : Elsevier
Page : 270 pages
File Size : 48,6 Mb
Release : 2021-10-05
Category : Transportation
ISBN : 9780128234334

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Road Traffic Modeling and Management by Fouzi Harrou,Abdelhafid Zeroual,Mohamad Mazen Hittawe,Ying Sun Pdf

Road Traffic Modeling and Management: Using Statistical Monitoring and Deep Learning provides a framework for understanding and enhancing road traffic monitoring and management. The book examines commonly used traffic analysis methodologies as well the emerging methods that use deep learning methods. Other sections discuss how to understand statistical models and machine learning algorithms and how to apply them to traffic modeling, estimation, forecasting and traffic congestion monitoring. Providing both a theoretical framework along with practical technical solutions, this book is ideal for researchers and practitioners who want to improve the performance of intelligent transportation systems. Provides integrated, up-to-date and complete coverage of the key components for intelligent transportation systems: traffic modeling, forecasting, estimation and monitoring Uses methods based on video and time series data for traffic modeling and forecasting Includes case studies, key processes guidance and comparisons of different methodologies

Author : Ioannis S. Trianntafyllou,Mangey Ram
Publisher : CRC Press
Page : 223 pages
File Size : 40,8 Mb
Release : 2022-09-27
Category : Technology & Engineering
ISBN : 9781000614756

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Statistical Modeling of Reliability Structures and Industrial Processes by Ioannis S. Trianntafyllou,Mangey Ram Pdf

This reference text introduces advanced topics in the field of reliability engineering, introduces statistical modeling techniques, and probabilistic methods for diverse applications. It comprehensively covers important topics including consecutive-type reliability systems, coherent structures, multi-scale statistical modeling, the performance of reliability structures, big data analytics, prognostics, and health management. It covers real-life applications including optimization of telecommunication networks, complex infrared detecting systems, oil pipeline systems, and vacuum systems in accelerators or spacecraft relay stations. The text will serve as an ideal reference book for graduate students and academic researchers in the fields of industrial engineering, manufacturing science, mathematics, and statistics.

Author : Gerald J. Hahn,Samuel S. Shapiro
Publisher : John Wiley & Sons
Page : 384 pages
File Size : 42,6 Mb
Release : 1967
Category : Mathematics
ISBN : UOM:39015013771673

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Statistical Models in Engineering by Gerald J. Hahn,Samuel S. Shapiro Pdf

A detailed treatment on the use of statistical models representing physical phenomena. Considers the relevance of the popular normal distribution models and the applicability of exponential distribution in reliability problems. Introduces and discusses the use of alternate models such as gamma, beta and Weibull distributions. Features expansive coverage of system performance and describes an exact method known as the transformation of variables. Deals with techniques on assessing the adequacy of a chosen model including both graphical and analytical procedures. Contains scores of illustrative examples, most of which have been adapted from actual problems.

Author : Birgir Hrafnkelsson
Publisher : Springer Nature
Page : 256 pages
File Size : 51,9 Mb
Release : 2023-12-10
Category : Mathematics
ISBN : 9783031397912

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Statistical Modeling Using Bayesian Latent Gaussian Models by Birgir Hrafnkelsson Pdf

This book focuses on the statistical modeling of geophysical and environmental data using Bayesian latent Gaussian models. The structure of these models is described in a thorough introductory chapter, which explains how to construct prior densities for the model parameters, how to infer the parameters using Bayesian computation, and how to use the models to make predictions. The remaining six chapters focus on the application of Bayesian latent Gaussian models to real examples in glaciology, hydrology, engineering seismology, seismology, meteorology and climatology. These examples include: spatial predictions of surface mass balance; the estimation of Antarctica’s contribution to sea-level rise; the estimation of rating curves for the projection of water level to discharge; ground motion models for strong motion; spatial modeling of earthquake magnitudes; weather forecasting based on numerical model forecasts; and extreme value analysis of precipitation on a high-dimensional grid. The book is aimed at graduate students and experts in statistics, geophysics, environmental sciences, engineering, and related fields.

Author : Andriëtte Bekker,Johannes T. Ferreira,Mohammad Arashi,Ding-Geng Chen
Publisher : Springer Nature
Page : 434 pages
File Size : 42,9 Mb
Release : 2022-12-15
Category : Mathematics
ISBN : 9783031139710

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Innovations in Multivariate Statistical Modeling by Andriëtte Bekker,Johannes T. Ferreira,Mohammad Arashi,Ding-Geng Chen Pdf

Multivariate statistical analysis has undergone a rich and varied evolution during the latter half of the 20th century. Academics and practitioners have produced much literature with diverse interests and with varying multidisciplinary knowledge on different topics within the multivariate domain. Due to multivariate algebra being of sustained interest and being a continuously developing field, its appeal breaches laterally across multiple disciplines to act as a catalyst for contemporary advances, with its core inferential genesis remaining in that of statistics. It is exactly this varied evolution caused by an influx in data production, diffusion, and understanding in scientific fields that has blurred many lines between disciplines. The cross-pollination between statistics and biology, engineering, medical science, computer science, and even art, has accelerated the vast amount of questions that statistical methodology has to answer and report on. These questions are often multivariate in nature, hoping to elucidate uncertainty on more than one aspect at the same time, and it is here where statistical thinking merges mathematical design with real life interpretation for understanding this uncertainty. Statistical advances benefit from these algebraic inventions and expansions in the multivariate paradigm. This contributed volume aims to usher novel research emanating from a multivariate statistical foundation into the spotlight, with particular significance in multidisciplinary settings. The overarching spirit of this volume is to highlight current trends, stimulate a focus on, and connect multidisciplinary dots from and within multivariate statistical analysis. Guided by these thoughts, a collection of research at the forefront of multivariate statistical thinking is presented here which has been authored by globally recognized subject matter experts.

Author : H. Bozdogan,Arjun K. Gupta
Publisher : Springer
Page : 208 pages
File Size : 47,9 Mb
Release : 1987-10-31
Category : Mathematics
ISBN : UCAL:B4407248

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Multivariate Statistical Modeling and Data Analysis by H. Bozdogan,Arjun K. Gupta Pdf

This volume contains the Proceedings of the Advanced Symposium on Multivariate Modeling and Data Analysis held at the 64th Annual Heeting of the Virginia Academy of Sciences (VAS)--American Statistical Association's Vir ginia Chapter at James Madison University in Harrisonburg. Virginia during Hay 15-16. 1986. This symposium was sponsored by financial support from the Center for Advanced Studies at the University of Virginia to promote new and modern information-theoretic statist ical modeling procedures and to blend these new techniques within the classical theory. Multivariate statistical analysis has come a long way and currently it is in an evolutionary stage in the era of high-speed computation and computer technology. The Advanced Symposium was the first to address the new innovative approaches in multi variate analysis to develop modern analytical and yet practical procedures to meet the needs of researchers and the societal need of statistics. vii viii PREFACE Papers presented at the Symposium by e1l11lJinent researchers in the field were geared not Just for specialists in statistics, but an attempt has been made to achieve a well balanced and uniform coverage of different areas in multi variate modeling and data analysis. The areas covered included topics in the analysis of repeated measurements, cluster analysis, discriminant analysis, canonical cor relations, distribution theory and testing, bivariate densi ty estimation, factor analysis, principle component analysis, multidimensional scaling, multivariate linear models, nonparametric regression, etc.