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Author: Chandra Shekhar
Publisher: CRC Press
ISBN: 1040031471
Category : Technology & Engineering
Languages : en
Pages : 249
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Book Description
In an era dominated by mathematical and statistical models, this book unravels the profound significance of these tools in decoding uncertainties within numerical, observational, and calculation-based data. From governmental institutions to private entities, statistical prediction models provide a critical framework for optimal decision-making, offering nuanced insights into diverse realms, from climate to production and beyond. This book ·Serves as a comprehensive resource in statistical modeling, methodologies, and optimization techniques across various domains. ·Features contributions from global authors; the compilation comprises 10 insightful chapters, each addressing critical aspects of estimation and optimization through statistical modeling. ·Covers a spectrum of topics, from non-parametric goodness-of-fit statistics to Bayesian applications; the book explores novel resampling methods, advanced measures for empirical mode, and transient behavior analysis in queueing systems. ·Includes asymptotic properties of goodness-of-fit statistics, practical applications of Bayesian Statistics, modifications to the Hard EM algorithm, and explicit transient probabilities. ·Culminates with an exploration of an inventory model for perishable items, integrating preservation technology and learning effects to determine the economic order quantity. This book stands as a testament to global collaboration, offering a rich tapestry of commendable statistical and mathematical modeling alongside real-world problem-solving. It is poised to ignite further exploration, discussion, and innovation in the realms of statistical modeling and optimization.
Author: Chandra Shekhar
Publisher: CRC Press
ISBN: 1040031471
Category : Technology & Engineering
Languages : en
Pages : 249
Get Book Here
Book Description
In an era dominated by mathematical and statistical models, this book unravels the profound significance of these tools in decoding uncertainties within numerical, observational, and calculation-based data. From governmental institutions to private entities, statistical prediction models provide a critical framework for optimal decision-making, offering nuanced insights into diverse realms, from climate to production and beyond. This book ·Serves as a comprehensive resource in statistical modeling, methodologies, and optimization techniques across various domains. ·Features contributions from global authors; the compilation comprises 10 insightful chapters, each addressing critical aspects of estimation and optimization through statistical modeling. ·Covers a spectrum of topics, from non-parametric goodness-of-fit statistics to Bayesian applications; the book explores novel resampling methods, advanced measures for empirical mode, and transient behavior analysis in queueing systems. ·Includes asymptotic properties of goodness-of-fit statistics, practical applications of Bayesian Statistics, modifications to the Hard EM algorithm, and explicit transient probabilities. ·Culminates with an exploration of an inventory model for perishable items, integrating preservation technology and learning effects to determine the economic order quantity. This book stands as a testament to global collaboration, offering a rich tapestry of commendable statistical and mathematical modeling alongside real-world problem-solving. It is poised to ignite further exploration, discussion, and innovation in the realms of statistical modeling and optimization.
Author: Chandra Shekhar
Publisher: CRC Press
ISBN: 104003134X
Category : Technology & Engineering
Languages : en
Pages : 195
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Book Description
In the dynamic landscape of modern data analysis, this curated guide by global experts explores the latest in statistical methodologies, modeling techniques, and optimization strategies. This comprehensive text offers insights into diverse fields such as engineering, economics, medicine, and agriculture, addressing real-world challenges. It delves into the intricacies of the Lomax distribution under a Type II censoring scheme, exploring various loss functions. The compilation uncovers estimators for population proportion, product of two population means, and more, supported by empirical and simulation studies. Additionally, it scrutinizes the prevalence of caesarean section deliveries in India, correlating with socio-economic factors. This book · Traverses diverse fields for insights into real-world challenges. · Delves into the intricacies of the Lomax distribution under a Type II censoring scheme. · Uncovers estimators supported by empirical and simulation studies. · Scrutinizes the prevalence of caesarean section deliveries in India, correlating with socio-economic factors. This compilation promises a holistic exploration of advanced statistical and optimization methods, offering readers valuable insights into their pragmatic applications across a spectrum of real-world issues.
Author: Rakhee Kulshrestha
Publisher: CRC Press
ISBN: 1000288676
Category : Mathematics
Languages : en
Pages : 172
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Book Description
This book covers an interdisciplinary approach for understanding mathematical modeling by offering a collection of models, solved problems related to the models, the methodologies employed, and the results using projects and case studies with insight into the operation of substantial real-time systems. The book covers a broad scope in the areas of statistical science, probability, stochastic processes, fluid dynamics, supply chain, optimization, and applications. It discusses advanced topics and the latest research findings, uses an interdisciplinary approach for real-time systems, offers a platform for integrated research, and identifies the gaps in the field for further research. The book is for researchers, students, and teachers that share a goal of learning advanced topics and the latest research in mathematical modeling.
Author: National Research Council
Publisher: National Academies Press
ISBN: 0309176085
Category : Computers
Languages : en
Pages : 83
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Book Description
This book identifies challenges and opportunities in the development and implementation of software that contain significant statistical content. While emphasizing the relevance of using rigorous statistical and probabilistic techniques in software engineering contexts, it presents opportunities for further research in the statistical sciences and their applications to software engineering. It is intended to motivate and attract new researchers from statistics and the mathematical sciences to attack relevant and pressing problems in the software engineering setting. It describes the "big picture," as this approach provides the context in which statistical methods must be developed. The book's survey nature is directed at the mathematical sciences audience, but software engineers should also find the statistical emphasis refreshing and stimulating. It is hoped that the book will have the effect of seeding the field of statistical software engineering by its indication of opportunities where statistical thinking can help to increase understanding, productivity, and quality of software and software production.
Author: Snezhana Gocheva-Ilieva
Publisher: Mdpi AG
ISBN: 9783036526928
Category : Mathematics
Languages : en
Pages : 184
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Book Description
The modeling and processing of empirical data is one of the main subjects and goals of statistics. Nowadays, with the development of computer science, the extraction of useful and often hidden information and patterns from data sets of different volumes and complex data sets in warehouses has been added to these goals. New and powerful statistical techniques with machine learning (ML) and data mining paradigms have been developed. To one degree or another, all of these techniques and algorithms originate from a rigorous mathematical basis, including probability theory and mathematical statistics, operational research, mathematical analysis, numerical methods, etc. Popular ML methods, such as artificial neural networks (ANN), support vector machines (SVM), decision trees, random forest (RF), among others, have generated models that can be considered as straightforward applications of optimization theory and statistical estimation. The wide arsenal of classical statistical approaches combined with powerful ML techniques allows many challenging and practical problems to be solved. This Special Issue belongs to the section "Mathematics and Computer Science". Its aim is to establish a brief collection of carefully selected papers presenting new and original methods, data analyses, case studies, comparative studies, and other research on the topic of statistical data modeling and ML as well as their applications. Particular attention is given, but is not limited, to theories and applications in diverse areas such as computer science, medicine, engineering, banking, education, sociology, economics, among others. The resulting palette of methods, algorithms, and applications for statistical modeling and ML presented in this Special Issue is expected to contribute to the further development of research in this area. We also believe that the new knowledge acquired here as well as the applied results are attractive and useful for young scientists, doctoral students, and researchers from various scientific specialties.
Author: William Steve Mallios
Publisher: Wiley-Blackwell
ISBN:
Category : Mathematics
Languages : en
Pages : 256
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Book Description
Textbook for graduate level courses in applied modeling. Demonstrates the relevance of statistics in the late 20th century and the value of such modeling to the decision support system. Includes an overview of Bayesian discriminate analysis and regression systems. Annotation copyright Book News, Inc
Author: A. C. Davison
Publisher: Cambridge University Press
ISBN: 9780521734493
Category : Mathematics
Languages : en
Pages : 0
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Book Description
Models and likelihood are the backbone of modern statistics and data analysis. The coverage is unrivaled, with sections on survival analysis, missing data, Markov chains, Markov random fields, point processes, graphical models, simulation and Markov chain Monte Carlo, estimating functions, asymptotic approximations, local likelihood and spline regressions as well as on more standard topics. Anthony Davison blends theory and practice to provide an integrated text for advanced undergraduate and graduate students, researchers and practicioners. Its comprehensive coverage makes this the standard text and reference in the subject.
Author: H. Bozdogan
Publisher: Springer Science & Business Media
ISBN: 9401108544
Category : Mathematics
Languages : en
Pages : 356
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Book Description
These three volumes comprise the proceedings of the US/Japan Conference, held in honour of Professor H. Akaike, on the `Frontiers of Statistical Modeling: an Informational Approach'. The major theme of the conference was the implementation of statistical modeling through an informational approach to complex, real-world problems. Volume 1 contains papers which deal with the Theory and Methodology of Time Series Analysis. Volume 1 also contains the text of the Banquet talk by E. Parzen and the keynote lecture of H. Akaike. Volume 2 is devoted to the general topic of Multivariate Statistical Modeling, and Volume 3 contains the papers relating to Engineering and Scientific Applications. For all scientists whose work involves statistics.
Author: Tilottama Goswami
Publisher: Academic Press
ISBN: 0323972527
Category : Computers
Languages : en
Pages : 398
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Book Description
Statistical Modeling in Machine Learning: Concepts and Applications presents the basic concepts and roles of statistics, exploratory data analysis and machine learning. The various aspects of Machine Learning are discussed along with basics of statistics. Concepts are presented with simple examples and graphical representation for better understanding of techniques. This book takes a holistic approach – putting key concepts together with an in-depth treatise on multi-disciplinary applications of machine learning. New case studies and research problem statements are discussed, which will help researchers in their application areas based on the concepts of statistics and machine learning. Statistical Modeling in Machine Learning: Concepts and Applications will help statisticians, machine learning practitioners and programmers solving various tasks such as classification, regression, clustering, forecasting, recommending and more. Provides a comprehensive overview of the state-of-the-art in statistical concepts applied to Machine Learning with the help of real-life problems, applications and tutorials Presents a step-by-step approach from fundamentals to advanced techniques Includes Case Studies with both successful and unsuccessful applications of Machine Learning to understand challenges in its implementation, along with worked examples
Author: Jürgen Pilz
Publisher: Springer Nature
ISBN: 3031400550
Category : Mathematics
Languages : en
Pages : 265
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Book Description
This volume presents a selection of articles on statistical modeling and simulation, with a focus on different aspects of statistical estimation and testing problems, the design of experiments, reliability and queueing theory, inventory analysis, and the interplay between statistical inference, machine learning methods and related applications. The refereed contributions originate from the 10th International Workshop on Simulation and Statistics, SimStat 2019, which was held in Salzburg, Austria, September 2–6, 2019, and were either presented at the conference or developed afterwards, relating closely to the topics of the workshop. The book is intended for statisticians and Ph.D. students who seek current developments and applications in the field.
