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This book presents some post-estimation and predictions strategies
for the host of useful statistical models with applications in data
science. It combines statistical learning and machine learning
techniques in a unique and optimal way. It is well-known that
machine learning methods are subject to many issues relating to
bias, and consequently the mean squared error and prediction error
may explode. For this reason, we suggest shrinkage strategies to
control the bias by combining a submodel selected by a penalized
method with a model with many features. Further, the suggested
shrinkage methodology can be successfully implemented for high
dimensional data analysis. Many researchers in statistics and
medical sciences work with big data. They need to analyse this data
through statistical modelling. Estimating the model parameters
accurately is an important part of the data analysis. This book may
be a repository for developing improve estimation strategies for
statisticians. This book will help researchers and practitioners
for their teaching and advanced research, and is an excellent
textbook for advanced undergraduate and graduate courses involving
shrinkage, statistical, and machine learning. The book succinctly
reveals the bias inherited in machine learning method and
successfully provides tools, tricks and tips to deal with the bias
issue. Expertly sheds light on the fundamental reasoning for model
selection and post estimation using shrinkage and related
strategies. This presentation is fundamental, because shrinkage and
other methods appropriate for model selection and estimation
problems and there is a growing interest in this area to fill the
gap between competitive strategies. Application of these strategies
to real life data set from many walks of life. Analytical results
are fully corroborated by numerical work and numerous worked
examples are included in each chapter with numerous graphs for data
visualization. The presentation and style of the book clearly makes
it accessible to a broad audience. It offers rich, concise
expositions of each strategy and clearly describes how to use each
estimation strategy for the problem at hand. This book emphasizes
that statistics/statisticians can play a dominant role in solving
Big Data problems, and will put them on the precipice of scientific
discovery. The book contributes novel methodologies for HDDA and
will open a door for continued research in this hot area. The
practical impact of the proposed work stems from wide applications.
The developed computational packages will aid in analyzing a broad
range of applications in many walks of life.
Big Data and Information Theory are a binding force between various
areas of knowledge that allow for societal advancement. Rapid
development of data analytic and information theory allows
companies to store vast amounts of information about production,
inventory, service, and consumer activities. More powerful CPUs and
cloud computing make it possible to do complex optimization instead
of using heuristic algorithms, as well as instant rather than
offline decision-making. The era of "big data" challenges includes
analysis, capture, curation, search, sharing, storage, transfer,
visualization, and privacy violations. Big data calls for better
integration of optimization, statistics, and data mining. In
response to these challenges this book brings together leading
researchers and engineers to exchange and share their experiences
and research results about big data and information theory
applications in various areas. This book covers a broad range of
topics including statistics, data mining, data warehouse
implementation, engineering management in large-scale
infrastructure systems, data-driven sustainable supply chain
network, information technology service offshoring project issues,
online rumors governance, preliminary cost estimation, and
information system project selection. The chapters in this book
were originally published in the journal, International Journal of
Management Science and Engineering Management.
This book gathers the proceedings of the 14th International
Conference on Management Science and Engineering Management (ICMSEM
2020). Held at the Academy of Studies of Moldova from July 30 to
August 2, 2020, the conference provided a platform for researchers
and practitioners in the field to share their ideas and
experiences. Covering a wide range of topics, including hot
management issues in engineering science, the book presents novel
ideas and the latest research advances in the area of management
science and engineering management. It includes both theoretical
and practical studies of management science applied in computing
methodology, highlighting advanced management concepts, and
computing technologies for decision-making problems involving
large, uncertain and unstructured data. The book also describes the
changes and challenges relating to decision-making procedures at
the dawn of the big data era, and discusses new technologies for
analysis, capture, search, sharing, storage, transfer and
visualization, as well as advances in the integration of
optimization, statistics and data mining. Given its scope, it will
appeal to a wide readership, particularly those looking for new
ideas and research directions.
This book gathers the proceedings of the 14th International
Conference on Management Science and Engineering Management (ICMSEM
2020). Held at the Academy of Studies of Moldova from July 30 to
August 2, 2020, the conference provided a platform for researchers
and practitioners in the field to share their ideas and
experiences. Covering a wide range of topics, including hot
management issues in engineering science, the book presents novel
ideas and the latest research advances in the area of management
science and engineering management. It includes both theoretical
and practical studies of management science applied in computing
methodology, highlighting advanced management concepts, and
computing technologies for decision-making problems involving
large, uncertain and unstructured data. The book also describes the
changes and challenges relating to decision-making procedures at
the dawn of the big data era, and discusses new technologies for
analysis, capture, search, sharing, storage, transfer and
visualization, and in the context of privacy violations, as well as
advances in the integration of optimization, statistics and data
mining. Given its scope, it will appeal to a wide readership,
particularly those looking for new ideas and research directions.
This book gathers the proceedings of the 13th International
Conference on Management Science and Engineering Management (ICMSEM
2019), which was held at Brock University, Ontario, Canada on
August 5-8, 2019. Exploring the latest ideas and pioneering
research achievements in management science and engineering
management, the respective contributions highlight both theoretical
and practical studies on management science and computing
methodologies, and present advanced management concepts and
computing technologies for decision-making problems involving
large, uncertain and unstructured data. Accordingly, the
proceedings offer researchers and practitioners in related fields
an essential update, as well as a source of new research
directions.
This book gathers the proceedings of the 13th International
Conference on Management Science and Engineering Management (ICMSEM
2019), which was held at Brock University, Ontario, Canada on
August 5-8, 2019. Exploring the latest ideas and pioneering
research achievements in management science and engineering
management, the respective contributions highlight both theoretical
and practical studies on management science and computing
methodologies, and present advanced management concepts and
computing technologies for decision-making problems involving
large, uncertain and unstructured data. Accordingly, the
proceedings offer researchers and practitioners in related fields
an essential update, as well as a source of new research
directions.
This proceedings book is divided in 2 Volumes and 8 Parts. Part I
is dedicated to Decision Support System, which is about the
information system that supports business or organizational
decision-making activities; Part II is on Computing Methodology,
which is always used to provide the most effective algorithm for
numerical solutions of various modeling problems; Part III presents
Information Technology, which is the application of computers to
store, study, retrieve, transmit and manipulate data, or
information in the context of a business or other enterprise; Part
IV is dedicated to Data Analysis, which is a process of inspecting,
cleansing, transforming, and modeling data with the goal of
discovering useful information, suggesting conclusions, and
supporting decision-making; Part V presents papers on Operational
Management, which is about the plan, organization, implementation
and control of the operation process; Part VI is on Project
Management, which is about the initiating, planning, executing,
controlling, and closing the work of a team to achieve specific
goals and meet specific success criteria at the specified time in
the field of engineering; Part VII presents Green Supply Chain,
which is about the management of the flow of goods and services
based on the concept of "low-carbon"; Part VIII is focused on
Industry Strategy Management, which refers to the decision-making
and management art of an industry or organization in a long-term
and long-term development direction, objectives, tasks and
policies, as well as resource allocation.
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