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Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Automatic control engineering
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Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data (Hardcover)
Loot Price: R3,889
Discovery Miles 38 890
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Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data (Hardcover)
Expected to ship within 12 - 19 working days
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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.
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