Post Shrinkage Strategies In Statistical And Machine Learning For High Dimensional Data

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Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data

Author : Syed Ejaz Ahmed
Publisher : CRC Press
ISBN 13 : 1000876659
Total Pages : 409 pages
Book Rating : 4.0/5 (8 download)

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Book Synopsis Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data by : Syed Ejaz Ahmed

Download or read book Post-Shrinkage Strategies in Statistical and Machine Learning for High Dimensional Data written by Syed Ejaz Ahmed and published by CRC Press. This book was released on 2023-05-25 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

The Eighteenth International Conference on Management Science and Engineering Management

Author : Jiuping Xu
Publisher : Springer Nature
ISBN 13 : 9819750989
Total Pages : 1703 pages
Book Rating : 4.8/5 (197 download)

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Book Synopsis The Eighteenth International Conference on Management Science and Engineering Management by : Jiuping Xu

Download or read book The Eighteenth International Conference on Management Science and Engineering Management written by Jiuping Xu and published by Springer Nature. This book was released on with total page 1703 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Rank-Based Methods for Shrinkage and Selection

Author : A. K. Md. Ehsanes Saleh
Publisher : John Wiley & Sons
ISBN 13 : 1119625424
Total Pages : 484 pages
Book Rating : 4.1/5 (196 download)

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Book Synopsis Rank-Based Methods for Shrinkage and Selection by : A. K. Md. Ehsanes Saleh

Download or read book Rank-Based Methods for Shrinkage and Selection written by A. K. Md. Ehsanes Saleh and published by John Wiley & Sons. This book was released on 2022-04-12 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rank-Based Methods for Shrinkage and Selection A practical and hands-on guide to the theory and methodology of statistical estimation based on rank Robust statistics is an important field in contemporary mathematics and applied statistical methods. Rank-Based Methods for Shrinkage and Selection: With Application to Machine Learning describes techniques to produce higher quality data analysis in shrinkage and subset selection to obtain parsimonious models with outlier-free prediction. This book is intended for statisticians, economists, biostatisticians, data scientists and graduate students. Rank-Based Methods for Shrinkage and Selection elaborates on rank-based theory and application in machine learning to robustify the least squares methodology. It also includes: Development of rank theory and application of shrinkage and selection Methodology for robust data science using penalized rank estimators Theory and methods of penalized rank dispersion for ridge, LASSO and Enet Topics include Liu regression, high-dimension, and AR(p) Novel rank-based logistic regression and neural networks Problem sets include R code to demonstrate its use in machine learning

A Nature-Inspired Approach to Cryptology

Author : Shishir Kumar Shandilya
Publisher : Springer Nature
ISBN 13 : 9819970814
Total Pages : 325 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis A Nature-Inspired Approach to Cryptology by : Shishir Kumar Shandilya

Download or read book A Nature-Inspired Approach to Cryptology written by Shishir Kumar Shandilya and published by Springer Nature. This book was released on 2024-01-15 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces nature-inspired algorithms and their applications to modern cryptography. It helps the readers to get into the field of nature-based approaches to solve complex cryptographic issues. This book provides a comprehensive view of nature-inspired research which could be applied in cryptography to strengthen security. It will also explore the novel research directives such as Clever algorithms and immune-based cyber resilience. New experimented nature-inspired approaches are having enough potential to make a huge impact in the field of cryptanalysis. This book gives a lucid introduction to this exciting new field and will promote further research in this domain. The book discusses the current landscape of cryptography and nature-inspired research and will be helpful to prospective students and professionals to explore further.

Dimensionality Reduction with Unsupervised Nearest Neighbors

Author : Oliver Kramer
Publisher : Springer Science & Business Media
ISBN 13 : 3642386520
Total Pages : 137 pages
Book Rating : 4.6/5 (423 download)

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Book Synopsis Dimensionality Reduction with Unsupervised Nearest Neighbors by : Oliver Kramer

Download or read book Dimensionality Reduction with Unsupervised Nearest Neighbors written by Oliver Kramer and published by Springer Science & Business Media. This book was released on 2013-05-30 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is devoted to a novel approach for dimensionality reduction based on the famous nearest neighbor method that is a powerful classification and regression approach. It starts with an introduction to machine learning concepts and a real-world application from the energy domain. Then, unsupervised nearest neighbors (UNN) is introduced as efficient iterative method for dimensionality reduction. Various UNN models are developed step by step, reaching from a simple iterative strategy for discrete latent spaces to a stochastic kernel-based algorithm for learning submanifolds with independent parameterizations. Extensions that allow the embedding of incomplete and noisy patterns are introduced. Various optimization approaches are compared, from evolutionary to swarm-based heuristics. Experimental comparisons to related methodologies taking into account artificial test data sets and also real-world data demonstrate the behavior of UNN in practical scenarios. The book contains numerous color figures to illustrate the introduced concepts and to highlight the experimental results.

Multi-Label Dimensionality Reduction

Author : Liang Sun
Publisher : CRC Press
ISBN 13 : 1439806160
Total Pages : 206 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Multi-Label Dimensionality Reduction by : Liang Sun

Download or read book Multi-Label Dimensionality Reduction written by Liang Sun and published by CRC Press. This book was released on 2016-04-19 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Similar to other data mining and machine learning tasks, multi-label learning suffers from dimensionality. An effective way to mitigate this problem is through dimensionality reduction, which extracts a small number of features by removing irrelevant, redundant, and noisy information. The data mining and machine learning literature currently lacks

Statistical and Machine-Learning Data Mining:

Author : Bruce Ratner
Publisher : CRC Press
ISBN 13 : 149879761X
Total Pages : 690 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Statistical and Machine-Learning Data Mining: by : Bruce Ratner

Download or read book Statistical and Machine-Learning Data Mining: written by Bruce Ratner and published by CRC Press. This book was released on 2017-07-12 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interest in predictive analytics of big data has grown exponentially in the four years since the publication of Statistical and Machine-Learning Data Mining: Techniques for Better Predictive Modeling and Analysis of Big Data, Second Edition. In the third edition of this bestseller, the author has completely revised, reorganized, and repositioned the original chapters and produced 13 new chapters of creative and useful machine-learning data mining techniques. In sum, the 43 chapters of simple yet insightful quantitative techniques make this book unique in the field of data mining literature. What is new in the Third Edition: The current chapters have been completely rewritten. The core content has been extended with strategies and methods for problems drawn from the top predictive analytics conference and statistical modeling workshops. Adds thirteen new chapters including coverage of data science and its rise, market share estimation, share of wallet modeling without survey data, latent market segmentation, statistical regression modeling that deals with incomplete data, decile analysis assessment in terms of the predictive power of the data, and a user-friendly version of text mining, not requiring an advanced background in natural language processing (NLP). Includes SAS subroutines which can be easily converted to other languages. As in the previous edition, this book offers detailed background, discussion, and illustration of specific methods for solving the most commonly experienced problems in predictive modeling and analysis of big data. The author addresses each methodology and assigns its application to a specific type of problem. To better ground readers, the book provides an in-depth discussion of the basic methodologies of predictive modeling and analysis. While this type of overview has been attempted before, this approach offers a truly nitty-gritty, step-by-step method that both tyros and experts in the field can enjoy playing with.

Sparse Boosting Based Machine Learning Methods for High-Dimensional Data

Author : Mu Yue
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)

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Book Synopsis Sparse Boosting Based Machine Learning Methods for High-Dimensional Data by : Mu Yue

Download or read book Sparse Boosting Based Machine Learning Methods for High-Dimensional Data written by Mu Yue and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In high-dimensional data, penalized regression is often used for variable selection and parameter estimation. However, these methods typically require time-consuming cross-validation methods to select tuning parameters and retain more false positives under high dimensionality. This chapter discusses sparse boosting based machine learning methods in the following high-dimensional problems. First, a sparse boosting method to select important biomarkers is studied for the right censored survival data with high-dimensional biomarkers. Then, a two-step sparse boosting method to carry out the variable selection and the model-based prediction is studied for the high-dimensional longitudinal observations measured repeatedly over time. Finally, a multi-step sparse boosting method to identify patient subgroups that exhibit different treatment effects is studied for the high-dimensional dense longitudinal observations. This chapter intends to solve the problem of how to improve the accuracy and calculation speed of variable selection and parameter estimation in high-dimensional data. It aims to expand the application scope of sparse boosting and develop new methods of high-dimensional survival analysis, longitudinal data analysis, and subgroup analysis, which has great application prospects.

Netboost: Statistical Modeling Strategies for High-dimensional Data

Author : Pascal Schlosser
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (118 download)

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Book Synopsis Netboost: Statistical Modeling Strategies for High-dimensional Data by : Pascal Schlosser

Download or read book Netboost: Statistical Modeling Strategies for High-dimensional Data written by Pascal Schlosser and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Statistical Methods at the Forefront of Biomedical Advances

Author : Yolanda Larriba
Publisher : Springer Nature
ISBN 13 : 3031327292
Total Pages : 280 pages
Book Rating : 4.0/5 (313 download)

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Book Synopsis Statistical Methods at the Forefront of Biomedical Advances by : Yolanda Larriba

Download or read book Statistical Methods at the Forefront of Biomedical Advances written by Yolanda Larriba and published by Springer Nature. This book was released on 2023-08-09 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents novel statistics methods and reproducible software that helps to solve challenging problems in biomedicine. Specifically, it consists of a collection of 11 chapters contributed by some of the leading experts in the mathematical and statistical field which address new challenges in very disparate biomedical areas, such as genomics, cancer, circadian biology, microbiome, mental disorders, and more. The mathematical rigor is written in a user-friendly way to serve a general biomedical audience ranging from trainees or students to doctors, as well as scientific researchers, university departments, and PhD students.

Statistical Foundations of Data Science

Author : Jianqing Fan
Publisher : CRC Press
ISBN 13 : 0429527616
Total Pages : 942 pages
Book Rating : 4.4/5 (295 download)

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Book Synopsis Statistical Foundations of Data Science by : Jianqing Fan

Download or read book Statistical Foundations of Data Science written by Jianqing Fan and published by CRC Press. This book was released on 2020-09-21 with total page 942 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.

Empirical Asset Pricing

Author : Wayne Ferson
Publisher : MIT Press
ISBN 13 : 0262039370
Total Pages : 497 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Empirical Asset Pricing by : Wayne Ferson

Download or read book Empirical Asset Pricing written by Wayne Ferson and published by MIT Press. This book was released on 2019-03-12 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to the theory and methods of empirical asset pricing, integrating classical foundations with recent developments. This book offers a comprehensive advanced introduction to asset pricing, the study of models for the prices and returns of various securities. The focus is empirical, emphasizing how the models relate to the data. The book offers a uniquely integrated treatment, combining classical foundations with more recent developments in the literature and relating some of the material to applications in investment management. It covers the theory of empirical asset pricing, the main empirical methods, and a range of applied topics. The book introduces the theory of empirical asset pricing through three main paradigms: mean variance analysis, stochastic discount factors, and beta pricing models. It describes empirical methods, beginning with the generalized method of moments (GMM) and viewing other methods as special cases of GMM; offers a comprehensive review of fund performance evaluation; and presents selected applied topics, including a substantial chapter on predictability in asset markets that covers predicting the level of returns, volatility and higher moments, and predicting cross-sectional differences in returns. Other chapters cover production-based asset pricing, long-run risk models, the Campbell-Shiller approximation, the debate on covariance versus characteristics, and the relation of volatility to the cross-section of stock returns. An extensive reference section captures the current state of the field. The book is intended for use by graduate students in finance and economics; it can also serve as a reference for professionals.

Data Dimensionality Reduction Techniques: what Works with Machine Learning Models

Author : Yuting Chen
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (139 download)

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Book Synopsis Data Dimensionality Reduction Techniques: what Works with Machine Learning Models by : Yuting Chen

Download or read book Data Dimensionality Reduction Techniques: what Works with Machine Learning Models written by Yuting Chen and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-dimensional data has a wide range of applications in research, such as education, health, social media, and many other research fields. However, the high dimensionality of data can raise many problems for data analyses. This study focuses on commonly used techniques of dimensionality reduction for machine learning models, which play an essential and inevitable role in data prepossessing and statistical analysis. The main issues of high-dimensional data for machine learning tasks include the accuracy of data classification and visualization in machine learning models. Therefore, in this study, machine learning algorithms are used to predict and classify datasets to evaluate the accuracy, precision, recall, and F1 score of results, which are evaluated and compared by mean, variance, confidence intervals, and coverage. This study focuses on data mining issues, comparing and discussing different dimensionality reduction techniques with different dataset features. Eight dimensionality reduction techniques (Principal Component Analysis, Kernel Principal Component Analysis, Singular Value Decomposition, Non-negative matrix factorization, Independent Component Analysis, Multidimensional Scaling, Isomap, and Auto-encoder) are compared and evaluated on simulated datasets. Specifically, this study evaluates and compares the performances of the commonly used dimensionality reduction techniques by exploring the issues about features and characteristics of different techniques through Monte Carlo simulation studies with four machine learning classification models: logistic regression, linear support vector machine, nonlinear support vector machine, and k-nearest neighbors. The results of this study indicated that the DRTs decreased the accuracy, precision, recall, and F1 scores compared with results without DRTs. And overall, MDS performed dramatically better than other DRTs. SVD, PCA, and ICA had similar results because they are all linear DRTs. Although it is also a linear DRT, NMF performed as poorly as KPCA, which is a nonlinear DRT. The other two nonlinear DRTs, Isomap and Autoencoder, had the worst performance in this study. The results provided recommendations for empirical researchers using machine learning models with high dimensional data under specific conditions.

Data Science and Machine Learning

Author : Dirk P. Kroese
Publisher : CRC Press
ISBN 13 : 1000730778
Total Pages : 538 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Data Science and Machine Learning by : Dirk P. Kroese

Download or read book Data Science and Machine Learning written by Dirk P. Kroese and published by CRC Press. This book was released on 2019-11-20 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

Practical Statistics for Data Scientists

Author : Peter Bruce
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491952911
Total Pages : 395 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Practical Statistics for Data Scientists by : Peter Bruce

Download or read book Practical Statistics for Data Scientists written by Peter Bruce and published by "O'Reilly Media, Inc.". This book was released on 2017-05-10 with total page 395 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data

Interpretable Machine Learning

Author : Christoph Molnar
Publisher : Lulu.com
ISBN 13 : 0244768528
Total Pages : 320 pages
Book Rating : 4.2/5 (447 download)

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Book Synopsis Interpretable Machine Learning by : Christoph Molnar

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications

Author : Arun Kumar Sangaiah
Publisher : Academic Press
ISBN 13 : 0128133279
Total Pages : 362 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications by : Arun Kumar Sangaiah

Download or read book Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications written by Arun Kumar Sangaiah and published by Academic Press. This book was released on 2018-08-21 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Intelligence for Multimedia Big Data on the Cloud with Engineering Applications covers timely topics, including the neural network (NN), particle swarm optimization (PSO), evolutionary algorithm (GA), fuzzy sets (FS) and rough sets (RS), etc. Furthermore, the book highlights recent research on representative techniques to elaborate how a data-centric system formed a powerful platform for the processing of cloud hosted multimedia big data and how it could be analyzed, processed and characterized by CI. The book also provides a view on how techniques in CI can offer solutions in modeling, relationship pattern recognition, clustering and other problems in bioengineering. It is written for domain experts and developers who want to understand and explore the application of computational intelligence aspects (opportunities and challenges) for design and development of a data-centric system in the context of multimedia cloud, big data era and its related applications, such as smarter healthcare, homeland security, traffic control trading analysis and telecom, etc. Researchers and PhD students exploring the significance of data centric systems in the next paradigm of computing will find this book extremely useful. Presents a brief overview of computational intelligence paradigms and its significant role in application domains Illustrates the state-of-the-art and recent developments in the new theories and applications of CI approaches Familiarizes the reader with computational intelligence concepts and technologies that are successfully used in the implementation of cloud-centric multimedia services in massive data processing Provides new advances in the fields of CI for bio-engineering application