Data Analysis By Resampling
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Resampling Methods
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Author : Phillip I. Good
language : en
Publisher:
Release Date : 2001
Resampling Methods written by Phillip I. Good and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Resampling (Statistics) categories.
Data Analysis By Resampling
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Author : Clifford E. Lunneborg
language : en
Publisher: Brooks/Cole
Release Date : 2000
Data Analysis By Resampling written by Clifford E. Lunneborg and has been published by Brooks/Cole this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Computers categories.
In DATA ANALYSIS BY RESAMPLING, Clifford Lunneborg argues that modern computing power has rendered the model-driven and assumption-plagued data analyses of the past unnecessary, obsolete, and inappropriate. This book introduces readers to modern, design-driven analyses that depend only on the observed data, on knowledge of how the data were collected, and on questions the data were intended to answer. Overall, Lunneborg provides a modern and timely approach to statistical inference.
Resampling Methods
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Author : Phillip I. Good
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-14
Resampling Methods written by Phillip I. Good and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-14 with Computers categories.
"Most introductory statistics books ignore or give little attention to resampling methods, and thus another generation learns the less than optimal methods of statistical analysis. Good attempts to remedy this situation by writing an introductory text that focuses on resampling methods, and he does it well." — Ron C. Fryxell, Albion College "...The wealth of the bibliography covers a wide range of disciplines." ---Dr. Dimitris Karlis, Athens University of Economics This thoroughly revised second edition is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests. It is an essential resource for industrial statisticians, statistical consultants, and research professionals in science, engineering, and technology. Only requiring minimal mathematics beyond algebra, it provides a table-free introduction to data analysis utilizing numerous exercises, practical data sets, and freely available statistical shareware. Topics and Features: * Offers more practical examples plus an additional chapter dedicated to regression and data mining techniques and their limitations * Uses resampling approach to introduction statistics * A practical presentation that covers all three sampling methods: bootstrap, density-estimation, and permutations * Includes systematic guide to help one select the correct procedure for a particular application * Detailed coverage of all three statistical methodologies: classification, estimation, and hypothesis testing * Suitable for classroom use and individual, self-study purposes * Numerous practical examples using popular computer programs such as SAS®, Stata®, and StatXact® * Useful appendixes with computer programs and code to develop individualized methods * Downloadable freeware from author’s website:http://users.oco.net/drphilgood/resamp.htm With its accessible style and intuitive topic development, the book is an excellent basic resource for the power, simplicity, and versatility of the bootstrap, cross-validation, and permutation tests. Students, professionals, and researchers will find it a prarticularly useful handbook for modern resampling methods and their applications.
A Practitioner S Guide To Resampling For Data Analysis Data Mining And Modeling
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Author : Phillip Good
language : en
Publisher: Chapman and Hall/CRC
Release Date : 2011-08-25
A Practitioner S Guide To Resampling For Data Analysis Data Mining And Modeling written by Phillip Good and has been published by Chapman and Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-08-25 with Business & Economics categories.
Distribution-free resampling methods—permutation tests, decision trees, and the bootstrap—are used today in virtually every research area. A Practitioner’s Guide to Resampling for Data Analysis, Data Mining, and Modeling explains how to use the bootstrap to estimate the precision of sample-based estimates and to determine sample size, data permutations to test hypotheses, and the readily-interpreted decision tree to replace arcane regression methods. Highlights Each chapter contains dozens of thought provoking questions, along with applicable R and Stata code Methods are illustrated with examples from agriculture, audits, bird migration, clinical trials, epidemiology, image processing, immunology, medicine, microarrays and gene selection Lists of commercially available software for the bootstrap, decision trees, and permutation tests are incorporated in the text Access to APL, MATLAB, and SC code for many of the routines is provided on the author’s website The text covers estimation, two-sample and k-sample univariate, and multivariate comparisons of means and variances, sample size determination, categorical data, multiple hypotheses, and model building Statistics practitioners will find the methods described in the text easy to learn and to apply in a broad range of subject areas from A for Accounting, Agriculture, Anthropology, Aquatic science, Archaeology, Astronomy, and Atmospheric science to V for Virology and Vocational Guidance, and Z for Zoology. Practitioners and research workers and in the biomedical, engineering and social sciences, as well as advanced students in biology, business, dentistry, medicine, psychology, public health, sociology, and statistics will find an easily-grasped guide to estimation, testing hypotheses and model building.
Introductory Statistics And Analytics
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Author : Peter C. Bruce
language : en
Publisher: John Wiley & Sons
Release Date : 2014-12-03
Introductory Statistics And Analytics written by Peter C. Bruce and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-03 with Mathematics categories.
Concise, thoroughly class-tested primer that features basicstatistical concepts in the concepts in the context of analytics,resampling, and the bootstrap A uniquely developed presentation of key statistical topics,Introductory Statistics and Analytics: A ResamplingPerspective provides an accessible approach to statisticalanalytics, resampling, and the bootstrap for readers with variouslevels of exposure to basic probability and statistics. Originallyclass-tested at one of the first online learning companies in thediscipline, www.statistics.com, the book primarily focuses onapplications of statistical concepts developed via resampling, witha background discussion of mathematical theory. This featurestresses statistical literacy and understanding, which demonstratesthe fundamental basis for statistical inference and demystifiestraditional formulas. The book begins with illustrations that have the essentialstatistical topics interwoven throughout before moving on todemonstrate the proper design of studies. Meeting all of theGuidelines for Assessment and Instruction in Statistics Education(GAISE) requirements for an introductory statistics course,Introductory Statistics and Analytics: A ResamplingPerspective also includes: Over 300 “Try It Yourself” exercises andintermittent practice questions, which challenge readers atmultiple levels to investigate and explore key statisticalconcepts Numerous interactive links designed to provide solutions toexercises and further information on crucial concepts Linkages that connect statistics to the rapidly growing fieldof data science Multiple discussions of various software systems, such asMicrosoft Office Excel®, StatCrunch, and R, to develop andanalyze data Areas of concern and/or contrasting points-of-view indicatedthrough the use of “Caution” icons Introductory Statistics and Analytics: A ResamplingPerspective is an excellent primary textbook for courses inpreliminary statistics as well as a supplement for courses inupper-level statistics and related fields, such as biostatisticsand econometrics. The book is also a general reference for readersinterested in revisiting the value of statistics.
Python For Algorithmic Trading Cookbook
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Author : Jason Strimpel
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-08-16
Python For Algorithmic Trading Cookbook written by Jason Strimpel and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-16 with Business & Economics categories.
Harness the power of Python libraries to transform freely available financial market data into algorithmic trading strategies and deploy them into a live trading environment Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Follow practical Python recipes to acquire, visualize, and store market data for market research Design, backtest, and evaluate the performance of trading strategies using professional techniques Deploy trading strategies built in Python to a live trading environment with API connectivity Book DescriptionDiscover how Python has made algorithmic trading accessible to non-professionals with unparalleled expertise and practical insights from Jason Strimpel, founder of PyQuant News and a seasoned professional with global experience in trading and risk management. This book guides you through from the basics of quantitative finance and data acquisition to advanced stages of backtesting and live trading. Detailed recipes will help you leverage the cutting-edge OpenBB SDK to gather freely available data for stocks, options, and futures, and build your own research environment using lightning-fast storage techniques like SQLite, HDF5, and ArcticDB. This book shows you how to use SciPy and statsmodels to identify alpha factors and hedge risk, and construct momentum and mean-reversion factors. You’ll optimize strategy parameters with walk-forward optimization using VectorBT and construct a production-ready backtest using Zipline Reloaded. Implementing all that you’ve learned, you’ll set up and deploy your algorithmic trading strategies in a live trading environment using the Interactive Brokers API, allowing you to stream tick-level data, submit orders, and retrieve portfolio details. By the end of this algorithmic trading book, you'll not only have grasped the essential concepts but also the practical skills needed to implement and execute sophisticated trading strategies using Python.What you will learn Acquire and process freely available market data with the OpenBB Platform Build a research environment and populate it with financial market data Use machine learning to identify alpha factors and engineer them into signals Use VectorBT to find strategy parameters using walk-forward optimization Build production-ready backtests with Zipline Reloaded and evaluate factor performance Set up the code framework to connect and send an order to Interactive Brokers Who this book is for Python for Algorithmic Trading Cookbook equips traders, investors, and Python developers with code to design, backtest, and deploy algorithmic trading strategies. You should have experience investing in the stock market, knowledge of Python data structures, and a basic understanding of using Python libraries like pandas. This book is also ideal for individuals with Python experience who are already active in the market or are aspiring to be.
Data Analysis A Practical Guide To Resampling Methods
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Author : Phillip I. Good
language : en
Publisher:
Release Date : 2003
Data Analysis A Practical Guide To Resampling Methods written by Phillip I. Good and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Resampling (Statistics) categories.
This thoroughly revised second edition is a practical guide to data analysis using the bootstrap, cross-validation, and permutation tests. It is an essential resource for industrial statisticians, statistical consultants, and research professionals in sci
The Jackknife And Bootstrap
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Author : Jun Shao
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
The Jackknife And Bootstrap written by Jun Shao and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
The jackknife and bootstrap are the most popular data-resampling meth ods used in statistical analysis. The resampling methods replace theoreti cal derivations required in applying traditional methods (such as substitu tion and linearization) in statistical analysis by repeatedly resampling the original data and making inferences from the resamples. Because of the availability of inexpensive and fast computing, these computer-intensive methods have caught on very rapidly in recent years and are particularly appreciated by applied statisticians. The primary aims of this book are (1) to provide a systematic introduction to the theory of the jackknife, the bootstrap, and other resampling methods developed in the last twenty years; (2) to provide a guide for applied statisticians: practitioners often use (or misuse) the resampling methods in situations where no theoretical confirmation has been made; and (3) to stimulate the use of the jackknife and bootstrap and further devel opments of the resampling methods. The theoretical properties of the jackknife and bootstrap methods are studied in this book in an asymptotic framework. Theorems are illustrated by examples. Finite sample properties of the jackknife and bootstrap are mostly investigated by examples and/or empirical simulation studies. In addition to the theory for the jackknife and bootstrap methods in problems with independent and identically distributed (Li.d.) data, we try to cover, as much as we can, the applications of the jackknife and bootstrap in various complicated non-Li.d. data problems.
Artificial Intelligence And Advanced Analytics For Food Security
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Author : Chandrasekar Vuppalapati
language : en
Publisher: CRC Press
Release Date : 2023-07-17
Artificial Intelligence And Advanced Analytics For Food Security written by Chandrasekar Vuppalapati and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-17 with Technology & Engineering categories.
Climate change, increasing population, food-versus-fuel economics, pandemics, etc. pose a threat to food security to unprecedented levels. It has fallen upon the practitioners of agriculture and technologists of the world to innovate and become more productive to address the multi-pronged food security challenges. Agricultural innovation is key to managing food security concerns. The infusion of data science, artificial intelligence (AI), advanced analytics, satellites data, geospatial data, climatology, sensor technologies, and climate modeling with traditional agricultural practices such as soil engineering, fertilizers use, and agronomy are some of the best ways to achieve this. Data science helps farmers to unravel patterns in fertilizer pricing, equipment usage, transportation and storage costs, yield per hectare, and weather trends to better plan and spend resources. AI enables farmers to learn from fellow farmers to apply best techniques that are transferred learning from AI to improve agricultural productivity and to achieve financial sustainability. Sensor technologies play an important role in getting real-time farm field data and provide feedback loops to improve overall agricultural practices and can yield huge productivity gains. Advanced Analytics modeling is essential software technique that codifies farmers’ tacit knowledge such as better seed per soil, better feed for dairy cattle breed, or production practices to match weather pattern that was acquired over years of their hard work to share with worldwide farmers to improve overall production efficiencies, the best antidote to food security issue. In addition to the paradigm shift, economic sustainability of small farms is a major enabler of food security. The book reviews all these technological advances and proposes macroeconomic pricing models that data mines macroeconomic signals and the influence of global economic trends on small farm sustainability to provide actionable insights to farmers to avert any financial disasters due to recurrent economic crises.
A Practitioner S Guide To Resampling For Data Analysis Data Mining And Modeling
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Author : Phillip I. Good
language : en
Publisher: Chapman and Hall/CRC
Release Date : 2012
A Practitioner S Guide To Resampling For Data Analysis Data Mining And Modeling written by Phillip I. Good and has been published by Chapman and Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with BUSINESS & ECONOMICS categories.
Distribution-free resampling methods--permutation tests, decision trees, and the bootstrap--are used today in virtually every research area. A Practitioner's Guide to Resampling for Data Analysis, Data Mining, and Modeling explains how to use the bootstrap to estimate the precision of sample-based estimates and to determine sample size, data permutations to test hypotheses, and the readily-interpreted decision tree to replace arcane regression methods.Highlights Each chapter contains dozens of thought provoking questions, along with applicable R and Stata codeMethods are illustrated with examples from agriculture, audits, bird migration, clinical trials, epidemiology, image processing, immunology, medicine, microarrays and gene selectionLists of commercially available software for the bootstrap, decision trees, and permutation tests are incorporated in the textAccess to APL, MATLAB, and SC code for many of the routines is provided on the author's websiteThe text covers estimation, two-sample and k-sample univariate, and multivariate comparisons of means and variances, sample size determination, categorical data, multiple hypotheses, and model buildingStatistics practitioners will find the methods described in the text easy to learn and to apply in a broad range of subject areas from A for Accounting, Agriculture, Anthropology, Aquatic science, Archaeology, Astronomy, and Atmospheric science to V for Virology and Vocational Guidance, and Z for Zoology.Practitioners and research workers and in the biomedical, engineering and social sciences, as well as advanced students in biology, business, dentistry, medicine, psychology, public health, sociology, and statistics will find an easily-grasped guide to estimation, testing hypotheses and model building.