Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore it is much easier to estimate & interpret than a dense model Statistical Learning with Sparsity The Lasso & Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data Top experts in this rapidly evolving field the authors describe the lasso for linear regression & a simple coordinate descent algorithm for its computation They discuss the application of 1 penalties to generalized linear models & support vector machines cover generalized penalties such as the elastic net & group lasso & review numerical methods for optimization They also present statistical inference methods for fitted (lasso) models including the bootstrap Bayesian methods & recently developed approaches In addition the book examines matrix decomposition sparse multivariate analysis graphical models & compressed sensing It concludes with a survey of theoretical results for the lasso In this age of big data the number of features measured on a person or object can be large & might be larger than the number of observations This book shows how the sparsity assumption allows us to tackle these problems & extract useful & reproducible patterns from big datasets Data analysts computer scientists & theorists will appreciate this thorough & up-to-date treatment of sparse statistical modeling ...

In each generation scientists must redefine their fields abstracting simplifying & distilling the previous standard topics to make room for new advances & methods Sethna's book takes this step for statistical mechanics
- a field rooted in physics & chemistry whose ideas & methods are now central to information theory complexity & modern biology Aimed at advanced undergraduates & early graduate students in all of these fields Sethna limits his main presentation to the topics that future mathematicians & biologists as well as physicists & chemists will find fascinating & central to their work The amazing breadth of the field is reflected in the author's large supply of carefully crafted exercises each an introduction to a whole field of study everything from chaos through information theory to life at the end of the universe
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How do beginning students of statistics for geography learn to fully understand the key concepts & apply the principal techniques? This text now in its Fourth Edition provides exactly that resource Accessibly written & focussed on student learning it's a statistics 101 that

Includes:: definitions examples & exercise throughout Now fully integrated with online self-assessment exercises & video navigation it explains everything required to get full credits for any undergraduate statistics module Descriptive statistics probability inferential statistics hypothesis testing & sampling variance correlation regression analysis spatial patterns spatial data reduction using factor analysis & cluster analysis Exercises in the text are complemented with online exercise & prompts that test the understanding of concepts & techniques additional online exercises review understanding of the entire chapter relating concepts & techniques Completely revised & updated for accessibility including new material (on measures of distance statistical power sample size selection & basic probability) with related exercises & downloadable datasets It is the only text required for undergraduate modules in statistical analysis statistical methods & quantitative geography

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Designing algorithms to recommend items such as news articles & movies to users is a challenging task in numerous web applications The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives Major technical challenges are high dimensional prediction with sparse data & constructing high dimensional sequential designs to collect data for user modeling & system design This comprehensive treatment of the statistical issues that arise in recommender systems

Includes:: detailed in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods) bilinear random-effects models (matrix factorization) & scalable model fitting using modern computing paradigms like Map Reduce The authors draw upon their vast experience working with such large-scale systems at Yahoo! & Linked In & bridge the gap between theory & practice by illustrating complex concepts with examples from applications they are directly involved with

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Statistical methods in modern research increasingly entail developing estimating & testing models for data Rather than rigid methods of data analysis the need today is for more flexible methods for modelling data In this logical easy-to-follow & exceptionally clear book David Flora provides a comprehensive survey of the major statistical procedures currently used His innovative model-based approach teaches you how to Understand & choose the right statistical model to fit your data Match substantive theory & statistical models Apply statistical procedures hands-on with example data analyses Develop & use graphs to understand data & fit models to data Work with statistical modeling principles using any software package Learn by applying with input & output files for R SAS SPSS & Mplus Statistical Methods for the Social & Behavioural Sciences A Model Based Approach is the essential guide for those looking to extend their understanding of the principles of statistics & begin using the right statistical modeling method for their own data It is particularly suited to second or advanced courses in statistical methods across the social & behavioural sciences ...

Statistical Rethinking A Bayesian Course with Examples in R & Stan builds readers' knowledge of & confidence in statistical modeling Reflecting the need for even minor programming in today's model-based statistics the book pushes readers to perform step-by-step calculations that are usually automated This unique computational approach ensures that readers understand enough of the details to make reasonable choices & interpretations in their own modeling work The text presents generalized linear multilevel models from a Bayesian perspective relying on a simple logical interpretation of Bayesian probability & maximum entropy It covers from the basics of regression to multilevel models The author also discusses measurement error missing data & Gaussian process models for spatial & network autocorrelation By using complete R code examples throughout this book provides a practical foundation for performing statistical inference Designed for both Ph D students & seasoned professionals in the natural & social sciences it prepares them for more advanced or specialized statistical modeling Web Resource The book is accompanied by an R package (rethinking) that is available on the author's website & Git Hub The two core functions (map & map 2stan) of this package allow a variety of statistical models to be constructed from standard model formulas ...

Fully endorsed by OCR & revised to match the 2005

Specification this series has been carefully revised by experienced

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Fully endorsed by OCR & revised to match the 2005

Specification this series has been carefully revised by experienced

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The new edition of this bestseller continues to demystify the world of statistics for health care professionals Revised throughout the 11th Edition features new material in the areas of binary data to deal with relative risk absolute risk & the evidence-based criteria of numbers needed to treat 192 pages Campbell Michael J ...

Statistics For Dummies 2nd Edition (9781119293521) was previously published as Statistics For Dummies 2nd Edition (9780470911082) While this version features a new Dummies cover & design the content is the same as the prior release & should not be considered a new or updated product The fun & easy way to get down to business with statistics Stymied by statistics? No fear? this friendly guide offers clear practical explanations of statistical ideas techniques formulas & calculations with lots of examples that show you how these concepts apply to your everyday life Statistics For Dummies shows you how to interpret & critique graphs & charts determine the odds with probability guesstimate with complete confidence using confidence intervals set up & carry out a hypothesis test compute statistical formulas & more Tracks to a typical first semester statistics course Updated examples resonate with today's students Explanations mirror teaching methods & classroom protocol Packed with practical advice & real-world problems Statistics For Dummies gives you everything you need to analyze & interpret data for enhanced classroom or on-the-job performance ...