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The Predictors
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Download or read book The Predictors written by Thomas A. Bass and published by Macmillan. This book was released on 2000-11 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bass relates how two rumpled physicists set up computers in an adobe house in Santa Fe for a start-up company, and follows their journey into the centers of financial power where "the predictors" find investors and finally go live with real money.
Book Synopsis The Predictors of Subjective Well-Being by : Fevziye DOLUNAY CUĞ
Download or read book The Predictors of Subjective Well-Being written by Fevziye DOLUNAY CUĞ and published by Akademisyen Kitabevi. This book was released on 2020-01-27 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Regression with Linear Predictors by : Per Kragh Andersen
Download or read book Regression with Linear Predictors written by Per Kragh Andersen and published by Springer. This book was released on 2010-07-15 with total page 502 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a book about regression analysis, that is, the situation in statistics where the distribution of a response (or outcome) variable is related to - planatory variables (or covariates). This is an extremely common situation in the application of statistical methods in many ?elds, andlinear regression,- gistic regression, and Cox proportional hazards regression are frequently used for quantitative, binary, and survival time outcome variables, respectively. Several books on these topics have appeared and for that reason one may well ask why we embark on writing still another book on regression. We have two main reasons for doing this: 1. First, we want to highlightsimilaritiesamonglinear,logistic,proportional hazards,andotherregressionmodelsthatincludealinearpredictor. These modelsareoftentreatedentirelyseparatelyintextsinspiteofthefactthat alloperationsonthemodelsdealingwiththelinearpredictorareprecisely the same, including handling of categorical and quantitative covariates, testing for linearity and studying interactions. 2. Second, we want to emphasize that, for any type of outcome variable, multiple regression models are composed of simple building blocks that areaddedtogetherinthelinearpredictor:thatis,t-tests,one-wayanalyses of variance and simple linear regressions for quantitative outcomes, 2×2, 2×(k+1) tables and simple logistic regressions for binary outcomes, and 2-and (k+1)-sample logrank testsand simple Cox regressionsfor survival data. Thishastwoconsequences. Allthesesimpleandwellknownmethods can be considered as special cases of the regression models. On the other hand, the e?ect of a single explanatory variable in a multiple regression model can be interpreted in a way similar to that obtained in the simple analysis, however, now valid only for the other explanatory variables in the model “held ?xed”.
Book Synopsis Feature Engineering and Selection by : Max Kuhn
Download or read book Feature Engineering and Selection written by Max Kuhn and published by CRC Press. This book was released on 2019-07-25 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.
Book Synopsis Forecasting: principles and practice by : Rob J Hyndman
Download or read book Forecasting: principles and practice written by Rob J Hyndman and published by OTexts. This book was released on 2018-05-08 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Book Synopsis Clinical Prediction Models by : Ewout W. Steyerberg
Download or read book Clinical Prediction Models written by Ewout W. Steyerberg and published by Springer. This book was released on 2019-07-22 with total page 558 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of this volume provides insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability. The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. Updates to this new and expanded edition include: • A discussion of Big Data and its implications for the design of prediction models • Machine learning issues • More simulations with missing ‘y’ values • Extended discussion on between-cohort heterogeneity • Description of ShinyApp • Updated LASSO illustration • New case studies
Download or read book A Doomsday Reader written by Ted Daniels and published by NYU Press. This book was released on 1999-08-01 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: A collection of pronouncements, edicts, and scriptures predicting the apocalypse The approach of the year 2000 has made the study of apocalyptic movements trendy. But groups anticipating the end of the world will continue to predict Armageddon even after the calendar clicks to triple 0s. A Doomsday Reader brings together pronouncements, edicts, and scriptures written by prominent apocalyptic movements from a wide range of traditions and ideologies to offer an exceptional look into their belief systems. Focused on attaining paradise, millenarianism often anticipates great, cosmic change. While most think of religious belief as motivating such fervor, Daniels' comparative approach encompasses secular movements such as environmentalism and the Montana Freemen, and argues that such groups are often more political than religious in nature. The book includes documents from groups such as the Branch Davidians, the Order of the Solar Temple, Heaven's Gate, and white supremacists. Each document is preceded by a substantive introduction placing the movement and its beliefs in context. This important overview of contemporary politics of the End will remain a valuable resource long after the year 2000 has come and gone.
Book Synopsis Memory as Prediction by : Tomaso Vecchi
Download or read book Memory as Prediction written by Tomaso Vecchi and published by MIT Press. This book was released on 2020-11-24 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical reflections on memory and prediction, linking these concepts to the role of the cerebellum in higher cognition. What is memory? What is memory for? Where is memory in the brain? Although memory is probably the most studied function in cognition, these fundamental questions remain challenging. We can try to answer the question of memory's purpose by defining the function of memory as remembering the past. And yet this definition is not consistent with the many errors that characterize our memory, or with the phylogenetic and ontogenetic origin of memory. In this book, Tomaso Vecchi and Daniele Gatti argue that the purpose of memory is not to remember the past but to predict the future. Vecchi and Gatti link memory and prediction to the role of the cerebellum in higher cognition, relying on recent empirical data to support theoretical reflections. They propose a new model of memory functions that comprises a system devoted to prediction, based in the cerebellum and mediated by the hippocampus, and a parallel system with a major role for cortical structures and mediated by the amygdala. Although memory is often conceived as a kind of storehouse, this storehouse is constantly changing, integrating new information in a continual process of modification. In order to explain these characteristics, Vecchi and Gatti argue, we must change our interpretation of the nature and functions of the memory system.
Book Synopsis Universal Time-Series Forecasting with Mixture Predictors by : Daniil Ryabko
Download or read book Universal Time-Series Forecasting with Mixture Predictors written by Daniil Ryabko and published by Springer Nature. This book was released on 2020-09-26 with total page 85 pages. Available in PDF, EPUB and Kindle. Book excerpt: The author considers the problem of sequential probability forecasting in the most general setting, where the observed data may exhibit an arbitrary form of stochastic dependence. All the results presented are theoretical, but they concern the foundations of some problems in such applied areas as machine learning, information theory and data compression.
Book Synopsis Superforecasting by : Philip E. Tetlock
Download or read book Superforecasting written by Philip E. Tetlock and published by Crown. This book was released on 2015-09-29 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: NEW YORK TIMES BESTSELLER • NAMED ONE OF THE BEST BOOKS OF THE YEAR BY THE ECONOMIST “The most important book on decision making since Daniel Kahneman's Thinking, Fast and Slow.”—Jason Zweig, The Wall Street Journal Everyone would benefit from seeing further into the future, whether buying stocks, crafting policy, launching a new product, or simply planning the week’s meals. Unfortunately, people tend to be terrible forecasters. As Wharton professor Philip Tetlock showed in a landmark 2005 study, even experts’ predictions are only slightly better than chance. However, an important and underreported conclusion of that study was that some experts do have real foresight, and Tetlock has spent the past decade trying to figure out why. What makes some people so good? And can this talent be taught? In Superforecasting, Tetlock and coauthor Dan Gardner offer a masterwork on prediction, drawing on decades of research and the results of a massive, government-funded forecasting tournament. The Good Judgment Project involves tens of thousands of ordinary people—including a Brooklyn filmmaker, a retired pipe installer, and a former ballroom dancer—who set out to forecast global events. Some of the volunteers have turned out to be astonishingly good. They’ve beaten other benchmarks, competitors, and prediction markets. They’ve even beaten the collective judgment of intelligence analysts with access to classified information. They are "superforecasters." In this groundbreaking and accessible book, Tetlock and Gardner show us how we can learn from this elite group. Weaving together stories of forecasting successes (the raid on Osama bin Laden’s compound) and failures (the Bay of Pigs) and interviews with a range of high-level decision makers, from David Petraeus to Robert Rubin, they show that good forecasting doesn’t require powerful computers or arcane methods. It involves gathering evidence from a variety of sources, thinking probabilistically, working in teams, keeping score, and being willing to admit error and change course. Superforecasting offers the first demonstrably effective way to improve our ability to predict the future—whether in business, finance, politics, international affairs, or daily life—and is destined to become a modern classic.
Book Synopsis Introduction to Data Science by : Rafael A. Irizarry
Download or read book Introduction to Data Science written by Rafael A. Irizarry and published by CRC Press. This book was released on 2019-11-20 with total page 794 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.
Book Synopsis Planning Clinical Research by : Robert A. Parker
Download or read book Planning Clinical Research written by Robert A. Parker and published by Cambridge University Press. This book was released on 2016-10-12 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: Planning clinical research requires many decisions. The authors of this book explain key decisions with examples showing what works and what does not.
Book Synopsis Predicting the Next President by : Allan J. Lichtman
Download or read book Predicting the Next President written by Allan J. Lichtman and published by Rowman & Littlefield. This book was released on 2024-07-01 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the days after Donald Trump’s unexpected victory on election night 2016, The New York Times, CNN, and other leading media outlets reached out to one of the few pundits who had correctly predicted the outcome, Allan J. Lichtman. While many election forecasters base their findings exclusively on public opinion polls, Lichtman looks at the underlying fundamentals that have driven every presidential election since 1860. Using his 13 historical factors or “keys” (four political, seven performance, and two personality), Lichtman had been predicting Trump’s win since September 2016. In the updated 2024 edition, he applies the keys to every presidential election since 1860 and shows readers the current state of the 2024 race. In doing so, he dispels much of the mystery behind electoral politics and challenges many traditional assumptions. An indispensable resource for political junkies!
Book Synopsis The SAGE Handbook of Multilevel Modeling by : Marc A. Scott
Download or read book The SAGE Handbook of Multilevel Modeling written by Marc A. Scott and published by SAGE. This book was released on 2013-08-31 with total page 954 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this important new Handbook, the editors have gathered together a range of leading contributors to introduce the theory and practice of multilevel modeling. The Handbook establishes the connections in multilevel modeling, bringing together leading experts from around the world to provide a roadmap for applied researchers linking theory and practice, as well as a unique arsenal of state-of-the-art tools. It forges vital connections that cross traditional disciplinary divides and introduces best practice in the field. Part I establishes the framework for estimation and inference, including chapters dedicated to notation, model selection, fixed and random effects, and causal inference. Part II develops variations and extensions, such as nonlinear, semiparametric and latent class models. Part III includes discussion of missing data and robust methods, assessment of fit and software. Part IV consists of exemplary modeling and data analyses written by methodologists working in specific disciplines. Combining practical pieces with overviews of the field, this Handbook is essential reading for any student or researcher looking to apply multilevel techniques in their own research.
Book Synopsis The Predictors of the Elder Care Experience by Adult Children by : Debra Lynn Sietsema
Download or read book The Predictors of the Elder Care Experience by Adult Children written by Debra Lynn Sietsema and published by . This book was released on 2005 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Regression and Other Stories by : Andrew Gelman
Download or read book Regression and Other Stories written by Andrew Gelman and published by Cambridge University Press. This book was released on 2020-07-23 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical approach to using regression and computation to solve real-world problems of estimation, prediction, and causal inference.
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 322 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
