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Modeling Experimental And Observational Data
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Book Synopsis Modeling Experimental and Observational Data by : Clifford E. Lunneborg
Download or read book Modeling Experimental and Observational Data written by Clifford E. Lunneborg and published by Duxbury Resource Center. This book was released on 1994 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: Straightforward and accessible, this book shows students how modeling is a useful and powerful tool in the applied sciences. Lunneborg's unique approach emphasizes understanding concepts through examples rather than rote computation. Includes: -- A data analytic and model-building approach -- Modern and comprehensive treatment of simple, multiple, and logistic regression and the analysis of variance -- A focus on application and concepts rather than mathematical computation
Book Synopsis Measuring Racial Discrimination by : National Research Council
Download or read book Measuring Racial Discrimination written by National Research Council and published by National Academies Press. This book was released on 2004-07-24 with total page 335 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many racial and ethnic groups in the United States, including blacks, Hispanics, Asians, American Indians, and others, have historically faced severe discriminationâ€"pervasive and open denial of civil, social, political, educational, and economic opportunities. Today, large differences among racial and ethnic groups continue to exist in employment, income and wealth, housing, education, criminal justice, health, and other areas. While many factors may contribute to such differences, their size and extent suggest that various forms of discriminatory treatment persist in U.S. society and serve to undercut the achievement of equal opportunity. Measuring Racial Discrimination considers the definition of race and racial discrimination, reviews the existing techniques used to measure racial discrimination, and identifies new tools and areas for future research. The book conducts a thorough evaluation of current methodologies for a wide range of circumstances in which racial discrimination may occur, and makes recommendations on how to better assess the presence and effects of discrimination.
Book Synopsis Design and Analysis of Experiments and Observational Studies using R by : Nathan Taback
Download or read book Design and Analysis of Experiments and Observational Studies using R written by Nathan Taback and published by CRC Press. This book was released on 2022-03-10 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduction to Design and Analysis of Scientific Studies exposes undergraduate and graduate students to the foundations of classical experimental design and observational studies through a modern framework - The Rubin Causal Model. A causal inference framework is important in design, data collection and analysis since it provides a framework for investigators to readily evaluate study limitations and draw appropriate conclusions. R is used to implement designs and analyse the data collected. Features: Classical experimental design with an emphasis on computation using tidyverse packages in R. Applications of experimental design to clinical trials, A/B testing, and other modern examples. Discussion of the link between classical experimental design and causal inference. The role of randomization in experimental design and sampling in the big data era. Exercises with solutions. Instructor slides in RMarkdown, a new R package will be developed to be used with book, and a bookdown version of the book will be freely available. The proposed book will emphasize ethics, communication and decision making as part of design, data analysis, and statistical thinking.
Author :M. Elizabeth Halloran Publisher :Springer Science & Business Media ISBN 13 :0387686363 Total Pages :390 pages Book Rating :4.3/5 (876 download)
Book Synopsis Design and Analysis of Vaccine Studies by : M. Elizabeth Halloran
Download or read book Design and Analysis of Vaccine Studies written by M. Elizabeth Halloran and published by Springer Science & Business Media. This book was released on 2009-10-27 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: As well as being a reference for the design, analysis, and interpretation of vaccine studies, the text covers all design and analysis stages, from vaccine development to post-licensure surveillance, presenting likelihood, frequentists, and Bayesian approaches.
Book Synopsis Model-Oriented Design of Experiments by : Valerii V. Fedorov
Download or read book Model-Oriented Design of Experiments written by Valerii V. Fedorov and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 120 pages. Available in PDF, EPUB and Kindle. Book excerpt: Here, the authors explain the basic ideas so as to generate interest in modern problems of experimental design. The topics discussed include designs for inference based on nonlinear models, designs for models with random parameters and stochastic processes, designs for model discrimination and incorrectly specified (contaminated) models, as well as examples of designs in functional spaces. Since the authors avoid technical details, the book assumes only a moderate background in calculus, matrix algebra, and statistics. However, at many places, hints are given as to how readers may enhance and adopt the basic ideas for advanced problems or applications. This allows the book to be used for courses at different levels, as well as serving as a useful reference for graduate students and researchers in statistics and engineering.
Book Synopsis Uplift Modeling for Randomized Experiments and Observational Studies by : Xiao Fang (Ph. D.)
Download or read book Uplift Modeling for Randomized Experiments and Observational Studies written by Xiao Fang (Ph. D.) and published by . This book was released on 2018 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uplift modeling refers to the problem where we need to identify from a set of treatments the candidate that leads to the most desirable outcome based on subject characteristics. Most work in the last century focus on the average effect of a treatment across a population of interest, but ignores subject heterogeneity which is very common in real world. Recently there has been explosion of empirical settings which makes it possible to infer individualized treatment responses. We first consider the problem with data from randomized experiments. We put forward an unbiased estimate of the expected response, which makes it possible to evaluate an uplift model with multiple treatments. This is the first evaluation metric of uplift models aligning with the problem objective in the literature. Based on this evaluation metric, we design an ensemble tree-based algorithm (CTS) for uplift modeling. The splitting criterion and termination conditions are derived with the consideration of the special structure of uplift modeling problem. Experimental results on synthetic data and industry data show the advantage of our specialized uplift modeling algorithm over separate model approach and other existing uplift modeling algorithms. We next prove the asymptotic properties of a simplified CTS algorithm. The exhaustive search for locally optimal splitting points makes it difficult to theoretically analyze tree-based algorithms. Thus we adopt dyadic splits to CTS algorithm and obtain the bound of regret-expectation of performance difference between oracle and our algorithm. The convergence rate of the regret depends on the feature dimension, which emphasizes the importance of feature selection. While model performance usually improves with the number of features, it requires exponentially more data to approximate the optimal treatment rule. Choosing the appropriate complexity of the model and selecting the most powerful features are critical to achieving desirable performance. Finally we study the uplift modeling problem in the context of observational studies. In observational studies. treatment selection is influenced by subject characteristics. As a result. baseline characteristics often differ systematically between different treatments. Thus confounding factors need to be untangled before valid predictions are made. We combine a modification of the standard feed-forward architecture with our CTS algorithm to optimize predictive accuracy and minimize feature distribution distance between treatment. Experimental results on synthetic data show that the combination of neural network feature representation and ensemble tree-based model is promising to handle real-world problems.
Book Synopsis Spline Models for Observational Data by : Grace Wahba
Download or read book Spline Models for Observational Data written by Grace Wahba and published by SIAM. This book was released on 1990-09-01 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book serves well as an introduction into the more theoretical aspects of the use of spline models. It develops a theory and practice for the estimation of functions from noisy data on functionals. The simplest example is the estimation of a smooth curve, given noisy observations on a finite number of its values. Convergence properties, data based smoothing parameter selection, confidence intervals, and numerical methods are established which are appropriate to a number of problems within this framework. Methods for including side conditions and other prior information in solving ill posed inverse problems are provided. Data which involves samples of random variables with Gaussian, Poisson, binomial, and other distributions are treated in a unified optimization context. Experimental design questions, i.e., which functionals should be observed, are studied in a general context. Extensions to distributed parameter system identification problems are made by considering implicitly defined functionals.
Book Synopsis Probability Theory and Statistical Inference by : Aris Spanos
Download or read book Probability Theory and Statistical Inference written by Aris Spanos and published by Cambridge University Press. This book was released on 2019-09-19 with total page 787 pages. Available in PDF, EPUB and Kindle. Book excerpt: This empirical research methods course enables informed implementation of statistical procedures, giving rise to trustworthy evidence.
Book Synopsis Estimating Causal Effects by : Barbara Schneider
Download or read book Estimating Causal Effects written by Barbara Schneider and published by . This book was released on 2007 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains the value of quasi-experimental techniques that can be used to approximate randomized experiments. The goal is to describe the logic of causal inference for researchers and policymakers who are not necessarily trained in experimental and quasi-experimental designs and statistical techniques.
Book Synopsis Mathematical and Experimental Modeling of Physical and Biological Processes by : H.T. Banks
Download or read book Mathematical and Experimental Modeling of Physical and Biological Processes written by H.T. Banks and published by CRC Press. This book was released on 2009-01-12 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through several case study problems from industrial and scientific research laboratory applications, Mathematical and Experimental Modeling of Physical and Biological Processes provides students with a fundamental understanding of how mathematics is applied to problems in science and engineering. For each case study problem, the authors discuss why a model is needed and what goals can be achieved with the model. Exploring what mathematics can reveal about applications, the book focuses on the design of appropriate experiments to validate the development of mathematical models. It guides students through the modeling process, from empirical observations and formalization of properties to model analysis and interpretation of results. The authors also describe the hardware and software tools used to design the experiments so faculty/students can duplicate them. Integrating real-world applications into the traditional mathematics curriculum, this textbook deals with the formulation and analysis of mathematical models in science and engineering. It gives students an appreciation of the use of mathematics and encourages them to further study the applied topics. Real experimental data for projects can be downloaded from CRC Press Online.
Book Synopsis Targeted Learning by : Mark J. van der Laan
Download or read book Targeted Learning written by Mark J. van der Laan and published by Springer Science & Business Media. This book was released on 2011-06-17 with total page 628 pages. Available in PDF, EPUB and Kindle. Book excerpt: The statistics profession is at a unique point in history. The need for valid statistical tools is greater than ever; data sets are massive, often measuring hundreds of thousands of measurements for a single subject. The field is ready to move towards clear objective benchmarks under which tools can be evaluated. Targeted learning allows (1) the full generalization and utilization of cross-validation as an estimator selection tool so that the subjective choices made by humans are now made by the machine, and (2) targeting the fitting of the probability distribution of the data toward the target parameter representing the scientific question of interest. This book is aimed at both statisticians and applied researchers interested in causal inference and general effect estimation for observational and experimental data. Part I is an accessible introduction to super learning and the targeted maximum likelihood estimator, including related concepts necessary to understand and apply these methods. Parts II-IX handle complex data structures and topics applied researchers will immediately recognize from their own research, including time-to-event outcomes, direct and indirect effects, positivity violations, case-control studies, censored data, longitudinal data, and genomic studies.
Book Synopsis Design of Observational Studies by : Paul R. Rosenbaum
Download or read book Design of Observational Studies written by Paul R. Rosenbaum and published by Springer Science & Business Media. This book was released on 2009-10-22 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: An observational study is an empiric investigation of effects caused by treatments when randomized experimentation is unethical or infeasible. Observational studies are common in most fields that study the effects of treatments on people, including medicine, economics, epidemiology, education, psychology, political science and sociology. The quality and strength of evidence provided by an observational study is determined largely by its design. Design of Observational Studies is both an introduction to statistical inference in observational studies and a detailed discussion of the principles that guide the design of observational studies. Design of Observational Studies is divided into four parts. Chapters 2, 3, and 5 of Part I cover concisely, in about one hundred pages, many of the ideas discussed in Rosenbaum’s Observational Studies (also published by Springer) but in a less technical fashion. Part II discusses the practical aspects of using propensity scores and other tools to create a matched comparison that balances many covariates. Part II includes a chapter on matching in R. In Part III, the concept of design sensitivity is used to appraise the relative ability of competing designs to distinguish treatment effects from biases due to unmeasured covariates. Part IV discusses planning the analysis of an observational study, with particular reference to Sir Ronald Fisher’s striking advice for observational studies, "make your theories elaborate." The second edition of his book, Observational Studies, was published by Springer in 2002.
Book Synopsis Analysis of Observational Health Care Data Using SAS by : Douglas E. Faries
Download or read book Analysis of Observational Health Care Data Using SAS written by Douglas E. Faries and published by SAS Press. This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book guides researchers in performing and presenting high-quality analyses of all kinds of non-randomized studies, including analyses of observational studies, claims database analyses, assessment of registry data, survey data, pharmaco-economic data, and many more applications. The text is sufficiently detailed to provide not only general guidance, but to help the researcher through all of the standard issues that arise in such analyses. Just enough theory is included to allow the reader to understand the pros and cons of alternative approaches and when to use each method. The numerous contributors to this book illustrate, via real-world numerical examples and SAS code, appropriate implementations of alternative methods. The end result is that researchers will learn how to present high-quality and transparent analyses that will lead to fair and objective decisions from observational data. This book is part of the SAS Press program.
Author :Agency for Health Care Research and Quality (U.S.) Publisher :Government Printing Office ISBN 13 :1587634236 Total Pages :236 pages Book Rating :4.5/5 (876 download)
Book Synopsis Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide by : Agency for Health Care Research and Quality (U.S.)
Download or read book Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide written by Agency for Health Care Research and Quality (U.S.) and published by Government Printing Office. This book was released on 2013-02-21 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)
Book Synopsis The Experimental Side of Modeling by : Isabelle F. Peschard
Download or read book The Experimental Side of Modeling written by Isabelle F. Peschard and published by U of Minnesota Press. This book was released on 2018-10-02 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: An innovative, multifaceted approach to scientific experiments as designed by and shaped through interaction with the modeling process The role of scientific modeling in mediation between theories and phenomena is a critical topic within the philosophy of science, touching on issues from climate modeling to synthetic models in biology, high energy particle physics, and cognitive sciences. Offering a radically new conception of the role of data in the scientific modeling process as well as a new awareness of the problematic aspects of data, this cutting-edge volume offers a multifaceted view on experiments as designed and shaped in interaction with the modeling process. Contributors address such issues as the construction of models in conjunction with scientific experimentation; the status of measurement and the function of experiment in the identification of relevant parameters; how the phenomena under study are reconceived when accounted for by a model; and the interplay between experimenting, modeling, and simulation when results do not mesh. Highlighting the mediating role of models and the model-dependence (as well as theory-dependence) of data measurement, this volume proposes a normative and conceptual innovation in scientific modeling—that the phenomena to be investigated and modeled must not be precisely identified at the start but specified during the course of the interactions arising between experimental and modeling activities. Contributors: Nancy D. Cartwright, U of California, San Diego; Anthony Chemero, U of Cincinnati; Ronald N. Giere, U of Minnesota; Jenann Ismael, U of Arizona; Tarja Knuuttila, U of South Carolina; Andrea Loettgers, U of Bern, Switzerland; Deborah Mayo, Virginia Tech; Joseph Rouse, Wesleyan U; Paul Teller, U of California, Davis; Michael Weisberg, U of Pennsylvania; Eric Winsberg, U of South Florida.
Book Synopsis Observational Studies by : Paul R. Rosenbaum
Download or read book Observational Studies written by Paul R. Rosenbaum and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: An observational study is an empirical investigation of the effects of treatments, policies, or exposures. It differes from an experiment in that the investigator cannot control the assignments of treatments to subjects. Scientists across a wide range of disciplines undertake such studies, and the aim of this book is to provide a sound statistical account of the principles and methods for the design and analysis of observational studies. Readers are assumed to have a working knowledge of basic probability and statistics, but otherwise the account is reasonably self-contained. Throughout there are extended discussions of actual observational studies to illustrate the ideas discussed. These are drawn from topics as diverse as smoking and lung cancer, lead in children, nuclear weapons testing, and placement programs for students. As a result, many researchers involved in observational studes will find this an invaluable companion to their work.
Book Synopsis Empirical Modeling and Data Analysis for Engineers and Applied Scientists by : Scott A. Pardo
Download or read book Empirical Modeling and Data Analysis for Engineers and Applied Scientists written by Scott A. Pardo and published by Springer. This book was released on 2016-07-19 with total page 255 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions. While science is about discovery, the primary paradigm of engineering and "applied science" is design. Scientists are in the discovery business and want, in general, to understand the natural world rather than to alter it. In contrast, engineers and applied scientists design products, processes, and solutions to problems. That said, statistics, as a discipline, is mostly oriented toward the discovery paradigm. Young engineers come out of their degree programs having taken courses such as "Statistics for Engineers and Scientists" without any clear idea as to how they can use statistical methods to help them design products or processes. Many seem to think that statistics is only useful for demonstrating that a device or process actually does what it was designed to do. Statistics courses emphasize creating predictive or classification models - predicting nature or classifying individuals, and statistics is often used to prove or disprove phenomena as opposed to aiding in the design of a product or process. In industry however, Chemical Engineers use designed experiments to optimize petroleum extraction; Manufacturing Engineers use experimental data to optimize machine operation; Industrial Engineers might use data to determine the optimal number of operators required in a manual assembly process. This text teaches engineering and applied science students to incorporate empirical investigation into such design processes. Much of the discussion in this book is about models, not whether the models truly represent reality but whether they adequately represent reality with respect to the problems at hand; many ideas focus on how to gather data in the most efficient way possible to construct adequate models. Includes chapters on subjects not often seen together in a single text (e.g., measurement systems, mixture experiments, logistic regression, Taguchi methods, simulation) Techniques and concepts introduced present a wide variety of design situations familiar to engineers and applied scientists and inspire incorporation of experimentation and empirical investigation into the design process. Software is integrally linked to statistical analyses with fully worked examples in each chapter; fully worked using several packages: SAS, R, JMP, Minitab, and MS Excel - also including discussion questions at the end of each chapter. The fundamental learning objective of this textbook is for the reader to understand how experimental data can be used to make design decisions and to be familiar with the most common types of experimental designs and analysis methods.
