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Bayesian Optimal Experimental Design For The Comparison Of Treatment With A Control In The Analysis Of Variance Setting
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Book Synopsis Bayesian Optimal Experimental Design for the Comparison of Treatment with a Control in the Analysis of Variance Setting by : Blaza Toman
Download or read book Bayesian Optimal Experimental Design for the Comparison of Treatment with a Control in the Analysis of Variance Setting written by Blaza Toman and published by . This book was released on 1987 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Journal of Statistical Planning and Inference by : North-Holland Publishing Company
Download or read book Journal of Statistical Planning and Inference written by North-Holland Publishing Company and published by . This book was released on 1991 with total page 906 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Comparisons Among Treatment Means in an Analysis of Variance by : Victor Chew
Download or read book Comparisons Among Treatment Means in an Analysis of Variance written by Victor Chew and published by . This book was released on 1977 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bulletin - Institute of Mathematical Statistics by : Institute of Mathematical Statistics
Download or read book Bulletin - Institute of Mathematical Statistics written by Institute of Mathematical Statistics and published by . This book was released on 1990 with total page 792 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book NBS Special Publication written by and published by . This book was released on 1970 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Optimal Experimental Design by : Jesús López-Fidalgo
Download or read book Optimal Experimental Design written by Jesús López-Fidalgo and published by Springer Nature. This book was released on 2023-10-14 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a concise introduction to optimal experimental design and efficiently prepares the reader for research in the area. It presents the common concepts and techniques for linear and nonlinear models as well as Bayesian optimal designs. The last two chapters are devoted to particular themes of interest, including recent developments and hot topics in optimal experimental design, and real-world applications. Numerous examples and exercises are included, some of them with solutions or hints, as well as references to the existing software for computing designs. The book is primarily intended for graduate students and young researchers in statistics and applied mathematics who are new to the field of optimal experimental design. Given the applications and the way concepts and results are introduced, parts of the text will also appeal to engineers and other applied researchers.
Book Synopsis Bayesian Optimal Experimental Design by : Ine Steyls
Download or read book Bayesian Optimal Experimental Design written by Ine Steyls and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Design and Analysis of Experiments, Volume 3 by : Klaus Hinkelmann
Download or read book Design and Analysis of Experiments, Volume 3 written by Klaus Hinkelmann and published by John Wiley & Sons. This book was released on 2012-02-14 with total page 598 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides timely applications, modifications, and extensions of experimental designs for a variety of disciplines Design and Analysis of Experiments, Volume 3: Special Designs and Applications continues building upon the philosophical foundations of experimental design by providing important, modern applications of experimental design to the many fields that utilize them. The book also presents optimal and efficient designs for practice and covers key topics in current statistical research. Featuring contributions from leading researchers and academics, the book demonstrates how the presented concepts are used across various fields from genetics and medicinal and pharmaceutical research to manufacturing, engineering, and national security. Each chapter includes an introduction followed by the historical background as well as in-depth procedures that aid in the construction and analysis of the discussed designs. Topical coverage includes: Genetic cross experiments, microarray experiments, and variety trials Clinical trials, group-sequential designs, and adaptive designs Fractional factorial and search, choice, and optimal designs for generalized linear models Computer experiments with applications to homeland security Robust parameter designs and split-plot type response surface designs Analysis of directional data experiments Throughout the book, illustrative and numerical examples utilize SAS®, JMP®, and R software programs to demonstrate the discussed techniques. Related data sets and software applications are available on the book's related FTP site. Design and Analysis of Experiments, Volume 3 is an ideal textbook for graduate courses in experimental design and also serves as a practical, hands-on reference for statisticians and researchers across a wide array of subject areas, including biological sciences, engineering, medicine, and business.
Book Synopsis Comprehensive Dissertation Index by :
Download or read book Comprehensive Dissertation Index written by and published by . This book was released on 1989 with total page 1016 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis An Author and Permuted Title Index to Selected Statistical Journals by : Brian L. Joiner
Download or read book An Author and Permuted Title Index to Selected Statistical Journals written by Brian L. Joiner and published by . This book was released on 1970 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: All articles, notes, queries, corrigenda, and obituaries appearing in the following journals during the indicated years are indexed: Annals of mathematical statistics, 1961-1969; Biometrics, 1965-1969#3; Biometrics, 1951-1969; Journal of the American Statistical Association, 1956-1969; Journal of the Royal Statistical Society, Series B, 1954-1969,#2; South African statistical journal, 1967-1969,#2; Technometrics, 1959-1969.--p.iv.
Book Synopsis Bayesian Data Analysis, Third Edition by : Andrew Gelman
Download or read book Bayesian Data Analysis, Third Edition written by Andrew Gelman and published by CRC Press. This book was released on 2013-11-01 with total page 677 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Book Synopsis Optimal Bayesian Experimental Design in the Presence of Model Error by :
Download or read book Optimal Bayesian Experimental Design in the Presence of Model Error written by and published by . This book was released on 2015 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt: The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction. We propose an information theoretic framework and algorithms for robust optimal experimental design with simulation-based models, with the goal of maximizing information gain in targeted subsets of model parameters, particularly in situations where experiments are costly. Our framework employs a Bayesian statistical setting, which naturally incorporates heterogeneous sources of information. An objective function reflects expected information gain from proposed experimental designs. Monte Carlo sampling is used to evaluate the expected information gain, and stochastic approximation algorithms make optimization feasible for computationally intensive and high-dimensional problems. A key aspect of our framework is the introduction of model calibration discrepancy terms that are used to "relax" the model so that proposed optimal experiments are more robust to model error or inadequacy. We illustrate the approach via several model problems and misspecification scenarios. In particular, we show how optimal designs are modified by allowing for model error, and we evaluate the performance of various designs by simulating "real-world" data from models not considered explicitly in the optimization objective.
Book Synopsis A Combinatorial Approach to Goal-oriented Optimal Bayesian Experimental Design by : Fengyi Li (S.M.)
Download or read book A Combinatorial Approach to Goal-oriented Optimal Bayesian Experimental Design written by Fengyi Li (S.M.) and published by . This book was released on 2019 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimal experimental design plays an important role in science and engineering. In many situations, we have many observations but only few of them can be selected due to limited resources. We then need to decide which ones to select based on our goal. In this thesis, we study the Bayesian linear Gaussian model with a large number of observations, and propose several algorithms for solving the combinatorial problem of observation selection/optimal experimental design in a goal-oriented setting. Here, the quantity of interest (QoI) is not the model parameters, but some (vector-valued) function of the parameters. We wish to select a subset of the candidate observations that is most informative for this QoI, in the sense of reducing its uncertainty. More precisely, we seek to maximize the mutual information between the selected observations and the QoI. Finding the true optimum is NP-hard, and in this setting, the mutual information objective is in general not submodular. We thus introduce several algorithms that approximate the optimal solution, including a greedy approach, a minorize-maximize approach employing modular bounds, and certain score-based heuristics. We compare the computational cost these algorithms, and demonstrate their performance on a synthetic data set and a real data set from a climate model.
Book Synopsis Numerical Approaches for Sequential Bayesian Optimal Experimental Design by : Xun Huan
Download or read book Numerical Approaches for Sequential Bayesian Optimal Experimental Design written by Xun Huan and published by . This book was released on 2015 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Experimental data play a crucial role in developing and refining models of physical systems. Some experiments can be more valuable than others, however. Well-chosen experiments can save substantial resources, and hence optimal experimental design (OED) seeks to quantify and maximize the value of experimental data. Common current practice for designing a sequence of experiments uses suboptimal approaches: batch (open-loop) design that chooses all experiments simultaneously with no feedback of information, or greedy (myopic) design that optimally selects the next experiment without accounting for future observations and dynamics. In contrast, sequential optimal experimental design (sOED) is free of these limitations. With the goal of acquiring experimental data that are optimal for model parameter inference, we develop a rigorous Bayesian formulation for OED using an objective that incorporates a measure of information gain. This framework is first demonstrated in a batch design setting, and then extended to sOED using a dynamic programming (DP) formulation. We also develop new numerical tools for sOED to accommodate nonlinear models with continuous (and often unbounded) parameter, design, and observation spaces. Two major techniques are employed to make solution of the DP problem computationally feasible. First, the optimal policy is sought using a one-step lookahead representation combined with approximate value iteration. This approximate dynamic programming method couples backward induction and regression to construct value function approximations. It also iteratively generates trajectories via exploration and exploitation to further improve approximation accuracy in frequently visited regions of the state space. Second, transport maps are used to represent belief states, which reflect the intermediate posteriors within the sequential design process. Transport maps offer a finite-dimensional representation of these generally non-Gaussian random variables, and also enable fast approximate Bayesian inference, which must be performed millions of times under nested combinations of optimization and Monte Carlo sampling. The overall sOED algorithm is demonstrated and verified against analytic solutions on a simple linear-Gaussian model. Its advantages over batch and greedy designs are then shown via a nonlinear application of optimal sequential sensing: inferring contaminant source location from a sensor in a time-dependent convection-diffusion system. Finally, the capability of the algorithm is tested for multidimensional parameter and design spaces in a more complex setting of the source inversion problem.
Book Synopsis Optimal Designs when Comparing Experimental Treatments with a Control by : Azhar Nizam
Download or read book Optimal Designs when Comparing Experimental Treatments with a Control written by Azhar Nizam and published by . This book was released on 1987 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bayesian Estimation and Experimental Design in Linear Regression Models by : Jürgen Pilz
Download or read book Bayesian Estimation and Experimental Design in Linear Regression Models written by Jürgen Pilz and published by . This book was released on 1983 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Design and Analysis of Non-Inferiority Trials by : Mark D. Rothmann
Download or read book Design and Analysis of Non-Inferiority Trials written by Mark D. Rothmann and published by CRC Press. This book was released on 2011-07-12 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: The increased use of non-inferiority analysis has been accompanied by a proliferation of research on the design and analysis of non-inferiority studies. Using examples from real clinical trials, Design and Analysis of Non-Inferiority Trials brings together this body of research and confronts the issues involved in the design of a non-inferiority trial. Each chapter begins with a non-technical introduction, making the text easily understood by those without prior knowledge of this type of trial. Topics covered include: A variety of issues of non-inferiority trials, including multiple comparisons, missing data, analysis population, the use of safety margins, the internal consistency of non-inferiority inference, the use of surrogate endpoints, trial monitoring, and equivalence trials Specific issues and analysis methods when the data are binary, continuous, and time-to-event The history of non-inferiority trials and the design and conduct considerations for a non-inferiority trial The strength of evidence of an efficacy finding and how to evaluate the effect size of an active control therapy A comprehensive discussion on the purpose and issues involved with non-inferiority trials, Design and Analysis of Non-inferiority Trials will assist current and future scientists and statisticians on the optimal design of non-inferiority trials and in assessing the quality of non-inferiority comparisons done in practice.
