Download or read book Adjusting for Selection Bias Using Gaussian Process Models written by Meng Du and published by . This book was released on 2014 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Selection Bias Adjustment in Treatment effect Models as a Method of Aggregation written by Robert A. Moffitt and published by . This book was released on 1995 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim of this note is to interpret estimation of the conventional treatment-effect selection-bias model in econometrics as a method of aggregation and to draw the implications of this interpretation. In addition, the paper notes the connection of this interpretation with an older style of analysis using grouped data and illustrates the aggregation analogy with examples from the literature. The estimation technique used to illustrate the points is the method of instrumental variables.
Download or read book Using Weights to Adjust for Sample Selection when Auxiliary Information is Available written by Aviv Nevo and published by . This book was released on 2001 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper I analyze GMM estimation when the sample is not a random draw from the population of interest. I exploit auxiliary information, in the form of moments from the population of interest, in order to compute weights that are proportional to the inverse probability of selection. The essential idea is to construct weights, for each observation in the primary data, such that the moments of the weighted data are set equal to the additional moments. The estimator is applied to the Dutch Transportation Panel, in which refreshment draws were taken from the population of interest in order to deal with heavy attrition of the original panel. I show how these additional samples can be used to adjust for sample selection.
Download or read book Variable Selection with Penalized Gaussian Process Regression Models written by Gang Yi and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Hierarchical Modeling and Analysis for Spatial Data Second Edition written by Sudipto Banerjee and published by CRC Press. This book was released on 2014-09-12 with total page 587 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keep Up to Date with the Evolving Landscape of Space and Space-Time Data Analysis and Modeling Since the publication of the first edition, the statistical landscape has substantially changed for analyzing space and space-time data. More than twice the size of its predecessor, Hierarchical Modeling and Analysis for Spatial Data, Second Edition reflects the major growth in spatial statistics as both a research area and an area of application. New to the Second Edition New chapter on spatial point patterns developed primarily from a modeling perspective New chapter on big data that shows how the predictive process handles reasonably large datasets New chapter on spatial and spatiotemporal gradient modeling that incorporates recent developments in spatial boundary analysis and wombling New chapter on the theoretical aspects of geostatistical (point-referenced) modeling Greatly expanded chapters on methods for multivariate and spatiotemporal modeling New special topics sections on data fusion/assimilation and spatial analysis for data on extremes Double the number of exercises Many more color figures integrated throughout the text Updated computational aspects, including the latest version of WinBUGS, the new flexible spBayes software, and assorted R packages The Only Comprehensive Treatment of the Theory, Methods, and Software This second edition continues to provide a complete treatment of the theory, methods, and application of hierarchical modeling for spatial and spatiotemporal data. It tackles current challenges in handling this type of data, with increased emphasis on observational data, big data, and the upsurge of associated software tools. The authors also explore important application domains, including environmental science, forestry, public health, and real estate.
Download or read book Efficient Reinforcement Learning Using Gaussian Processes written by Marc Peter Deisenroth and published by KIT Scientific Publishing. This book was released on 2010 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines Gaussian processes in both model-based reinforcement learning (RL) and inference in nonlinear dynamic systems.First, we introduce PILCO, a fully Bayesian approach for efficient RL in continuous-valued state and action spaces when no expert knowledge is available. PILCO takes model uncertainties consistently into account during long-term planning to reduce model bias. Second, we propose principled algorithms for robust filtering and smoothing in GP dynamic systems.
Download or read book Gaussian Processes for Machine Learning written by Carl Edward Rasmussen and published by MIT Press. This book was released on 2005-11-23 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.
Download or read book Selection Bias in the Estimation of Experimental and Quasi experimental Effects written by Robert A. Moffitt and published by . This book was released on 1977 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book The effect of and adjustment for selection bias in randomized controlled clinical trials written by Lieven Nils Kennes and published by . This book was released on 2013 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Applying Quantitative Bias Analysis to Epidemiologic Data written by Matthew P. Fox and published by Springer Nature. This book was released on 2022-03-24 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook and guide focuses on methodologies for bias analysis in epidemiology and public health, not only providing updates to the first edition but also further developing methods and adding new advanced methods. As computational power available to analysts has improved and epidemiologic problems have become more advanced, missing data, Bayes, and empirical methods have become more commonly used. This new edition features updated examples throughout and adds coverage addressing: Measurement error pertaining to continuous and polytomous variables Methods surrounding person-time (rate) data Bias analysis using missing data, empirical (likelihood), and Bayes methods A unique feature of this revision is its section on best practices for implementing, presenting, and interpreting bias analyses. Pedagogically, the text guides students and professionals through the planning stages of bias analysis, including the design of validation studies and the collection of validity data from other sources. Three chapters present methods for corrections to address selection bias, uncontrolled confounding, and measurement errors, and subsequent sections extend these methods to probabilistic bias analysis, missing data methods, likelihood-based approaches, Bayesian methods, and best practices.
Download or read book Scalable Approximate Inference and Model Selection in Gaussian Process Regression written by David Burt and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book On the Importance of Reliable Covariate Measurement in Selection Bias Adjustments Using Propensity Scores written by Peter M. Steiner and published by . This book was released on 2009 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper investigates how bias reduction was affected when different degrees of measurement error were systematically introduced into the measures constituting the final estimated propensity score (PS), the PS only for the set of effective covariates and the PS only for the ineffective ones. Since there was already some error in the Shadish et al. covariate measures, a more complex simulation was also done without this source of error. In many ways, this last analysis is the most important. This study uses data from a within-study comparison as the basis for a simulation study. The results suggest that the covariates deemed to be most effective in reducing selection bias should be reliably measured. Failure to do so will reduce the bias reduction achieved. In contrast, the reliability of ineffective covariates has minimal effect on bias reduction. Further, the larger the set of interrelated covariates used to control for selection bias the less sensitive is bias reduction to measurement error. But it is crucial to include the singly most effective covariates within this covariate set. The authors did not find any method specific differences in attenuation rates. When the same covariates are used in all analyses, PS methods show basically the same sensitivity to measurement errors in covariates as ANCOVA (analysis of covariance). (Contains 2 figures.).
Download or read book Understanding Models and Model Bias with Gaussian Processes written by Thomas R. Cook and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Gaussian Process Priors for Bayesian Portfolio Selection written by Roman Croessmann and published by . This book was released on 2018 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: This article shows how asset characteristics can be incorporated into the Bayesian portfolio selection framework. We use Gaussian process priors to model the belief that assets with similar characteristics are likely to have similar expected returns. The resulting Bayesian shrinkage estimator biases expected return estimates of assets with similar characteristics towards each other. A closed-form solution of the optimal portfolio weights in the Gaussian process prior framework is derived. Our simulation results and our empirical analysis suggest that portfolio selection with Gaussian process priors is a competitive alternative to benchmark portfolio selection approaches from the literature.
Download or read book MODEL SELECTION FOR GAUSSIAN PROCESS REGRESSION BY APPROXIMATION SET CODING written by Benjamin Fischer and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Selection Bias in Machine Learning written by Surendra Kumar Singhi and published by . This book was released on 2006 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Developing and Evaluating Methods for Mitigating Sample Selection Bias in Machine Learning written by Lourdes Pelayo Ramirez and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
