Download or read book Control Variates for Monte Carlo Analysis of Nonlinear Statitical sic Models written by James J. Swain and published by . This book was released on 1989 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Monte Carlo Analysis of Nonlinear Statistical Models I Theory Revision written by J. J. Swain and published by . This book was released on 1985 with total page 18 pages. Available in PDF, EPUB and Kindle. Book excerpt: Parameter values of nonlinear statistical models are typically estimated from data using iterative numerical procedures. The resulting joint sampling distribution of the parameter estimators is often intractable, resulting in the use of approximators or Monte Carlo simulation to determine properties of the sampling distribution. This paper develops methods, using linear and quadratic approximators as control variates, that reduce the variance of the Monte Carlo estimator by orders of magnitude. Estimation of means, higher order raw moments, variances, covariances, and percentiles is considered. (Author).
Download or read book Simulation and the Monte Carlo Method written by Reuven Y. Rubinstein and published by John Wiley & Sons. This book was released on 2009-09-25 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the first simultaneous coverage of the statistical aspects of simulation and Monte Carlo methods, their commonalities and their differences for the solution of a wide spectrum of engineering and scientific problems. It contains standard material usually considered in Monte Carlo simulation as well as new material such as variance reduction techniques, regenerative simulation, and Monte Carlo optimization.
Download or read book Monte Carlo Methods in Statistical Physics written by M. E. J. Newman and published by Clarendon Press. This book was released on 1999-02-11 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to Monte Carlo simulations in classical statistical physics and is aimed both at students beginning work in the field and at more experienced researchers who wish to learn more about Monte Carlo methods. The material covered includes methods for both equilibrium and out of equilibrium systems, and common algorithms like the Metropolis and heat-bath algorithms are discussed in detail, as well as more sophisticated ones such as continuous time Monte Carlo, cluster algorithms, multigrid methods, entropic sampling and simulated tempering. Data analysis techniques are also explained starting with straightforward measurement and error-estimation techniques and progressing to topics such as the single and multiple histogram methods and finite size scaling. The last few chapters of the book are devoted to implementation issues, including discussions of such topics as lattice representations, efficient implementation of data structures, multispin coding, parallelization of Monte Carlo algorithms, and random number generation. At the end of the book the authors give a number of example programmes demonstrating the applications of these techniques to a variety of well-known models.
Download or read book Control Variate Approach for Multi user Estimation Via Monte Carlo Simulation written by Na Sun and published by . This book was released on 2013 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Monte Carlo (MC) simulation forms a very flexible and widely used computational method employed in many areas of science and engineering. The focus of this research is on the variance reduction technique of Control Variates (CV) which is a statistical approach used to improve the efficiency of MC simulation. We consider parametric estimation problems encountered in analysing stochastic systems where the stochastic system performance or its sensitivity depends on some model or decision parameter. Furthermore, we assume that the estimation is performed by one or more users at one or several parameter values. A store and reuse setting is introduced where at a set-up stage sonic information is gathered computationally and stored. The stored information is then used at the estimation phase by users to help with their estimation problems.Three problems in this setting are addressed. (i) An analysis of the user's choices at the estimation phase is provided. The information generated at the set-up phase is stored in the form of information about a set of random variables that can be used as control variates. Users need to decide whether, and if so how, to use the stored information. A so-called cost-adjusted mean squared error is used as a measure cost of the available estimators and user's decision is formulated as a constrained minimization problem. (ii) A recent approach to defining generic control variates in parametric estimation problems is generalized in two distinct directions: the first involves considering an alternative parametrization of the original problem through a change of probability measure. This parametrization is particularly relevant to sensitivity estimation problems with respect to model and decision parameters. In the second, for problems where the quantities of interest are defined on sample paths of stochastic processes that model the underlying stochastic dynamics, systematic control variate selection based on approximate dynamics is proposed. (iii) When common random inputs are used parametric estimation variables become statistically dependent. This dependence is explicitly modelled as a random field and conditions are derived to imply the effectiveness of estimation variables as control variates. Comparisons with the metamodeling approach of Kriging and recently proposed Stochastic Kriging that use similar inputs data to predict the mean of the estimation variable are provided.
Download or read book A Guide to Monte Carlo Simulations in Statistical Physics written by David P. Landau and published by Cambridge University Press. This book was released on 2000-08-17 with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes all aspects of Monte Carlo simulation of complex physical systems encountered in condensed-matter physics and statistical mechanics, as well as in related fields, such as polymer science and lattice gauge theory. The authors give a succinct overview of simple sampling methods and develop the importance sampling method. In addition they introduce quantum Monte Carlo methods, aspects of simulations of growth phenomena and other systems far from equilibrium, and the Monte Carlo Renormalization Group approach to critical phenomena. The book includes many applications, examples, and current references, and exercises to help the reader.
Download or read book Adaptive Control Variates in Monte Carlo Simulation written by Sujin Kim and published by . This book was released on 2006 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Backward Simulation Methods for Monte Carlo Statistical Inference written by Fredrik Lindsten and published by . This book was released on 2013 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: Backward Simulation Methods for Monte Carlo Statistical Inference presents and discusses various backward simulation methods for Monte Carlo statistical inference. The focus is on SMC-based backward simulators, which are useful for inference in analytically intractable models, such as nonlinear and/or non-Gaussian SSMs, but also in more general latent variable models.
Download or read book Monte Carlo Methods in Statistical Physics written by Kurt Binder and published by Springer. This book was released on 1979 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Regression based Monte Carlo Methods with Optimal Control Variates written by Stefan Häfner and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Control Variate Method for Monte Carlo Simulations of Heath Jarrow Morton Models with Jumps written by Carl Chiarella and published by . This book was released on 2005 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Monte Carlo Simulation Study of a Non linear Statistical Model written by Sheldon Fred Whitaker and published by . This book was released on 1967 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Constructive and Generic Control Variates for Monte Carlo Estimation written by Tarik Borogovac and published by . This book was released on 2009 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: Estimation of quantities that can be represented as expectations of appropriately defined random variables is an important problem in diverse areas of science and engineering. Monte Carlo (MC) sampling/simulation is a very general approach for estimation, and is the method of choice in many application areas. To increase the computational efficiency of MC simulation a number of Variance Reduction Techniques (VRT), which aim to reduce the variance of the MC estimator, have been devised. The design of effective VRT's has so far relied on the existence of specific problem features, and the acuity of the user to discover and properly exploit such features. One of the most effective VRT's is the method of Control Variates (CV). This method relies on a number of auxiliary random variables, called controls, that carry information about the estimation variable and "explain" part of its variance. If the means of the controls are known, or high quality estimates of them are available, the CV technique prescribes a generic procedure for transferring the relevant information to the estimation variable, leading to a controlled estimator with smaller variance. The main difficulty with the CV technique is in discovering controls that are informative about the estimation variable. This thesis presents a generic approach to the selection controls that is applicable to a broad class of problems where the estimation variable depends on a model parameter. It is shown that, under conditions, information at a set of parameters can be used to define effective controls for estimation at neighboring parameters. A connection between sample-wise function approximation methods and the CV method is established. Motivated by this connection, controls for the estimation variable and for its sensitivity with respect to the parameter are proposed. Their effectiveness is demonstrated on simulations from the fields of finance, materials science and photon transport. The requirement of tractability of controls is replaced by generic computational procedures through which the necessary information about the controls is procured. Two alternative algorithms that perform this function are given, and the CV estimators that result are analyzed.
Download or read book Monte Carlo Methods in Derivative Modelling written by Kai Zhang and published by . This book was released on 2011 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Techniques for Efficient Monte Carlo Simulation Volume III Variance Reduction written by E. J. McGrath and published by . This book was released on 1973 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many Monte Carlo simulation problems lend themselves readily to the application of variance reduction techniques. These techniques can result in great improvements in simulation efficiency. The document describes the basic concepts of variance reduction (Part 1), and a methodology for application of variance reduction techniques is presented in Part 2. Appendices include the basic analytical expressions for application of variance reduction schemes as well as an abstracted bibliography. The techniques considered here include importance sampling, Russian roulette and splitting, systematic sampling, stratified sampling, expected values, statistical estimation, correlated sampling, history reanalysis, control variates, antithetic variates, regression, sequantial sampling, adjoint formulation, transformations, orthonormal and conditional Monte Carlo. Emphasis has been placed on presentation of the material for application by the general user. This has been accomplished by presenting a step by step procedure for selection and application of the appropriate technique(s) for a given problem. (Author).
Download or read book Survival Analysis written by John P. Klein and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Making complex methods more accessible to applied researchers without an advanced mathematical background, the authors present the essence of new techniques available, as well as classical techniques, and apply them to data. Practical suggestions for implementing the various methods are set off in a series of practical notes at the end of each section, while technical details of the derivation of the techniques are sketched in the technical notes. This book will thus be useful for investigators who need to analyse censored or truncated life time data, and as a textbook for a graduate course in survival analysis, the only prerequisite being a standard course in statistical methodology.
Download or read book Nonlinear Time Series Analysis written by Ruey S. Tsay and published by John Wiley & Sons. This book was released on 2018-09-14 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. The authors—noted experts in the field—explore the advantages and limitations of the nonlinear models and methods and review the improvements upon linear time series models. The need for this book is based on the recent developments in nonlinear time series analysis, statistical learning, dynamic systems and advanced computational methods. Parametric and nonparametric methods and nonlinear and non-Gaussian state space models provide a much wider range of tools for time series analysis. In addition, advances in computing and data collection have made available large data sets and high-frequency data. These new data make it not only feasible, but also necessary to take into consideration the nonlinearity embedded in most real-world time series. This vital guide: • Offers research developed by leading scholars of time series analysis • Presents R commands making it possible to reproduce all the analyses included in the text • Contains real-world examples throughout the book • Recommends exercises to test understanding of material presented • Includes an instructor solutions manual and companion website Written for students, researchers, and practitioners who are interested in exploring nonlinearity in time series, Nonlinear Time Series Analysis offers a comprehensive text that explores the advantages and limitations of the nonlinear models and methods and demonstrates the improvements upon linear time series models.
