Journal Of Computational And Graphical Statistics Ebook PDF (2023)

• Author : American Statistical Association
• Publisher :
• Pages :
• Relase : 1992
• ISBN : OCLC:959769898
• Rating : 4/5 (411 users)

Journal of Computational and Graphical Statistics by American Statistical Association Book PDF

• Author : Anonim
• Publisher :
• Pages : 308
• Relase : 1999
• ISBN : UCSD:31822020366621
• Rating : 4/5 (411 users)

Journal of Computational and Graphical Statistics by Anonim Book PDF

• Author : American Statistical Association
• Publisher :
• Pages :
• Relase : 1995
• ISBN : OCLC:959374420
• Rating : 4/5 (411 users)

Journal of Computational and Graphical Statistics (majalah). by American Statistical Association Book PDF

• Author : Anonim
• Publisher :
• Pages : 156
• Relase : 1999
• ISBN : UCSD:31822020366688
• Rating : 4/5 (411 users)

Journal of Computational and Graphical Statistics by Anonim Book PDF

• Author : Nonvikan Karl-Augustt Alahassa
• Publisher : Nonvikan Karl-Augustt Alahassa
• Pages : 227
• Relase : 2021-08-17
• ISBN :
• Rating : 4/5 (411 users)

The Theory of Perfect Learning by Nonvikan Karl-Augustt Alahassa Book PDF

The perfect learning exists. We mean a learning model that can be generalized, and moreover, that can always fit perfectly the test data, as well as the training data. We have performed in this thesis many experiments that validate this concept in many ways. The tools are given through the chapters that contain our developments. The classical Multilayer Feedforward model has been re-considered and a novel $N_k$-architecture is proposed to fit any multivariate regression task. This model can easily be augmented to thousands of possible layers without loss of predictive power, and has the potential to overcome our difficulties simultaneously in building a model that has a good fit on the test data, and don't overfit. His hyper-parameters, the learning rate, the batch size, the number of training times (epochs), the size of each layer, the number of hidden layers, all can be chosen experimentally with cross-validation methods. There is a great advantage to build a more powerful model using mixture models properties. They can self-classify many high dimensional data in a few numbers of mixture components. This is also the case of the Shallow Gibbs Network model that we built as a Random Gibbs Network Forest to reach the performance of the Multilayer feedforward Neural Network in a few numbers of parameters, and fewer backpropagation iterations. To make it happens, we propose a novel optimization framework for our Bayesian Shallow Network, called the {Double Backpropagation Scheme} (DBS) that can also fit perfectly the data with appropriate learning rate, and which is convergent and universally applicable to any Bayesian neural network problem. The contribution of this model is broad. First, it integrates all the advantages of the Potts Model, which is a very rich random partitions model, that we have also modified to propose its Complete Shrinkage version using agglomerative clustering techniques. The model takes also an advantage of Gibbs Fields for its weights precision matrix structure, mainly through Markov Random Fields, and even has five (5) variants structures at the end: the Full-Gibbs, the Sparse-Gibbs, the Between layer Sparse Gibbs which is the B-Sparse Gibbs in a short, the Compound Symmetry Gibbs (CS-Gibbs in short), and the Sparse Compound Symmetry Gibbs (Sparse-CS-Gibbs) model. The Full-Gibbs is mainly to remind fully-connected models, and the other structures are useful to show how the model can be reduced in terms of complexity with sparsity and parsimony. All those models have been experimented, and the results arouse interest in those structures, in a sense that different structures help to reach different results in terms of Mean Squared Error (MSE) and Relative Root Mean Squared Error (RRMSE). For the Shallow Gibbs Network model, we have found the perfect learning framework : it is the $(l_1, \boldsymbol{\zeta}, \epsilon_{dbs})-\textbf{DBS}$ configuration, which is a combination of the \emph{Universal Approximation Theorem}, and the DBS optimization, coupled with the (\emph{dist})-Nearest Neighbor-(h)-Taylor Series-Perfect Multivariate Interpolation (\emph{dist}-NN-(h)-TS-PMI) model [which in turn is a combination of the research of the Nearest Neighborhood for a good Train-Test association, the Taylor Approximation Theorem, and finally the Multivariate Interpolation Method]. It indicates that, with an appropriate number $l_1$ of neurons on the hidden layer, an optimal number $\zeta$ of DBS updates, an optimal DBS learnnig rate $\epsilon_{dbs}$, an optimal distance \emph{dist}$_{opt}$ in the research of the nearest neighbor in the training dataset for each test data $x_i^{\mbox{test}}$, an optimal order $h_{opt}$ of the Taylor approximation for the Perfect Multivariate Interpolation (\emph{dist}-NN-(h)-TS-PMI) model once the {\bfseries DBS} has overfitted the training dataset, the train and the test error converge to zero (0). As the Potts Models and many random Partitions are based on a similarity measure, we open the door to find \emph{sufficient} invariants descriptors in any recognition problem for complex objects such as image; using \emph{metric} learning and invariance descriptor tools, to always reach 100\% accuracy. This is also possible with invariant networks that are also universal approximators. Our work closes the gap between the theory and the practice in artificial intelligence, in a sense that it confirms that it is possible to learn with very small error allowed.

• Author : Walter W. Piegorsch
• Publisher : John Wiley & Sons
• Pages : 488
• Relase : 2015-06-11
• ISBN : 9781119043577
• Rating : 4/5 (411 users)

Statistical Data Analytics by Walter W. Piegorsch Book PDF

A comprehensive introduction to statistical methods for data mining and knowledge discovery. Applications of data mining and ‘big data’ increasingly take center stage in our modern, knowledge-driven society, supported by advances in computing power, automated data acquisition, social media development and interactive, linkable internet software. This book presents a coherent, technical introduction to modern statistical learning and analytics, starting from the core foundations of statistics and probability. It includes an overview of probability and statistical distributions, basics of data manipulation and visualization, and the central components of standard statistical inferences. The majority of the text extends beyond these introductory topics, however, to supervised learning in linear regression, generalized linear models, and classification analytics. Finally, unsupervised learning via dimension reduction, cluster analysis, and market basket analysis are introduced. Extensive examples using actual data (with sample R programming code) are provided, illustrating diverse informatic sources in genomics, biomedicine, ecological remote sensing, astronomy, socioeconomics, marketing, advertising and finance, among many others. Statistical Data Analytics: Focuses on methods critically used in data mining and statistical informatics. Coherently describes the methods at an introductory level, with extensions to selected intermediate and advanced techniques. Provides informative, technical details for the highlighted methods. Employs the open-source R language as the computational vehicle – along with its burgeoning collection of online packages – to illustrate many of the analyses contained in the book. Concludes each chapter with a range of interesting and challenging homework exercises using actual data from a variety of informatic application areas. This book will appeal as a classroom or training text to intermediate and advanced undergraduates, and to beginning graduate students, with sufficient background in calculus and matrix algebra. It will also serve as a source-book on the foundations of statistical informatics and data analytics to practitioners who regularly apply statistical learning to their modern data.

• Author : Alan Genz,Frank Bretz
• Publisher : Springer Science & Business Media
• Pages : 126
• Relase : 2009-07-09
• ISBN : 9783642016899
• Rating : 4/5 (411 users)

Computation of Multivariate Normal and t Probabilities by Alan Genz,Frank Bretz Book PDF

Multivariate normal and t probabilities are needed for statistical inference in many applications. Modern statistical computation packages provide functions for the computation of these probabilities for problems with one or two variables. This book describes recently developed methods for accurate and efficient computation of the required probability values for problems with two or more variables. The book discusses methods for specialized problems as well as methods for general problems. The book includes examples that illustrate the probability computations for a variety of applications.

• Author : Scott A. Sisson,Yanan Fan,Mark Beaumont
• Publisher : CRC Press
• Pages : 424
• Relase : 2018-09-03
• ISBN : 9781351643467
• Rating : 4/5 (411 users)

Handbook of Approximate Bayesian Computation by Scott A. Sisson,Yanan Fan,Mark Beaumont Book PDF

As the world becomes increasingly complex, so do the statistical models required to analyse the challenging problems ahead. For the very first time in a single volume, the Handbook of Approximate Bayesian Computation (ABC) presents an extensive overview of the theory, practice and application of ABC methods. These simple, but powerful statistical techniques, take Bayesian statistics beyond the need to specify overly simplified models, to the setting where the model is defined only as a process that generates data. This process can be arbitrarily complex, to the point where standard Bayesian techniques based on working with tractable likelihood functions would not be viable. ABC methods finesse the problem of model complexity within the Bayesian framework by exploiting modern computational power, thereby permitting approximate Bayesian analyses of models that would otherwise be impossible to implement. The Handbook of ABC provides illuminating insight into the world of Bayesian modelling for intractable models for both experts and newcomers alike. It is an essential reference book for anyone interested in learning about and implementing ABC techniques to analyse complex models in the modern world.

• Author : Geof H. Givens,Jennifer A. Hoeting
• Publisher : John Wiley & Sons
• Pages : 496
• Relase : 2012-10-09
• ISBN : 9781118555484
• Rating : 4/5 (411 users)

Computational Statistics by Geof H. Givens,Jennifer A. Hoeting Book PDF

This new edition continues to serve as a comprehensive guide to modern and classical methods of statistical computing. The book is comprised of four main parts spanning the field: Optimization Integration and Simulation Bootstrapping Density Estimation and Smoothing Within these sections,each chapter includes a comprehensive introduction and step-by-step implementation summaries to accompany the explanations of key methods. The new edition includes updated coverage and existing topics as well as new topics such as adaptive MCMC and bootstrapping for correlated data. The book website now includes comprehensive R code for the entire book. There are extensive exercises, real examples, and helpful insights about how to use the methods in practice.

• Author : Zhezhen Jin,Mengling Liu,Xiaolong Luo
• Publisher : Springer
• Pages : 214
• Relase : 2016-10-28
• ISBN : 9783319425719
• Rating : 4/5 (411 users)

New Developments in Statistical Modeling, Inference and Application by Zhezhen Jin,Mengling Liu,Xiaolong Luo Book PDF

The papers in this volume represent the most timely and advanced contributions to the 2014 Joint Applied Statistics Symposium of the International Chinese Statistical Association (ICSA) and the Korean International Statistical Society (KISS), held in Portland, Oregon. The contributions cover new developments in statistical modeling and clinical research: including model development, model checking, and innovative clinical trial design and analysis. Each paper was peer-reviewed by at least two referees and also by an editor. The conference was attended by over 400 participants from academia, industry, and government agencies around the world, including from North America, Asia, and Europe. It offered 3 keynote speeches, 7 short courses, 76 parallel scientific sessions, student paper sessions, and social events.

• Author : Anonim
• Publisher : ScholarlyEditions
• Pages : 993
• Relase : 2012-01-09
• ISBN : 9781464964787
• Rating : 4/5 (411 users)

Issues in Logic, Operations, and Computational Mathematics and Geometry: 2011 Edition by Anonim Book PDF

Issues in Logic, Operations, and Computational Mathematics and Geometry: 2011 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Logic, Operations, and Computational Mathematics and Geometry. The editors have built Issues in Logic, Operations, and Computational Mathematics and Geometry: 2011 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Logic, Operations, and Computational Mathematics and Geometry in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Logic, Operations, and Computational Mathematics and Geometry: 2011 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

• Author : Eric D. Feigelson,Jogesh Babu
• Publisher : Springer Science & Business Media
• Pages : 506
• Relase : 2006-05-26
• ISBN : 9780387215297
• Rating : 4/5 (411 users)

Statistical Challenges in Astronomy by Eric D. Feigelson,Jogesh Babu Book PDF

Digital sky surveys, high-precision astrometry from satellite data, deep-space data from orbiting telescopes, and the like have all increased the quantity and quality of astronomical data by orders of magnitude per year for several years. Making sense of this wealth of data requires sophisticated statistical techniques. Fortunately, statistical methodologies have similarly made great strides in recent years. Powerful synergies thus emerge when astronomers and statisticians join in examining astrostatistical problems and approaches. The book begins with an historical overview and tutorial articles on basic cosmology for statisticians and the principles of Bayesian analysis for astronomers. As in earlier volumes in this series, research contributions discussing topics in one field are joined with commentary from scholars in the other. Thus, for example, an overview of Bayesian methods for Poissonian data is joined by discussions of planning astronomical observations with optimal efficiency and nested models to deal with instrumental effects. The principal theme for the volume is the statistical methods needed to model fundamental characteristics of the early universe on its largest scales.

• Author : Yanan Fan,David Nott,Mike Smith,Jean-Luc Dortet-Bernadet
• Publisher : Academic Press
• Pages : 302
• Relase : 2019-10-30
• ISBN : 9780128158630
• Rating : 4/5 (411 users)

Flexible Bayesian Regression Modelling by Yanan Fan,David Nott,Mike Smith,Jean-Luc Dortet-Bernadet Book PDF

Flexible Bayesian Regression Modeling is a step-by-step guide to the Bayesian revolution in regression modeling, for use in advanced econometric and statistical analysis where datasets are characterized by complexity, multiplicity, and large sample sizes, necessitating the need for considerable flexibility in modeling techniques. It reviews three forms of flexibility: methods which provide flexibility in their error distribution; methods which model non-central parts of the distribution (such as quantile regression); and finally models that allow the mean function to be flexible (such as spline models). Each chapter discusses the key aspects of fitting a regression model. R programs accompany the methods. This book is particularly relevant to non-specialist practitioners with intermediate mathematical training seeking to apply Bayesian approaches in economics, biology, finance, engineering and medicine. Introduces powerful new nonparametric Bayesian regression techniques to classically trained practitioners Focuses on approaches offering both superior power and methodological flexibility Supplemented with instructive and relevant R programs within the text Covers linear regression, nonlinear regression and quantile regression techniques Provides diverse disciplinary case studies for correlation and optimization problems drawn from Bayesian analysis ‘in the wild’

• Author : Ding-Geng Chen,Zhezhen Jin,Gang Li,Yi Li,Aiyi Liu,Yichuan Zhao
• Publisher : Springer
• Pages : 348
• Relase : 2018-01-17
• ISBN : 9783319694160
• Rating : 4/5 (411 users)

New Advances in Statistics and Data Science by Ding-Geng Chen,Zhezhen Jin,Gang Li,Yi Li,Aiyi Liu,Yichuan Zhao Book PDF

This book is comprised of the presentations delivered at the 25th ICSA Applied Statistics Symposium held at the Hyatt Regency Atlanta, on June 12-15, 2016. This symposium attracted more than 700 statisticians and data scientists working in academia, government, and industry from all over the world. The theme of this conference was the “Challenge of Big Data and Applications of Statistics,” in recognition of the advent of big data era, and the symposium offered opportunities for learning, receiving inspirations from old research ideas and for developing new ones, and for promoting further research collaborations in the data sciences. The invited contributions addressed rich topics closely related to big data analysis in the data sciences, reflecting recent advances and major challenges in statistics, business statistics, and biostatistics. Subsequently, the six editors selected 19 high-quality presentations and invited the speakers to prepare full chapters for this book, which showcases new methods in statistics and data sciences, emerging theories, and case applications from statistics, data science and interdisciplinary fields. The topics covered in the book are timely and have great impact on data sciences, identifying important directions for future research, promoting advanced statistical methods in big data science, and facilitating future collaborations across disciplines and between theory and practice.

• Author : Dirk P. Kroese,Thomas Taimre,Zdravko I. Botev
• Publisher : John Wiley & Sons
• Pages : 772
• Relase : 2013-06-06
• ISBN : 1118014952
• Rating : 4/5 (411 users)

Handbook of Monte Carlo Methods by Dirk P. Kroese,Thomas Taimre,Zdravko I. Botev Book PDF

A comprehensive overview of Monte Carlo simulation that explores the latest topics, techniques, and real-world applications More and more of today’s numerical problems found in engineering and finance are solved through Monte Carlo methods. The heightened popularity of these methods and their continuing development makes it important for researchers to have a comprehensive understanding of the Monte Carlo approach. Handbook of Monte Carlo Methods provides the theory, algorithms, and applications that helps provide a thorough understanding of the emerging dynamics of this rapidly-growing field. The authors begin with a discussion of fundamentals such as how to generate random numbers on a computer. Subsequent chapters discuss key Monte Carlo topics and methods, including: Random variable and stochastic process generation Markov chain Monte Carlo, featuring key algorithms such as the Metropolis-Hastings method, the Gibbs sampler, and hit-and-run Discrete-event simulation Techniques for the statistical analysis of simulation data including the delta method, steady-state estimation, and kernel density estimation Variance reduction, including importance sampling, latin hypercube sampling, and conditional Monte Carlo Estimation of derivatives and sensitivity analysis Advanced topics including cross-entropy, rare events, kernel density estimation, quasi Monte Carlo, particle systems, and randomized optimization The presented theoretical concepts are illustrated with worked examples that use MATLAB®, a related Web site houses the MATLAB® code, allowing readers to work hands-on with the material and also features the author's own lecture notes on Monte Carlo methods. Detailed appendices provide background material on probability theory, stochastic processes, and mathematical statistics as well as the key optimization concepts and techniques that are relevant to Monte Carlo simulation. Handbook of Monte Carlo Methods is an excellent reference for applied statisticians and practitioners working in the fields of engineering and finance who use or would like to learn how to use Monte Carlo in their research. It is also a suitable supplement for courses on Monte Carlo methods and computational statistics at the upper-undergraduate and graduate levels.

• Author : James E. Gentle
• Publisher : Springer Science & Business Media
• Pages : 420
• Relase : 2006-04-18
• ISBN : 9780387216119
• Rating : 1/5 (1 users)

Elements of Computational Statistics by James E. Gentle Book PDF

Will provide a more elementary introduction to these topics than other books available; Gentle is the author of two other Springer books

• Author : Ricardo A. Maronna,R. Douglas Martin,Victor J. Yohai,Matías Salibián-Barrera
• Publisher : John Wiley & Sons
• Pages : 466
• Relase : 2019-01-04
• ISBN : 9781119214687
• Rating : 5/5 (1 users)

Robust Statistics by Ricardo A. Maronna,R. Douglas Martin,Victor J. Yohai,Matías Salibián-Barrera Book PDF

A new edition of this popular text on robust statistics, thoroughly updated to include new and improved methods and focus on implementation of methodology using the increasingly popular open-source software R. Classical statistics fail to cope well with outliers associated with deviations from standard distributions. Robust statistical methods take into account these deviations when estimating the parameters of parametric models, thus increasing the reliability of fitted models and associated inference. This new, second edition of Robust Statistics: Theory and Methods (with R) presents a broad coverage of the theory of robust statistics that is integrated with computing methods and applications. Updated to include important new research results of the last decade and focus on the use of the popular software package R, it features in-depth coverage of the key methodology, including regression, multivariate analysis, and time series modeling. The book is illustrated throughout by a range of examples and applications that are supported by a companion website featuring data sets and R code that allow the reader to reproduce the examples given in the book. Unlike other books on the market, Robust Statistics: Theory and Methods (with R) offers the most comprehensive, definitive, and up-to-date treatment of the subject. It features chapters on estimating location and scale; measuring robustness; linear regression with fixed and with random predictors; multivariate analysis; generalized linear models; time series; numerical algorithms; and asymptotic theory of M-estimates. Explains both the use and theoretical justification of robust methods Guides readers in selecting and using the most appropriate robust methods for their problems Features computational algorithms for the core methods Robust statistics research results of the last decade included in this 2nd edition include: fast deterministic robust regression, finite-sample robustness, robust regularized regression, robust location and scatter estimation with missing data, robust estimation with independent outliers in variables, and robust mixed linear models. Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is an ideal resource for researchers, practitioners, and graduate students in statistics, engineering, computer science, and physical and social sciences.

• Author : David F. Andrews,James E. H. Stafford
• Publisher : Oxford University Press, USA
• Pages : 184
• Relase : 2000
• ISBN : 0198507054
• Rating : 4/5 (411 users)

Symbolic Computation for Statistical Inference by David F. Andrews,James E. H. Stafford Book PDF

Over recent years, developments in statistical computing have freed statisticians from the burden of calculation and have made possible new methods of analysis that previously would have been too difficult or time-consuming. Up till now these developments have been primarily in numericalcomputation and graphical display, but equal steps forward are now being made in the area of symbolic computing, or in other words the use of computer languages and procedures to manipulate expressions. This allows researchers to compute an algebraic expression, rather than evaluate the expressionnumerically over a given range. This book summarizes a decade of research into the use of symbolic computation applied to statistical inference problems. It shows the considerable potential of the subject to automate statistical calculation, leaving researchers free to concentrate on new concepts.Starting with the development of algorithms applied to standard undergraduate problems, the book then goes on to develop increasingly more powerful tools. Later chapters then discuss the application of these algorithms to different areas of statistical methodology.

• Author : Yuichi Mori
• Publisher : Springer Science & Business Media
• Pages : 1096
• Relase : 2004-07-14
• ISBN : 3540404643
• Rating : 4/5 (411 users)

Handbook of Computational Statistics by Yuichi Mori Book PDF

The Handbook of Computational Statistics: Concepts and Methodology is divided into four parts. It begins with an overview over the field of Computational Statistics. The second part presents several topics in the supporting field of statistical computing. Emphasis is placed on the need of fast and accurate numerical algorithms and it discusses some of the basic methodologies for transformation, data base handling and graphics treatment. The third part focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data. Finally a set of selected applications like Bioinformatics, Medical Imaging, Finance and Network Intrusion Detection highlight the usefulness of computational statistics.

• Author : Ming-Hui Chen,Qi-Man Shao,Joseph G. Ibrahim
• Publisher : Springer Science & Business Media
• Pages : 387
• Relase : 2012-12-06
• ISBN : 9781461212768
• Rating : 4/5 (411 users)

Monte Carlo Methods in Bayesian Computation by Ming-Hui Chen,Qi-Man Shao,Joseph G. Ibrahim Book PDF

Dealing with methods for sampling from posterior distributions and how to compute posterior quantities of interest using Markov chain Monte Carlo (MCMC) samples, this book addresses such topics as improving simulation accuracy, marginal posterior density estimation, estimation of normalizing constants, constrained parameter problems, highest posterior density interval calculations, computation of posterior modes, and posterior computations for proportional hazards models and Dirichlet process models. The authors also discuss model comparisons, including both nested and non-nested models, marginal likelihood methods, ratios of normalizing constants, Bayes factors, the Savage-Dickey density ratio, Stochastic Search Variable Selection, Bayesian Model Averaging, the reverse jump algorithm, and model adequacy using predictive and latent residual approaches. The book presents an equal mixture of theory and applications involving real data, and is intended as a graduate textbook or a reference book for a one-semester course at the advanced masters or Ph.D. level. It will also serve as a useful reference for applied or theoretical researchers as well as practitioners.