The Oxford Handbook of Applied Nonparametric and Semiparametric Econometrics and Statistics PDF Download

Author: Jeffrey Racine
Publisher: Oxford University Press
ISBN: 0199857946
Category : Business & Economics
Languages : en
Pages : 562

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Book Description
This volume, edited by Jeffrey Racine, Liangjun Su, and Aman Ullah, contains the latest research on nonparametric and semiparametric econometrics and statistics. Chapters by leading international econometricians and statisticians highlight the interface between econometrics and statistical methods for nonparametric and semiparametric procedures.

Author: Colin G. Miller
Publisher: Springer Science & Business Media
ISBN: 1848827105
Category : Medical
Languages : en
Pages : 422

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Book Description
In the last few years the use of medical imaging has increased exponentially in routine clinical practice. This has been reflected in a rapidly increasing use of medical imaging in clinical trials, through all phases. More recently this has culminated in a number of inter-disciplinary meetings with the various stake holders, including the FDA. Changes in the regulatory process has resulted, when it comes to the submission of data to the FDA, in a therapeutic agent where one or more of the trial end-points is the assessment of a radiological end-point. No longer is it sufficient to have the images read by the local investigator site. The FDA has also identified Medical Imaging as one of the key 6 points in the Critical Path initiative which was launched in 2004. This puts a keen focus on the role of imaging and the need to clearly identify and understand this aspect of clinical trials. As the pharmaceutical, biotech and medical device industry continues to identify ways to improve and speed up product development, medical imaging plays a more significant role. An understanding of the methodology and the metrics is therefore required but difficult to ascertain in one easy to read volume for individuals entering this field. This book will therefore fulfill this void, be it for the pharmaceutical personnel from medical director to monitor, or the Principal Investigator who is having to understand the complexities of the imaging and why it is having to be sent off-site for a 'central read.'

Author: Robert Shumway
Publisher: CRC Press
ISBN: 1000008398
Category : Mathematics
Languages : en
Pages : 218

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Book Description
The goals of this text are to develop the skills and an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing dependent data. A useful feature of the presentation is the inclusion of nontrivial data sets illustrating the richness of potential applications to problems in the biological, physical, and social sciences as well as medicine. The text presents a balanced and comprehensive treatment of both time and frequency domain methods with an emphasis on data analysis. Numerous examples using data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and the analysis of economic and financial problems. The text can be used for a one semester/quarter introductory time series course where the prerequisites are an understanding of linear regression, basic calculus-based probability skills, and math skills at the high school level. All of the numerical examples use the R statistical package without assuming that the reader has previously used the software. Robert H. Shumway is Professor Emeritus of Statistics, University of California, Davis. He is a Fellow of the American Statistical Association and has won the American Statistical Association Award for Outstanding Statistical Application. He is the author of numerous texts and served on editorial boards such as the Journal of Forecasting and the Journal of the American Statistical Association. David S. Stoffer is Professor of Statistics, University of Pittsburgh. He is a Fellow of the American Statistical Association and has won the American Statistical Association Award for Outstanding Statistical Application. He is currently on the editorial boards of the Journal of Forecasting, the Annals of Statistical Mathematics, and the Journal of Time Series Analysis. He served as a Program Director in the Division of Mathematical Sciences at the National Science Foundation and as an Associate Editor for the Journal of the American Statistical Association and the Journal of Business & Economic Statistics.

Author: Richard A. Davis
Publisher: CRC Press
ISBN: 1466577746
Category : Mathematics
Languages : en
Pages : 484

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Book Description
Model a Wide Range of Count Time Series Handbook of Discrete-Valued Time Series presents state-of-the-art methods for modeling time series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the book focuses on time series of counts, some of the techniques discussed ca

Author: John T. Mentzer
Publisher: SAGE
ISBN: 1452238391
Category : Business & Economics
Languages : en
Pages : 369

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Book Description
Incorporating 25 years of sales forecasting management research with more than 400 companies, Sales Forecasting Management, Second Edition is the first text to truly integrate the theory and practice of sales forecasting management. This research includes the personal experiences of John T. Mentzer and Mark A. Moon in advising companies how to improve their sales forecasting management practices. Their program of research includes two major surveys of companies′ sales forecasting practices, a two-year, in-depth study of sales forecasting management practices of 20 major companies, and an ongoing study of how to apply the findings from the two-year study to conducting sales forecasting audits of additional companies. The book provides comprehensive coverage of the techniques and applications of sales forecasting analysis, combined with a managerial focus to give managers and users of the sales forecasting function a clear understanding of the forecasting needs of all business functions. New to This Edition: The author′s well-regarded Multicaster software system demo, previously available on cassette, has been updated and is now available for download from the authors′ Web site New insights on the critical area of qualitative forecasting are presented The results of additional surveys done since the publication of the first edition have been added The discussion of the four dimensions of forecasting management has been significantly enhanced Significant reorganization and updating has been done to strengthen and improve the material for the second edition. Sales Forecasting Management is an ideal text for graduate courses in sales forecasting management. Practitioners in marketing, sales, finance/accounting, production/purchasing, and logistics will also find this easy-to-understand volume essential.

Author: Jose Casals
Publisher: CRC Press
ISBN: 131536025X
Category : Mathematics
Languages : en
Pages : 286

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Book Description
The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, aggregation constraints, or missing in-sample values. Exploring the advantages of this approach, State-Space Methods for Time Series Analysis: Theory, Applications and Software presents many computational procedures that can be applied to a previously specified linear model in state-space form. After discussing the formulation of the state-space model, the book illustrates the flexibility of the state-space representation and covers the main state estimation algorithms: filtering and smoothing. It then shows how to compute the Gaussian likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also discusses signal extraction, describes two algorithms to obtain the VARMAX matrices corresponding to any linear state-space model, and addresses several issues relating to the aggregation and disaggregation of time series. The book concludes with a cross-sectional extension to the classical state-space formulation in order to accommodate longitudinal or panel data. Missing data is a common occurrence here, and the book explains imputation procedures necessary to treat missingness in both exogenous and endogenous variables. Web Resource The authors’ E4 MATLAB® toolbox offers all the computational procedures, administrative and analytical functions, and related materials for time series analysis. This flexible, powerful, and free software tool enables readers to replicate the practical examples in the text and apply the procedures to their own work.

Author: Randal Douc
Publisher: CRC Press
ISBN: 1466502347
Category : Mathematics
Languages : en
Pages : 548

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Book Description
This text emphasizes nonlinear models for a course in time series analysis. After introducing stochastic processes, Markov chains, Poisson processes, and ARMA models, the authors cover functional autoregressive, ARCH, threshold AR, and discrete time series models as well as several complementary approaches. They discuss the main limit theorems for Markov chains, useful inequalities, statistical techniques to infer model parameters, and GLMs. Moving on to HMM models, the book examines filtering and smoothing, parametric and nonparametric inference, advanced particle filtering, and numerical methods for inference.

Author: Jianqing Fan
Publisher: CRC Press
ISBN: 0429527616
Category : Mathematics
Languages : en
Pages : 942

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Book Description
Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.

Author: Christian Francq
Publisher: John Wiley & Sons
ISBN: 1119957397
Category : Mathematics
Languages : en
Pages : 469

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Book Description
This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation and tests. The book also provides coverage of several extensions such as asymmetric and multivariate models and looks at financial applications. Key features: Provides up-to-date coverage of the current research in the probability, statistics and econometric theory of GARCH models. Numerous illustrations and applications to real financial series are provided. Supporting website featuring R codes, Fortran programs and data sets. Presents a large collection of problems and exercises. This authoritative, state-of-the-art reference is ideal for graduate students, researchers and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.

Author: Magnus Larsson
Publisher: Packt Publishing Ltd
ISBN: 1801079153
Category : Computers
Languages : en
Pages : 775

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Book Description
A step-by-step guide to creating and deploying production-quality microservices-based applications Key FeaturesBuild cloud-native production-ready microservices with this comprehensively updated guideUnderstand the challenges of building large-scale microservice architecturesLearn how to get the best out of Spring Cloud, Kubernetes, and Istio in combinationBook Description With this book, you'll learn how to efficiently build and deploy microservices. This new edition has been updated for the most recent versions of Spring, Java, Kubernetes, and Istio, demonstrating faster and simpler handling of Spring Boot, local Kubernetes clusters, and Istio installation. The expanded scope includes native compilation of Spring-based microservices, support for Mac and Windows with WSL2, and an introduction to Helm 3 for packaging and deployment. A revamped security chapter now follows the OAuth 2.1 specification and makes use of the newly launched Spring Authorization Server from the Spring team. Starting with a set of simple cooperating microservices, you'll add persistence and resilience, make your microservices reactive, and document their APIs using OpenAPI. You’ll understand how fundamental design patterns are applied to add important functionality, such as service discovery with Netflix Eureka and edge servers with Spring Cloud Gateway. You’ll learn how to deploy your microservices using Kubernetes and adopt Istio. You'll explore centralized log management using the Elasticsearch, Fluentd, and Kibana (EFK) stack and monitor microservices using Prometheus and Grafana. By the end of this book, you'll be confident in building microservices that are scalable and robust using Spring Boot and Spring Cloud. What you will learnBuild reactive microservices using Spring BootDevelop resilient and scalable microservices using Spring CloudUse OAuth 2.1/OIDC and Spring Security to protect public APIsImplement Docker to bridge the gap between development, testing, and productionDeploy and manage microservices with KubernetesApply Istio for improved security, observability, and traffic managementWrite and run automated microservice tests with JUnit, testcontainers, Gradle, and bashWho this book is for If you are a Java or Spring Boot developer who wants to learn how to build microservice landscapes from scratch, this book is for you. No familiarity with microservices architecture is required.