Die folgenden Links führen aus den jeweiligen lokalen Bibliotheken zum Volltext:
Alternativ können Sie versuchen, selbst über Ihren lokalen Bibliothekskatalog auf das gewünschte Dokument zuzugreifen.
Bei Zugriffsproblemen kontaktieren Sie uns gern.
In: Chapman & Hall/CRC biostatistics series
Basic concepts -- Overall estimation of health disparities -- Domain-specific estimates -- Causality, moderation and mediation -- Machine learning based approaches to disparity estimation -- Health disparity estimation under a precision medicine paradigm -- Extended topics.
In: Chapman & Hall/CRC statistics in the social and behavioral sciences
Modern Applied Regressions creates an intricate and colorful mural with mosaics of categorical and limited response variable (CLRV) models using both Bayesian and Frequentist approaches. Written for graduate students, junior researchers, and quantitative analysts in behavioral, health, and social sciences, this text provides details for doing Bayesian and frequentist data analysis of CLRV models. Each chapter can be read and studied separately with R coding snippets and template interpretation for easy replication. Along with the doing part, the text provides basic and accessible statistical theories behind these models and uses a narrative style to recount their origins and evolution. This book first scaffolds both Bayesian and frequentist paradigms for regression analysis, and then moves onto different types of categorical and limited response variable models, including binary, ordered, multinomial, count, and survival regression. Each of the middle four chapters discusses a major type of CLRV regression that subsumes an array of important variants and extensions. The discussion of all major types usually begins with the history and evolution of the prototypical model, followed by the formulation of basic statistical properties and an elaboration on the doing part of the model and its extension. The doing part typically includes R codes, results, and their interpretation. The last chapter discusses advanced modeling and predictive techniques--multilevel modeling, causal inference and propensity score analysis, and machine learning--that are largely built with the toolkits designed for the CLRV models previously covered. The online resources for this book, including R and Stan codes and supplementarynotes, can be accessed at https://sites.google.com/site/socjunxu/home/statistics/modernapplied-regressions.
This book harbors an updated and standard material on the various aspects of Econometrics. It covers both fundamental and applied aspects and is intended to serve as a basis for a course in Econometrics and attempts at satisfying a need of postgraduate and doctoral students of Economics. It is hoped that, this book will also be worthwhile to teachers, researchers, professionals etc. Note: T& F does not sell or distribute the Hardback in India, Pakistan, Nepal, Bhutan, Bangladesh and Sri Lanka
A one-of-a-kind resource on identifying and dealing with bias in statistical research on causal effects. Do cell phones cause cancer? Can a new curriculum increase student achievement? Determining what the real causes of such problems are, and how powerful their effects may be, are central issues in research across various fields of study. Some researchers are highly skeptical of drawing causal conclusions except in tightly controlled randomized experiments, while others discount the threats posed by different sources of bias, even in less rigorous observational studies. Bias and Causation pre
A one-of-a-kind compilation of modern statistical methods designed to support and advance research across the social sciences. Statistics in the Social Sciences: Current Methodological Developments presents new and exciting statistical methodologies to help advance research and data analysis across the many disciplines in the social sciences. Quantitative methods in various subfields, from psychology to economics, are under demand for constant development and refinement. This volume features invited overview papers, as well as original research presented at the Sixth Annual Winemiller Conferen
chapter 1 Geometric data analysis -- chapter 2 Correspondence analysis -- chapter 3 Multiple correspondence analysis -- chapter 4 Passive and supplementary points, supplementary variables and structured data analysis -- chapter 5 MCA and ascending hierarchical cluster analysis -- chapter 6 Constructing spaces -- chapter 7 Analyzing sub-groups: class-specific MCA.
In: Data Analytics Applications Ser
Cover; Half Title; Series Page; Title Page; Copyright Page; Table of Contents; Preface; Editors; Contributors; 1: Smartphone Technology Integrated with Machine Learning for Airport Pavement Condition Assessment; 1.1 Introduction; 1.2 Smartphone-Driven Assessment of Airport Pavement Condition; 1.2.1 Description of Smartphone Application; 1.2.2 Smartphone Characteristics; 1.3 Case Study of Missouri Airports; 1.3.1 Calibration Study; 1.3.2 Missouri Airport Smartphone Data Collection Methodology; 1.3.3 Missouri Airport Smartphone Data Collection Results for Each Airport; 1.3.4 Discussion.
Good graphs make complex problems clear. From the weather forecast to the Dow Jones average, graphs are so ubiquitous today that it is hard to imagine a world without them. Yet they are a modern invention. This book is the first to comprehensively plot humankind's fascinating efforts to visualize data, from a key seventeenth-century precursor--England's plague-driven initiative to register vital statistics--right up to the latest advances. In a highly readable, richly illustrated story of invention and inventor that mixes science and politics, intrigue and scandal, revolution and shopping
Panel Data Econometrics with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemiology, this book presents classic methodology and applications as well as more advanced topics and recent developments in this field including error component models, spatial panels and dynamic models. They have developed the software programming in R and host replicable material on the book's accompanying website
"This book explores social mechanisms that drive network change and link them to computationally sound models of changing structure to detect patterns. This text identifies the social processes generating these networks and how networks have evolved"--
"Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control is a graduate-level introduction to the principles, algorithms, and practical aspects of stochastic optimization, including applications drawn from engineering, statistics, and computer science. The treatment is both rigorous and broadly accessible, distinguishing this text from much of the current literature and providing students, researchers, and practitioners with a strong foundation for the often-daunting task of solving real-world problems." "The book includes over 130 examples, Web links to software and data sets, more than 250 exercises for the reader, and an extensive list of references. These features help make the text an invaluable resource for those interested in the theory or practice of stochastic search and optimization."--Jacket
Getting started -- PCA with more than two variables -- Scaling of data -- Inferential procedures -- Putting it all together -- hearing loss I -- Operations with group data -- Vector interpretation I: simplifications and inferential techniques -- Vector interpretation II: rotation -- A case history-hearing loss II -- Singular value decomposition: multidimensional scaling I -- Distance models: multidimensional scaling II -- Linear models I: regression; PCA of predictor Variables -- Linear models II: analysis of variance; PCA of response variables -- Other applications of PCA -- Flatland: special procedures for two dimensions -- Odds and ends -- What is factor analysis anyhow? -- Other competitors.
In: Chapman and Hall/CRC financial mathematics series
chapter 1 Modeling of Equity-linked Insurance -- chapter 2 Elementary Stochastic Calculus -- chapter 3 Monte Carlo Simulations of Investment Guarantees -- chapter 4 Pricing and Valuation -- chapter 5 Risk Management - Reserving and Capital Requirement -- chapter 6 Risk Management - Dynamic Hedging -- chapter 7 Advanced ComputationalMethods.