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Statistical Methods for Global Health and Epidemiology: Principles, Methods and Applications (ICSA Book Series in Statistics)
by Xinguang Chen Din Ding-Geng ChenThis book examines statistical methods and models used in the fields of global health and epidemiology. It includes methods such as innovative probability sampling, data harmonization and encryption, and advanced descriptive, analytical and monitory methods. Program codes using R are included as well as real data examples. Contemporary global health and epidemiology involves a myriad of medical and health challenges, including inequality of treatment, the HIV/AIDS epidemic and its subsequent control, the flu, cancer, tobacco control, drug use, and environmental pollution. In addition to its vast scales and telescopic perspective; addressing global health concerns often involves examining resource-limited populations with large geographic, socioeconomic diversities. Therefore, advancing global health requires new epidemiological design, new data, and new methods for sampling, data processing, and statistical analysis. This book provides global health researchers with methods that will enable access to and utilization of existing data. Featuring contributions from both epidemiological and biostatistical scholars, this book is a practical resource for researchers, practitioners, and students in solving global health problems in research, education, training, and consultation.
Statistical Methods for Handling Incomplete Data
by Jae Kwang Kim Jun ShaoDue to recent theoretical findings and advances in statistical computing, there has been a rapid development of techniques and applications in the area of missing data analysis. Statistical Methods for Handling Incomplete Data covers the most up-to-date statistical theories and computational methods for analyzing incomplete data. Features Uses the mean score equation as a building block for developing the theory for missing data analysis Provides comprehensive coverage of computational techniques for missing data analysis Presents a rigorous treatment of imputation techniques, including multiple imputation fractional imputation Explores the most recent advances of the propensity score method and estimation techniques for nonignorable missing data Describes a survey sampling application Updated with a new chapter on Data Integration Now includes a chapter on Advanced Topics, including kernel ridge regression imputation and neural network model imputation The book is primarily aimed at researchers and graduate students from statistics, and could be used as a reference by applied researchers with a good quantitative background. It includes many real data examples and simulated examples to help readers understand the methodologies.
Statistical Methods for Healthcare Performance Monitoring (Chapman & Hall/CRC Biostatistics Series #92)
by Alex Bottle Paul AylinHealthcare is important to everyone, yet large variations in its quality have been well documented both between and within many countries. With demand and expenditure rising, it’s more crucial than ever to know how well the healthcare system and all its components – from staff member to regional network – are performing. This requires data, which inevitably differ in form and quality. It also requires statistical methods, the output of which needs to be presented so that it can be understood by whoever needs it to make decisions. Statistical Methods for Healthcare Performance Monitoring covers measuring quality, types of data, risk adjustment, defining good and bad performance, statistical monitoring, presenting the results to different audiences and evaluating the monitoring system itself. Using examples from around the world, it brings all the issues and perspectives together in a largely non-technical way for clinicians, managers and methodologists. Statistical Methods for Healthcare Performance Monitoring is aimed at statisticians and researchers who need to know how to measure and compare performance, health service regulators, health service managers with responsibilities for monitoring performance, and quality improvement scientists, including those involved in clinical audits.
Statistical Methods for Imbalanced Data in Ecological and Biological Studies (SpringerBriefs in Statistics)
by Osamu Komori Shinto EguchiThis book presents a fresh, new approach in that it provides a comprehensive recent review of challenging problems caused by imbalanced data in prediction and classification, and also in that it introduces several of the latest statistical methods of dealing with these problems. The book discusses the property of the imbalance of data from two points of view. The first is quantitative imbalance, meaning that the sample size in one population highly outnumbers that in another population. It includes presence-only data as an extreme case, where the presence of a species is confirmed, whereas the information on its absence is uncertain, which is especially common in ecology in predicting habitat distribution. The second is qualitative imbalance, meaning that the data distribution of one population can be well specified whereas that of the other one shows a highly heterogeneous property. A typical case is the existence of outliers commonly observed in gene expression data, and another is heterogeneous characteristics often observed in a case group in case-control studies. The extension of the logistic regression model, maxent, and AdaBoost for imbalanced data is discussed, providing a new framework for improvement of prediction, classification, and performance of variable selection. Weights functions introduced in the methods play an important role in alleviating the imbalance of data. This book also furnishes a new perspective on these problem and shows some applications of the recently developed statistical methods to real data sets.
Statistical Methods for Materials Science: The Data Science of Microstructure Characterization
by Jeffrey P. Simmons Lawrence F. Drummy Charles A. Bouman Marc De GraefData analytics has become an integral part of materials science. This book provides the practical tools and fundamentals needed for researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to microstructure characterization. It contains valuable guidance on essential topics such as denoising and data modeling. Additionally, the analysis and applications section addresses compressed sensing methods, stochastic models, extreme estimation, and approaches to pattern detection.
Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS (Chapman & Hall/CRC Biostatistics Series)
by Bin Li Qingzhao YuThird-variable effect refers to the effect transmitted by third-variables that intervene in the relationship between an exposure and a response variable. Differentiating between the indirect effect of individual factors from multiple third-variables is a constant problem for modern researchers. Statistical Methods for Mediation, Confounding and Moderation Analysis Using R and SAS introduces general definitions of third-variable effects that are adaptable to all different types of response (categorical or continuous), exposure, or third-variables. Using this method, multiple third- variables of different types can be considered simultaneously, and the indirect effect carried by individual third-variables can be separated from the total effect. Readers of all disciplines familiar with introductory statistics will find this a valuable resource for analysis. Key Features: Parametric and nonparametric method in third variable analysis Multivariate and Multiple third-variable effect analysis Multilevel mediation/confounding analysis Third-variable effect analysis with high-dimensional data Moderation/Interaction effect analysis within the third-variable analysis R packages and SAS macros to implement methods proposed in the book
Statistical Methods for Modeling Human Dynamics: An Interdisciplinary Dialogue (Notre Dame Series on Quantitative Methodology)
by Sy-Minn Chow Emilio Ferrer Fushing HsiehThis interdisciplinary volume features contributions from researchers in the fields of psychology, neuroscience, statistics, computer science, and physics. State-of-the-art techniques and applications used to analyze data obtained from studies in cognition, emotion, and electrophysiology are reviewed along with techniques for modeling in real time and for examining lifespan cognitive changes, for conceptualizing change using item response, nonparametric and hierarchical models, and control theory-inspired techniques for deriving diagnoses in medical and psychotherapeutic settings. The syntax for running the analyses presented in the book is provided on the Psychology Press site. Most of the programs are written in R while others are for Matlab, SAS, Win-BUGS, and DyFA. Readers will appreciate a review of the latest methodological techniques developed in the last few years. Highlights include an examination of: Statistical and mathematical modeling techniques for the analysis of brain imaging such as EEGs, fMRIs, and other neuroscience data Dynamic modeling techniques for intensive repeated measurement data Panel modeling techniques for fewer time points data State-space modeling techniques for psychological data Techniques used to analyze reaction time data. Each chapter features an introductory overview of the techniques needed to understand the chapter, a summary, and numerous examples. Each self-contained chapter can be read on its own and in any order. Divided into three major sections, the book examines techniques for examining within-person derivations in change patterns, intra-individual change, and inter-individual differences in change and interpersonal dynamics. Intended for advanced students and researchers, this book will appeal to those interested in applying state-of-the-art dynamic modeling techniques to the the study of neurological, developmental, cognitive, and social/personality psychology, as well as neuroscience, computer science, and engineering.
Statistical Methods for Organizational Research: Theory and Practice
by Chris DewberryThis clearly written textbook clarifies the concepts underpinning descriptive and inferential statistics in organizational research. Acting as much more than a theoretical reference tool, step-by-step it guides readers through the various key stages of successful data analysis.Covering everything from introductory descriptive statistics to advanced inferential techniques such as ANOVA, multiple and logistic regression and factor analysis, this is one of the most comprehensive textbooks available. Using examples directly relevant to organizational research it includes practical advice on such topics as the size of samples required in research studies, using and interpreting SPSS, and writing up results. In helping readers to develop a sound understanding of statistical methods, rather than focusing on complex formulas and computations, this outstanding textbook is as appropriate for those who wish to refresh their knowledge as those new to the subject area.
Statistical Methods for Pharmaceutical Research Planning
by S. W. BergmanThis book focuses on statistical methods which impinge more or less directly on the decisions that are made during the course of pharmaceutical and agro-chemical research, considering the four decision-making areas.
Statistical Methods for Practice and Research: A Guide to Data Analysis Using SPSS (Response Books)
by Ajai S Gaur Sanjaya S GaurThere is a growing trend these days to use statistical methods to comprehend and explain various situations and phenomena in different disciplines. Managers, social scientists and practicing researchers are increasingly collecting information and applying scientific methods to analyze the data. The ability to use statistical methods and tools becomes a crucial skill for the success of such efforts. This book is designed to assist students, managers, academics and researchers in solving statistical problems using SPSS and to help them understand how they can apply various statistical tools for their own research problems. SPSS is a very powerful and user friendly computer package for data analyses. It can take data from most other file types and generate tables, charts, plots, and descriptive statistics, and conduct complex statistical analyses. After providing a brief overview of SPSS and basic statistical concepts, the book covers: - Descriptive statistics - t-tests, chi-square tests and ANOVA - Correlation analysis - Multiple and logistics regression - Factor analysis and testing scale reliability - Advanced data handling Illustrated with simple, practical problems, and screen shots, this book outlines the steps for solving statistical problems using SPSS. Although the illustrations are based on version 16.0 of SPSS, users of the earlier versions will find the book equally useful and relevant. Written in a reader-friendly, non-technical style, this book will serve as a companion volume to any statistics textbook.
Statistical Methods for Psychology (Psy 613 Qualitative Research And Analysis In Psychology Ser.)
by David C. HowellSTATISTICAL METHODS FOR PSYCHOLOGY surveys the statistical techniques commonly used in the behavioral and social sciences, especially psychology and education. To help students gain a better understanding of the specific statistical hypothesis tests that are covered throughout the text, author David Howell emphasize conceptual understanding. Along with significantly updated discussions of effect size and meta-analysis, this Eighth Edition continues to focus on two key themes that are the cornerstones of this book's success: the importance of looking at the data before beginning a hypothesis test, and the importance of knowing the relationship between the statistical test in use and the theoretical questions being asked by the experiment.
Statistical Methods for QTL Mapping (Chapman & Hall/CRC Computational Biology Series #53)
by Zehua ChenWhile numerous advanced statistical approaches have recently been developed for quantitative trait loci (QTL) mapping, the methods are scattered throughout the literature. Statistical Methods for QTL Mapping brings together many recent statistical techniques that address the data complexity of QTL mapping. After introducing basic genetics topics an
Statistical Methods for Ranking Data
by Mayer Alvo Philip L. H. YuThis book introduces advanced undergraduate, graduate students and practitioners to statistical methods for ranking data. An important aspect of nonparametric statistics is oriented towards the use of ranking data. Rank correlation is defined through the notion of distance functions and the notion of compatibility is introduced to deal with incomplete data. Ranking data are also modeled using a variety of modern tools such as CART, MCMC, EM algorithm and factor analysis. This book deals with statistical methods used for analyzing such data and provides a novel and unifying approach for hypotheses testing. The techniques described in the book are illustrated with examples and the statistical software is provided on the authors' website.
Statistical Methods for Rates and Proportions
by Bruce Levin Joseph L. Fleiss Myunghee Cho Paik"This book is to be recommended as a standard shelf reference . . . and as a 'must' to be read by all who wish to better use and understand data involving dichotomous or dichotomizable measurements."--American Journal of PsychiatryIn the two decades since the second edition of Statistical Methods for Rates and Proportions was published, evolving technologies and new methodologies have significantly changed the way today's statistics are viewed and handled. The explosive development of personal computing and statistical software has facilitated the sophisticated analysis of data, putting capabilities that were once the domain of specialists into the hands of every researcher.The Third Edition of this important text addresses these changes and brings the literature up to date. While the previous edition focused on the use of desktop and handheld calculators, the new edition takes full advantage of modern computing power without losing the elegant simplicity that made the text so popular with students and practitioners alike. In authoritative yet clear terminology, the authors have brought the science of data analysis up to date without compromising its accessibility.Features of the Third Edition include:New material on sample size calculations and issues in clinical trials, and entirely new chapters on single-sample data, logistic regression, Poisson regression, regression models for matched samples, the analysis of correlated binary data, and methods for analyzing fourfold tables with missing dataThe addition of many new problems, both numerical and theoreticalAnswer sections for numerical problems and hints for tackling the theoretical onesA frequentist approach enhanced by the inclusion of empirical Bayesian methodology where appropriateCombining the latest research with the original studies that established the previous editions as leaders in the field, Statistical Methods for Rates and Proportions, Third Edition will continue to be an invaluable resource for students, statisticians, biostatisticians, and epidemiologists.
Statistical Methods for Reliability Data
by William Q. Meeker Luis A. EscobarAmstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Statistical Methods for Reliability Data was among those chosen. Bringing statistical methods for reliability testing in line with the computer age This volume presents state-of-the-art, computer-based statistical methods for reliability data analysis and test planning for industrial products. Statistical Methods for Reliability Data updates and improves established techniques as it demonstrates how to apply the new graphical, numerical, or simulation-based methods to a broad range of models encountered in reliability data analysis. It includes methods for planning reliability studies and analyzing degradation data, simulation methods used to complement large-sample asymptotic theory, general likelihood-based methods of handling arbitrarily censored data and truncated data, and more. In this book, engineers and statisticians in industry and academia will find: A wealth of information and procedures developed to give products a competitive edge Simple examples of data analysis computed with the S-PLUS system-for which a suite of functions and commands is available over the Internet End-of-chapter, real-data exercise sets Hundreds of computer graphics illustrating data, results of analyses, and technical concepts An essential resource for practitioners involved in product reliability and design decisions, Statistical Methods for Reliability Data is also an excellent textbook for on-the-job training courses, and for university courses on applied reliability data analysis at the graduate level. An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon requestfrom the Wiley editorial department.
Statistical Methods for Spatial Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)
by Carol A. Gotway Oliver SchabenbergerUnderstanding spatial statistics requires tools from applied and mathematical statistics, linear model theory, regression, time series, and stochastic processes. It also requires a mindset that focuses on the unique characteristics of spatial data and the development of specialized analytical tools designed explicitly for spatial data analysis. Statistical Methods for Spatial Data Analysis answers the demand for a text that incorporates all of these factors by presenting a balanced exposition that explores both the theoretical foundations of the field of spatial statistics as well as practical methods for the analysis of spatial data. This book is a comprehensive and illustrative treatment of basic statistical theory and methods for spatial data analysis, employing a model-based and frequentist approach that emphasizes the spatial domain. It introduces essential tools and approaches including: measures of autocorrelation and their role in data analysis; the background and theoretical framework supporting random fields; the analysis of mapped spatial point patterns; estimation and modeling of the covariance function and semivariogram; a comprehensive treatment of spatial analysis in the spectral domain; and spatial prediction and kriging. The volume also delivers a thorough analysis of spatial regression, providing a detailed development of linear models with uncorrelated errors, linear models with spatially-correlated errors and generalized linear mixed models for spatial data. It succinctly discusses Bayesian hierarchical models and concludes with reviews on simulating random fields, non-stationary covariance, and spatio-temporal processes.Additional material on the CRC Press website supplements the content of this book. The site provides data sets used as examples in the text, software code that can be used to implement many of the principal methods described and illustrated, and updates to the text itself.
Statistical Methods for Spatial Planning and Monitoring
by Silvestro Montrone Paola PerchinunnoThe book aims to investigate methods and techniques for spatial statistical analysis suitable to model spatial information in support of decision systems. Over the last few years there has been a considerable interest in these tools and in the role they can play in spatial planning and environmental modelling. One of the earliest and most famous definition of spatial planning was "a geographical expression to the economic, social, cultural and ecological policies of society": borrowing from this point of view, this text shows how an interdisciplinary approach is an effective way to an harmonious integration of national policies with regional and local analysis. A wide range of spatial models and techniques is, also, covered: spatial data mining, point processes analysis, nearest neighbor statistics and cluster detection, Fuzzy Regression model and local indicators of spatial association; all of these tools provide the policy-maker with a valuable support to policy development.
Statistical Methods for Spoken Dialogue Management
by Blaise ThomsonSpeech is the most natural mode of communication and yet attempts to build systems which support robust habitable conversations between a human and a machine have so far had only limited success. A key reason is that current systems treat speech input as equivalent to a keyboard or mouse, and behaviour is controlled by predefined scripts that try to anticipate what the user will say and act accordingly. But speech recognisers make many errors and humans are not predictable; the result is systems which are difficult to design and fragile in use. Statistical methods for spoken dialogue management takes a radically different view. It treats dialogue as the problem of inferring a user's intentions based on what is said. The dialogue is modelled as a probabilistic network and the input speech acts are observations that provide evidence for performing Bayesian inference. The result is a system which is much more robust to speech recognition errors and for which a dialogue strategy can be learned automatically using reinforcement learning. The thesis describes both the architecture, the algorithms needed for fast real-time inference over very large networks, model parameter estimation and policy optimisation. This ground-breaking work will be of interest both to practitioners in spoken dialogue systems and to cognitive scientists interested in models of human behaviour.
Statistical Methods for Survival Data Analysis
by John Wenyu Wang Elisa T. LeePraise for the Third Edition". . . an easy-to read introduction to survival analysis which covers the major concepts and techniques of the subject." --Statistics in Medical ResearchUpdated and expanded to reflect the latest developments, Statistical Methods for Survival Data Analysis, Fourth Edition continues to deliver a comprehensive introduction to the most commonly-used methods for analyzing survival data. Authored by a uniquely well-qualified author team, the Fourth Edition is a critically acclaimed guide to statistical methods with applications in clinical trials, epidemiology, areas of business, and the social sciences. The book features many real-world examples to illustrate applications within these various fields, although special consideration is given to the study of survival data in biomedical sciences.Emphasizing the latest research and providing the most up-to-date information regarding software applications in the field, Statistical Methods for Survival Data Analysis, Fourth Edition also includes:Marginal and random effect models for analyzing correlated censored or uncensored dataMultiple types of two-sample and K-sample comparison analysisUpdated treatment of parametric methods for regression model fitting with a new focus on accelerated failure time modelsExpanded coverage of the Cox proportional hazards modelExercises at the end of each chapter to deepen knowledge of the presented materialStatistical Methods for Survival Data Analysis is an ideal text for upper-undergraduate and graduate-level courses on survival data analysis. The book is also an excellent resource for biomedical investigators, statisticians, and epidemiologists, as well as researchers in every field in which the analysis of survival data plays a role.
Statistical Methods for Survival Trial Design: With Applications to Cancer Clinical Trials Using R (Chapman & Hall/CRC Biostatistics Series)
by Jianrong WuStatistical Methods for Survival Trial Design: With Applications to Cancer Clinical Trials Using R provides a thorough presentation of the principles of designing and monitoring cancer clinical trials in which time-to-event is the primary endpoint. Traditional cancer trial designs with time-to-event endpoints are often limited to the exponential model or proportional hazards model. In practice, however, those model assumptions may not be satisfied for long-term survival trials. This book is the first to cover comprehensively the many newly developed methodologies for survival trial design, including trial design under the Weibull survival models; extensions of the sample size calculations under the proportional hazard models; and trial design under mixture cure models, complex survival models, Cox regression models, and competing-risk models. A general sequential procedure based on the sequential conditional probability ratio test is also implemented for survival trial monitoring. All methodologies are presented with sufficient detail for interested researchers or graduate students.
Statistical Methods for the Evaluation of University Systems
by Vincenza Capursi Massimo AttanasioThis book presents a collection of statistical methods and procedures to assess data coming from educational systems. The topics examined include: statistical methods for constructing composite indicators, applied measurements, assessment of educational systems, measurement of the performance of the students at Italian universities, and statistical modeling for questionnaire data. Other issues are the implications of introducing different assessment criteria and procedures to the Italian university system.
Statistical Methods for the Information Professional: A Practical, Painless Approach to Understanding, Using, and Interpreting Statistics (Asist Monograph Ser.)
by Liwen VaughanIn this unique and useful book, the author clearly explains the statistical methods used in information science research, focusing on basic logic rather than mathematical intricacies. Her emphasis is on the meaning of statistics, when and how to apply them, and how to interpret the results of statistical analysis. Through the use of real-world examples, she shows how statistics can be used to improve services, make better decisions, and conduct more effective research. More than 80 helpful figures and tables, seven appendices, bibliography, and an index are included.
Statistical Methods for the Social and Behavioural Sciences: A Model-Based Approach
by David B. FloraStatistical methods in modern research increasingly entail developing, estimating and testing models for data. Rather than rigid methods of data analysis, the need today is for more flexible methods for modelling data. In this logical, easy-to-follow and exceptionally clear book, David Flora provides a comprehensive survey of the major statistical procedures currently used. His innovative model-based approach teaches you how to: Understand and choose the right statistical model to fit your data Match substantive theory and statistical models Apply statistical procedures hands-on, with example data analyses Develop and use graphs to understand data and fit models to data Work with statistical modeling principles using any software package Learn by applying, with input and output files for R, SAS, SPSS, and Mplus. Statistical Methods for the Social and Behavioural Sciences: A Model Based Approach is the essential guide for those looking to extend their understanding of the principles of statistics, and begin using the right statistical modeling method for their own data. It is particularly suited to second or advanced courses in statistical methods across the social and behavioural sciences.
Statistical Methods for the Social and Behavioural Sciences: A Model-Based Approach
by David B. FloraStatistical methods in modern research increasingly entail developing, estimating and testing models for data. Rather than rigid methods of data analysis, the need today is for more flexible methods for modelling data. In this logical, easy-to-follow and exceptionally clear book, David Flora provides a comprehensive survey of the major statistical procedures currently used. His innovative model-based approach teaches you how to: Understand and choose the right statistical model to fit your data Match substantive theory and statistical models Apply statistical procedures hands-on, with example data analyses Develop and use graphs to understand data and fit models to data Work with statistical modeling principles using any software package Learn by applying, with input and output files for R, SAS, SPSS, and Mplus. Statistical Methods for the Social and Behavioural Sciences: A Model Based Approach is the essential guide for those looking to extend their understanding of the principles of statistics, and begin using the right statistical modeling method for their own data. It is particularly suited to second or advanced courses in statistical methods across the social and behavioural sciences.
Statistical Methods for the Social Sciences
by Alan AgrestiStatistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for the Social Sciences introduces statistical methods to students majoring in social science disciplines. With an emphasis on concepts and applications, this book assumes you have no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a two-semester course. The 5th Edition gives you examples and exercises with a variety of “real data.” It includes more illustrations of statistical software for computations and takes advantage of the outstanding applets to explain key concepts, such as sampling distributions and conducting basic data analyses. It continues to downplay mathematics–often a stumbling block for students–while avoiding reliance on an overly simplistic recipe-based approach to statistics.