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Statistical Methods for Mediation Confounding and Moderation Analysis Using R and SAS

Statistical Methods for Mediation Confounding and Moderation Analysis Using R and SAS

Third-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

GBP 130.00
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Cluster Randomised Trials

Cluster Randomised Trials

Cluster Randomised Trials Second Edition discusses the design conduct and analysis of trials that randomise groups of individuals to different treatments. It explores the advantages of cluster randomisation with special attention given to evaluating the effects of interventions against infectious diseases. Avoiding unnecessary mathematical detail the book covers basic concepts underlying the use of cluster randomisation such as direct indirect and total effects. In the time since the publication of the first edition the use of cluster randomised trials (CRTs) has increased substantially which is reflected in the updates to this edition. There are greatly expanded sections on randomisation sample size estimation and alternative designs including new material on stepped wedge designs. There is a new section on handling ordinal outcome data and an appendix with descriptions and/or generating code of the example data sets. Although the book mainly focuses on medical and public health applications it shows that the rigorous evidence of intervention effects provided by CRTs has the potential to inform public policy in a wide range of other areas. The book encourages readers to apply the methods to their own trials reproduce the analyses presented and explore alternative approaches.

GBP 48.99
1

Hidden Markov Models for Time Series An Introduction Using R Second Edition

Hidden Markov Models for Time Series An Introduction Using R Second Edition

Hidden Markov Models for Time Series: An Introduction Using R Second Edition illustrates the great flexibility of hidden Markov models (HMMs) as general-purpose models for time series data. The book provides a broad understanding of the models and their uses. After presenting the basic model formulation the book covers estimation forecasting decoding prediction model selection and Bayesian inference for HMMs. Through examples and applications the authors describe how to extend and generalize the basic model so that it can be applied in a rich variety of situations. The book demonstrates how HMMs can be applied to a wide range of types of time series: continuous-valued circular multivariate binary bounded and unbounded counts and categorical observations. It also discusses how to employ the freely available computing environment R to carry out the computations. FeaturesPresents an accessible overview of HMMsExplores a variety of applications in ecology finance epidemiology climatology and sociologyIncludes numerous theoretical and programming exercisesProvides most of the analysed data sets onlineNew to the second editionA total of five chapters on extensions including HMMs for longitudinal data hidden semi-Markov models and models with continuous-valued state processNew case studies on animal movement rainfall occurrence and capture-recapture data | Hidden Markov Models for Time Series An Introduction Using R Second Edition

GBP 44.99
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Sampling Design and Analysis

Sampling Design and Analysis

The level is appropriate for an upper-level undergraduate or graduate-level statistics major. Sampling: Design and Analysis (SDA) will also benefit a non-statistics major with a desire to understand the concepts of sampling from a finite population. A student with patience to delve into the rigor of survey statistics will gain even more from the content that SDA offers. The updates to SDA have potential to enrich traditional survey sampling classes at both the undergraduate and graduate levels. The new discussions of low response rates non-probability surveys and internet as a data collection mode hold particular value as these statistical issues have become increasingly important in survey practice in recent years… I would eagerly adopt the new edition of SDA as the required textbook. (Emily Berg Iowa State University) What is the unemployment rate? What is the total area of land planted with soybeans? How many persons have antibodies to the virus causing COVID-19? Sampling: Design and Analysis Third Edition shows you how to design and analyze surveys to answer these and other questions. This authoritative text used as a standard reference by numerous survey organizations teaches the principles of sampling with examples from social sciences public opinion research public health business agriculture and ecology. Readers should be familiar with concepts from an introductory statistics class including probability and linear regression; optional sections contain statistical theory for readers familiar with mathematical statistics. Key Features: Has been thoroughly revised to incorporate recent research and applications. Includes a new chapter on nonprobability samples and more than 200 new examples and exercises have been added. Teaches the principles of sampling with examples from social sciences public opinion research public health business agriculture and ecology. SDA’s companion website contains data sets computer code and links to two free downloadable supplementary books (also available in paperback) that provide step-by-step guides—with code annotated output and helpful tips—for working through the SDA examples. Instructors can use either R or SAS® software. SAS® Software Companion for Sampling: Design and Analysis Third Edition by Sharon L. Lohr (2022 CRC Press) R Companion for Sampling: Design and Analysis Third Edition by Yan Lu and Sharon L. Lohr (2022 CRC Press) | Sampling Design and Analysis

GBP 66.99
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