Bøger i SpringerBriefs in Statistics serien

New developments in the second edition include: Configural Mediation Models, CFA with covariates, moderator CFA, and CFA modeling branches in tree-based methods.

This book focuses on the properties associated with the Dirichlet process, describing its use a priori for nonparametric inference and the Bayes estimate to obtain limits for the estimable parameter.

This book provides statistical methodologies for time series data, focusing on copula-based Markov chain models for serially correlated time series.

This book provides a self-contained introduction to shrinkage estimation for matrix-variate normal distribution models.

This richly illustrated book presents the objectives of, and the latest techniques for, the identifiability analysis and standard and robust regression analysis of complex dynamical models.

This book focuses on the structural analysis of demand under block rate pricing, a type of nonlinear pricing used mainly in public utility services.

This is the first book to present time series analysis using the SAS Enterprise Guide software. It includes some starting background and theory to various time series analysis techniques, and demonstrates the data analysis process and the final results via step-by-step extensive illustrations of the SAS Enterprise Guide software.

This book focuses on all-pairwise multiple comparisons of means in multi-sample models, introducing closed testing procedures based on maximum absolute values of some two-sample t-test statistics and on F-test statistics in homoscedastic multi-sample models.

This book is the modern first treatment of experimental designs, providing a comprehensive introduction to the interrelationship between the theory of optimal designs and the theory of cubature formulas in numerical analysis.

This book introduces readers to statistical methodologies used to analyze doubly truncated data. The first book exclusively dedicated to the topic, it provides likelihood-based methods, Bayesian methods, non-parametric methods, and linear regression methods.

This book introduces readers to advanced statistical methods for analyzing survival data involving correlated endpoints. Hence, the book offers an essential reference guide for medical statisticians and provides researchers with advanced, innovative statistical tools.

This book provides a concise and accessible overview of model averaging, with a focus on applications. Model averaging is a common means of allowing for model uncertainty when analysing data, and has been used in a wide range of application areas, such as ecology, econometrics, meteorology and pharmacology.

This book presents recent results in finite mixtures of skewed distributions to prepare readers to undertake mixture models using scale mixtures of skew normal distributions (SMSN).

This book presents recent non-asymptotic results for approximations in multivariate statistical analysis. It then introduces new areas of research in high-dimensional approximations for bootstrap procedures, Cornish-Fisher expansions, power-divergence statistics and approximations of statistics based on observations with random sample size.

This book deals with problems related to the evaluation of customer satisfaction in very different contexts and ways. Often satisfaction about a product or service is investigated through suitable surveys which try to capture the satisfaction about several partial aspects which characterize the perceived quality of that product or service. This book presents a series of statistical techniques adopted to analyze data from real situations where customer satisfaction surveys were performed.The aim is to give a simple guide of the variety of analysis that can be performed when analyzing data from sample surveys: starting from latent variable models to heterogeneity in satisfaction and also introducing some testing methods for comparing different customers. The book also discusses the construction of composite indicators including different benchmarks of satisfaction. Finally, some rank-based procedures for analyzing survey data are also shown.

This Brief provides a roadmap for the R language and programming environment with signposts to further resources and documentation.

This book introduces multidimensional scaling (MDS) and unfolding as data analysis techniques for applied researchers.

This concise volume covers nonparametric statistics topics that most are most likely to be seen and used from a practical decision support perspective.

This book introduces readers to copula-based statistical methods for analyzing survival data involving dependent censoring.

This book introduces academic researchers and professionals to the basic concepts and methods for characterizing interdependencies of multiple time series in the frequency domain.

This book seeks new perspectives on the growing inequalities that our societies face, putting forward Structured Additive Distributional Regression as a means of statistical analysis that circumvents the common problem of analytical reduction to simple point estimators.

This book provides a general theoretical background for constructing the recursive Bayesian estimation algorithms for mixture models.

This book provides a unified view on a new methodology for Machine Translation (MT). In this book, a detailed presentation of the methodology principles and system architecture is followed by a series of experiments, where the proposed system is compared to other MT systems using a set of established metrics including BLEU, NIST, Meteor and TER.

This book serves as a hands-on guide to the "acs" R package for demographers, planners, and other researchers who work with American Community Survey (ACS) data.

This book discusses the problem of model choice when the statistical models are separate, also called nonnested. Chapter 3 explores the Bayesian approach, the limitations of the classical Bayes factors and the proposed alternative Bayes factors to overcome these limitations.

In particular, it focuses on a truncated exponential family of distributions with a natural parameter and truncation parameter as a typical nonregular family. The emphasis is on presenting new results on the maximum likelihood estimation of a natural parameter or truncation parameter if one of them is a nuisance parameter.

Lastly, the book addresses the applications of asymmetric kernel estimation and testing to various forms of nonnegative economic and financial data. Until recently, the most popularly chosen nonparametric methods used symmetric kernel functions to estimate probability density functions of symmetric distributions with unbounded support.

This book presents a novel approach to time series econometrics, which studies the behavior of nonlinear stochastic processes.

This book serves as a practical guide to methods and statistics in medical research. Brief Guidelines for Methods and Statistics in Medical Research offers a valuable quick reference guide for healthcare students and practitioners conducting research in health related fields, written in an accessible style.