Bøger i SpringerBriefs in Statistics serien

This book provides comprehensive reviews of recent progress in matrix variate and tensor variate data analysis from applied points of view.

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 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 copula-based statistical methods for analyzing survival data involving dependent censoring.

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.

This book analyzes the origins of statistical thinking as well as its related philosophical questions, such as causality, determinism or chance. Despite the mathematical nature of the topic, no statistical background is required, making the book a valuable read for anyone interested in the history of statistics and human cognition.

This is the first book to provide a comprehensive introduction to a new modeling framework known as semiparametric structural equation modeling and its technique, with the fundamental background needed to understand it.

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 integrates the fundamentals of asymptotic theory of statistical inference for time series under nonstandard settings, e.g., infinite variance processes, not only from the point of view of efficiency but also from that of robustness and optimality by minimizing prediction error.

Chapter 2: Estimation in theory and practice, using biologically motivated examples. Chapter 3: Hypothesis testing with emphasis on concepts, particularly type-I , type-II errors, and interpreting test results. Multiple testing is discussed in more depth, and combination of independent tests is explained.

The objective of this book is to compare the statistical properties of penalty and non-penalty estimation strategies for some popular models.

This book tackles the Optimal Non-Linear Experimental Design problem from an applications perspective.

This timely guide explains how to derive the most benefit from automatically mining the vast, and growing, quantities of information in medical databases. It lays out the fruits of the authors' expertise in a condensed, easily consulted format.

Through real-world datasets, this book shows the reader how to work with material in biostatistics using the open source software R.

Software Reliability Modeling

The existence of missing observations is a very important aspect to be considered in the application of survey sampling, for example. Traditionally, simple random sampling is used to select samples. RSS models are developed as counterparts of well-known simple random sampling (SRS) models.

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

The purpose of this book is to illustrate a new statistical approach to test allelic association and genotype-specific effects in the genetic study of diseases.

Hence it is a blend of monograph, textbook, and handbook.It is intended for students and researchers who need quick access to useful formulas appearing in the linear regression model and related matrix theory. This is not a regular textbook - this is supporting material for courses given in linear statistical models.

In statistics, analysis of variance (ANOVA) is a collection of statistical models used to distinguish between an observed variance in a particular variable and its component parts.

Beginning with a brief introduction to linear programming, the book introduces the algebraic representations of conditional independence statements and their applications using linear programming methods.

The first part of this title contained all statistical tests that are relevant for starters on SPSS, and included standard parametric and non-parametric tests for continuous and binary variables, regression methods, trend tests, and reliability and validity assessments of diagnostic tests.

The first part of this title contained all statistical tests relevant to starting clinical investigations, and included tests for continuous and binary data, power, sample size, multiple testing, variability, confounding, interaction, and reliability.

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.

This book will be of interest to readers active in the fields of survival analysis, genetics, ecology, biology, demography, reliability and quality control. Since Sir David Cox's pioneering work in 1972, the proportional hazards model has become the most important model in survival analysis.

This book presents a systematic explanation of the SIML (Separating Information Maximum Likelihood) method, a new approach to financial econometrics. Considerable interest has been given to the estimation problem of integrated volatility and covariance by using high-frequency financial data.

In this book we present new weighted correlation coefficients and new methods of weighted principal component analysis.We also introduce new methods of dimension reduction and clustering for time series data and describe some theoretical results on the weighted correlation coefficients in separate sections.

This book covers Levy processes and their applications in the contexts of reliability and storage. Special attention is paid to life distributions and the maintenance of devices subject to degradation; estimating the parameters of the degradation process is also discussed, as is the maintenance of dams subject to Levy input.

The Weibull distribution has been one of the most cited lifetime distributions in reliability engineering. Over the last decade, many generalizations and extensions of the Weibull have been proposed in order to provide more flexibility than the traditional version when it comes to modeling lifetime data in diverse fields.

Bootstrap and other resampling procedures for dependent sequences such as Markov chains, Markov sequences, linear auto-regressive moving average sequences, block based bootstrap for stationary sequences and other block based procedures are also discussed in some detail.