While preserving the clear, accessible style of previous editions, Applied Nonparametric Statistical Methods, Fourth Edition reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets.
Modern Statistical Methodology and Software for Analyzing Spatial Point PatternsSpatial Point Patterns: Methodology and Applications with R shows scientific researchers and applied statisticians from a wide range of fields how to analyze their spatial point pattern data.
Due to global competition, safety regulations, and other factors, manufacturers are increasingly pressed to create products that are safe, highly reliable, and of high quality.
An important first step in studying the demography of wild animals is to identify the animals uniquely through applying markings, such as rings, tags, and bands.
Bayesian methods in reliability cannot be fully utilized and understood without full comprehension of the essential differences that exist between frequentist probability and subjective probability.
Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling.
Due to increasing demands for dimensionality reduction, research on feature selection has deeply and widely expanded into many fields, including computational statistics, pattern recognition, machine learning, data mining, and knowledge discovery.
Focusing on group sequential procedures, summarizes the sequential statistical methods used in anticancer, antiviral, cardiovascular, and gastrointestinal drug research and screening.
While many financial engineering books are available, the statistical aspects behind the implementation of stochastic models used in the field are often overlooked or restricted to a few well-known cases.
Now viewed as its own scientific discipline, clinical trial methodology encompasses the methods required for the protection of participants in a clinical trial and the methods necessary to provide a valid inference about the objective of the trial.
Providing a basic tool for studying nonlinear problems, Spectral Theory for Random and Nonautonomous Parabolic Equations and Applications focuses on the principal spectral theory for general time-dependent and random parabolic equations and systems.
The Most Comprehensive Book on the SubjectChronicles the Development of the Weibull Distribution in Statistical Theory and Applied StatisticsExploring one of the most important distributions in statistics, The Weibull Distribution: A Handbook focuses on its origin, statistical properties, and related distributions.
Bayesian analyses have made important inroads in modern clinical research due, in part, to the incorporation of the traditional tools of noninformative priors as well as the modern innovations of adaptive randomization and predictive power.
This remarkable text raises the analysis of data in health sciences and policy to new heights of refinement and applicability by introducing cutting-edge meta-analysis strategies while reviewing more commonly used techniques.
Featuring contributions from leading international academics and practitioners, Credit Risk: Models, Derivatives, and Management illustrates how a risk management system can be implemented through an understanding of portfolio credit risks, a set of suitable models, and the derivation of reliable empirical results.
Experimental design is often overlooked in the literature of applied and mathematical statistics: statistics is taught and understood as merely a collection of methods for analyzing data.
In the view of many probabilists, author Anatolii Puhalskii's research results stand among the most significant achievements in the modern theory of large deviations.
"e;Provides well-integrated, comprehensive coverage of all the major statistical designs and methods used for animal studies in pharmaceutical research and development.
Exploring the intersection of ethics and statistics, this comprehensive guide illustrates the proper use of probabilistic and statistical reasoning in the behavioral, social, and biomedical sciences.
Despite the unobserved components model (UCM) having many advantages over more popular forecasting techniques based on regression analysis, exponential smoothing, and ARIMA, the UCM is not well known among practitioners outside the academic community.
An introductory text for students taking a first course in statistics-in fields as diverse as engineering, business, chemistry, and biology-Essential Statistics: Fourth Edition thoroughly updates and enhances the hugely successful third edition.
Emphasizing the role of good statistical practices (GSP) in drug research and formulation, this book outlines important statistics applications for each stage of pharmaceutical development to ensure the valid design, analysis, and assessment of drug products under investigation and establish the safety and efficacy of pharmaceutical compounds.
An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks.
Adaptive Designs for Sequential Treatment Allocation presents a rigorous theoretical treatment of the results and mathematical foundation of adaptive design theory.
Probability Methods for Cost Uncertainty Analysis: A Systems Engineering Perspective, Second Edition gives you a thorough grounding in the analytical methods needed for modeling and measuring uncertainty in the cost of engineering systems.
Robust Methods for Data Reduction gives a non-technical overview of robust data reduction techniques, encouraging the use of these important and useful methods in practical applications.
Unexpected Expectations: The Curiosities of a Mathematical Crystal Ball explores how paradoxical challenges involving mathematical expectation often necessitate a reexamination of basic premises.
Taking into account the standards of the Basel Accord, Operational Risk Modelling and Management presents a simulation model for generating the loss distribution of operational risk.
Easily Use SAS to Produce Your GraphicsDiagrams, plots, and other types of graphics are indispensable components in nearly all phases of statistical analysis, from the initial assessment of the data to the selection of appropriate statistical models to the diagnosis of the chosen models once they have been fitted to the data.
