General

Although the basic statistical theory behind modern genetics is not very difficult, most statistical genetics papers are not easy to read for beginners in the field, and formulae quickly become very tedious to fit a particular area of application. ...

Galton used quantiles more than a hundred years ago in describing data. Tukey and Parzen used them in the 60s and 70s in describing populations. Since then, the authors of many papers, both theoretical and practical, have used various aspects of...

Complex stochastic systems comprises a vast area of research, from modelling specific applications to model fitting, estimation procedures, and computing issues. The exponential growth in computing power over the last two decades has revolutionized...

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. Switching from the frequentist to the subjective...

"Provides current models, tools, and examples for the formulation and evaluation of scientific hypotheses in causal terms. Introduces a new method of model parametritization. Illustrates structural equations and graphical elements for complex causal...

Over the last decade, statisticians have developed new statistical tools in the field of spatial point processes. At the same time, observational efforts have yielded a huge amount of new cosmological data to analyze. Although the main tools in astronomy...

In spite of the growing importance of Species Sensitivity Distribution models (SSDs) in ecological risk assessments, the conceptual basis, strengths, and weaknesses of using them have not been comprehensively reviewed. This book fills that need. Written...

Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory,...

In this monograph the narrow topology on random probability measures on Polish spaces is investigated in a thorough and comprehensive way. As a special feature, no additional assumptions on the probability space in the background, such as completeness or...

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...

Showcasing a discussion of the experimental process and a review of basic statistics, this volume provides methodologies to identify general data distribution, skewness, and outliers. It features a unique classification of the nonparametric analogs of...

Balancing the "cookbook" approach of some texts with the more mathematical approach of others, Nonparametric Statistical Methods for Complete and Censored Data introduces commonly used non-parametric methods for complete data and extends those...

A technically precise yet clear presentation of modern sequential methodologies having immediate applications to practical problems in the real world, Applied Sequential Methodologies communicates invaluable techniques for data mining, agricultural...

Statistical analyses of the numbers, lengths, and orientations of fission tracks etched in minerals yield dating and thermal history information valuable in geological and geoscience applications, particularly in oil exploration. Fission tracks can be...

Considered highly exotic tools as recently as the late 1990s, microarrays are now ubiquitous in biological research. Traditional statistical approaches to design and analysis were not developed to handle the high-dimensional, small sample problems posed...

System state estimation in the presence of noise is critical for control systems, signal processing, and many other applications in a variety of fields. Developed decades ago, the Kalman filter remains an important, powerful tool for estimating the...

Statistical distributions are one of the most important applied mathematical tools across a wide spectrum of disciplines, including engineering, biological sciences, and health and social sciences. Since they are used to model observed data and...

A clear, comprehensive treatment of the subject, Environmental Statistics with S-PLUS surveys the vast array of statistical methods used to collect and analyze environmental data. The book explains what these methods are, how to use them, and where to...

This gracefully organized text reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, figures, tables, and computer simulations to develop and illustrate concepts. Drills and...

Quantification of categorical, or non-numerical, data is a problem that scientists face across a wide range of disciplines. Exploring data analysis in various areas of research, such as the social sciences and biology, Multidimensional Nonlinear...

Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling...

To document the world's diversity of species and reconstruct the tree of life we need to undertake some simple but mountainous tasks. Most importantly, we need to tackle species rich groups. We need to collect, name, and classify them, and then position...

Useful in the theoretical and empirical analysis of nonlinear time series data, semiparametric methods have received extensive attention in the economics and statistics communities over the past twenty years. Recent studies show that semiparametric...

Data Analysis of Asymmetric Structures provides a comprehensive presentation of a variety of models and theories for the analysis of asymmetry and its applications and provides a wealth of new approaches in every section. It meets both the practical and...

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. Presenting an...

Sound forest management planning requires cost-efficient approaches to optimally utilize given resources. Emphasizing the mathematical and statistical features of forest sampling to assess classical dendrometrical quantities, Sampling Techniques for...

Illustrating a simple, novel method for solving an array of statistical problems, Observed Confidence Levels: Theory and Application describes the basic development of observed confidence levels, a methodology that can be applied to a variety of common...

Continuing advances in biomedical research and statistical methods call for a constant stream of updated, cohesive accounts of new developments so that the methodologies can be properly implemented in the biomedical field. Responding to this need,...

Surfaces are a central to geographical analysis. Their generation and manipulation are a key component of geographical information systems (GISs). However, geographical surface data is often not precise. When surfaces are used to model geographical...

Providing reliable information on an intervention effect, meta-analysis is a powerful statistical tool for analyzing and combining results from individual studies. Meta-Analysis of Binary Data Using Profile Likelihood focuses on the analysis and modeling...

Providing an interface between dry-bench bioinformaticians and wet-lab biologists, DNA Methylation Microarrays: Experimental Design and Statistical Analysis presents the statistical methods and tools to analyze high-throughput epigenomic data, in...

Unlike other books on the modeling and analysis of experimental data, Design and Analysis of Experiments: Classical and Regression Approaches with SAS not only covers classical experimental design theory, it also explores regression approaches...

Due to the tremendous accumulation of data for genetic markers, pharmacogenomics, the study of the functions and interactions of all genes in the overall variability of drug response, is one of the hottest areas of research in biomedical science...

Interactively Run Simulations and Experiment with Real or Simulated Data to Make Sequential Analysis Come Alive Taking an accessible, nonmathematical approach to this field, Sequential Methods and Their Applications illustrates the efficiency of...

Large observational studies involving research questions that require the measurement of several features on each individual arise in many fields including the social and medical sciences. This book sets out both the general concepts and the more...

Multivariate, heterogeneous data has been traditionally analyzed using the "one at a time" variable approach, often missing the main objective of discovering the relationships among multiple variables and samples. Enter chemometrics, with its...

Describes the State-of-the-Art in Spatial Data Mining, Focuses on Data Quality Substantial progress has been made toward developing effective techniques for spatial information processing in recent years. This science deals with models of reality in a...

Examines Critical Decisions for Transitioning Lab Science to a Clinical Setting The development of therapeutic pharmaceutical compounds is becoming more expensive, and the success rates for getting such treatments approved for marketing and to the...

A Cohesive Approach to Regression Models Confidence Intervals in Generalized Regression Models introduces a unified representation-the generalized regression model (GRM)-of various types of regression models. It also uses a likelihood-based approach for...

Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects...