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Algebra of Vectors and Matrices. Probability Theory, Tools and Techniques. Continuous Probability Models. The Theory of Least Squares and Analysis of Variance. Criteria and Methods of Estimation. Large Sample Theory and ...

Materias / palabras clave: Macromolecule; Protein Data Bank (RCSB PDB); Protein Data Bank; Biological system; Macromolecular Substances; Chemistry; Protein function; Inference; Computer science; Chemical physics; Statistical physics; Data mining

The Gibbs sampler, the algorithm of Metropolis and similar iterative simulation methods are potentially very helpful for summarizing multivariate distributions. Used naively, however, iterative simulation can give mislea...

Preface.PART I: OVERVIEW AND BASIC APPROACHES.Introduction.Missing Data in Experiments.Complete-Case and Available-Case Analysis, Including Weighting Methods.Single Imputation Methods.Estimation of Imputation Uncertainty...

Preface.Preface to the Second Edition.Preface to the First Edition.1. An Introduction to Applied Probability.2. Statistical Inference for a Single Proportion.3. Assessing Significance in a Fourfold Table.4. Determining S...

CHAPTER 1: Introduction to Design CHAPTER 2: Principles of Estimation and Inference: Means and Variance CHAPTER 3: Design and Analysis of Single-Factor Experiments: Completely Randomized Design CHAPTER 4: Single-Factor E...

Materias / palabras clave: Statistical inference; Inference; Statistical learning; Computer science; Machine learning; Artificial intelligence; Data mining; Statistics; Mathematics

Covers supervised learning (prediction) to unsupervised learning. This book contains topics including neural networks, support vector machines, classification trees and boosting

Information theory provides a constructive criterion for setting up probability distributions on the basis of partial knowledge, and leads to a type of statistical inference which is called the maximum-entropy estimate. ...

Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-speci...

Introduction * Information and Likelihood Theory: A Basis for Model Selection and Inference * Basic Use of the Information-Theoretic Approach * Formal Inference From More Than One Model: Multi-Model Inference (MMI) * Mon...

The model selection literature has been generally poor at reflecting the deep foundations of the Akaike information criterion (AIC) and at making appropriate comparisons to the Bayesian information criterion (BIC). There...

Donoho and Johnstone (1994) proposed a method for reconstructing an unknown function f on [0,1] from noisy data d/sub i/=f(t/sub i/)+/spl sigma/z/sub i/, i=0, ..., n-1,t/sub i/=i/n, where the z/sub i/ are independent and...

Probability Theory. Statistical Inference. Some Tests Based on the Binomial Distribution. Contingency Tables. Some Methods Based on Ranks. Statistics of the Kolmogorov-Smirnov Type. References. Appendix Tables. Answers t...

Statistical guidelines and expert statements are now available to assist in the analysis and reporting of studies in some biomedical disciplines. We present here a more progressive resource for sample-based studies, meta...

Summary 1. Here, I present a new, multifunctional phylogenetics package, phytools, for the R statistical computing environment. 2. The focus of the package is on methods for phylogenetic comparative biology; however, it ...

(NOTE: Each chapter begins with an Introduction, and concludes with Exercises and References.) I. GETTING STARTED. 1. Aspects of Multivariate Analysis. Applications of Multivariate Techniques. The Organization of Data. D...

Materias / palabras clave: Generalized linear mixed model; Inference; Statistical inference; Computer science; Ecology; Evolutionary ecology; Econometrics; Mathematics; Statistics; Machine learning; Artificial intelligence; Biology

Bayesian Data Analysis describes how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Using examples largely from the authors' own experiences, the book focuses on modern computat...

With its theoretical basis firmly established in molecular evolutionary and population genetics, the comparative DNA and protein sequence analysis plays a central role in reconstructing the evolutionary histories of spec...

Abstract With explanatory covariates, the standard analysis for competing risks data involves modeling the cause-specific hazard functions via a proportional hazards assumption. Unfortunately, the cause-specific hazard f...

Both in science and in practical affairs we reason by combining facts only inconclusively supported by evidence. Building on an abstract understanding of this process of combination, this book constructs a new theory of ...