Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This richly illustrated book provides an overview of the design and analysis of experiments with a focus on non-clinical experiments in the life sciences, including animal research. It covers the most common aspects of experimental design such as handling multiple treatment factors and improving precision. In addition, it addresses experiments with large numbers of treatment factors and response surface methods for optimizing experimental conditions or biotechnological yields.The book emphasizes the estimation of effect sizes and the principled use of statistical arguments in the broader scientific context. It gradually transitions from classical analysis of variance to modern linear mixed models, and provides detailed information on power analysis and sample size determination, including ‘portable power’ formulas for making quick approximate calculations. In turn, detailed discussions of several real-life examples illustrate the complexities and aberrations that can arise in practice.Chiefly intended for students, teachers and researchers in the fields of experimental biology and biomedicine, the book is largely self-contained and starts with the necessary background on basic statistical concepts. The underlying ideas and necessary mathematics are gradually introduced in increasingly complex variants of a single example. Hasse diagrams serve as a powerful method for visualizing and comparing experimental designs and deriving appropriate models for their analysis. Manual calculations are provided for early examples, allowing the reader to follow the analyses in detail. More complex calculations rely on the statistical software R, but are easily transferable to other software.Though there are few prerequisites for effectively using the book, previous exposure to basic statistical ideas and the software R would be advisable.

Experimental design. --- Design of experiments --- Statistical design --- Mathematical optimization --- Research --- Science --- Statistical decision --- Statistics --- Analysis of means --- Analysis of variance --- Experiments --- Methodology --- Disseny d'experiments --- Assaig (Estadística) --- Disseny experimental --- Dissenys estadístics --- Esquema experimental (Estadística) --- Experimentació (Estadística) --- Anàlisi de variància

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Experiments are a central methodology in the social sciences. Scholars from every discipline regularly turn to experiments. Practitioners rely on experimental evidence in evaluating social programs, policies, and institutions. This book is about how to "think" about experiments. It argues that designing a good experiment is a slow moving process (given the host of considerations) which is counter to the current fast moving temptations available in the social sciences. The book includes discussion of the place of experiments in the social science process, the assumptions underlying different types of experiments, the validity of experiments, the application of different designs, how to arrive at experimental questions, the role of replications in experimental research, and the steps involved in designing and conducting "good" experiments. The goal is to ensure social science research remains driven by important substantive questions and fully exploits the potential of experiments in a thoughtful manner.

Social sciences --- Experimental design. --- Experiments. --- Research. --- Mathematical optimization --- Research --- Science --- Statistical decision --- Statistics --- Analysis of means --- Analysis of variance --- Design of experiments --- Statistical design --- Social science research --- Experiments --- Methodology

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book presents a general method for deriving higher-order statistics of multivariate distributions with simple algorithms that allow for actual calculations. Multivariate nonlinear statistical models require the study of higher-order moments and cumulants. The main tool used for the definitions is the tensor derivative, leading to several useful expressions concerning Hermite polynomials, moments, cumulants, skewness, and kurtosis. A general test of multivariate skewness and kurtosis is obtained from this treatment. Exercises are provided for each chapter to help the readers understand the methods. Lastly, the book includes a comprehensive list of references, equipping readers to explore further on their own.

Anàlisi multivariable --- Anàlisi multivariant --- Estadística matemàtica --- Matrius (Matemàtica) --- Anàlisi de conglomerats --- Anàlisi de correspondències (Estadística) --- Anàlisi discriminant --- Modelització multiescala --- Models d'equacions estructurals --- Anàlisi conjunt (Màrqueting) --- Multivariate analysis. --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Mathematical statistics --- Matrices --- Statistics. --- Statistical Theory and Methods. --- Statistics and Computing. --- Data processing. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

"Le livre en deux tomes (1500 pages) de Laurent Le Floch et Frédéric Testard couvre le programme de probabilités du lycée, de licence et des préparations aux concours de recrutement d'enseignants. Il fournira en outre une solide base pour les étudiants suivant des masters intégrant une branche probabiliste. Dans le premier tome, la démarche "en spirale" adoptée par les auteurs les conduit a développer les cadres successifs (hasard fini, discret, continu) en introduisant des outils ad hoc, regroupés à la fin de chaque grande partie. Ce n'est que dans le second tome que l'introduction des concepts relevant de l'intégration de Lebesgue les conduit aux énoncés abstraits de la théorie "moderne". Tout au long de l'ouvrage, de très nombreux exercices (plus de 700 au total) permettent aux lecteurs, grâce à des énoncés très détaillés, d'approfondir leur compréhension des notions rencontrées. L'aspect informatique est évidemment présent, et de nombreux exercices permettent ainsi de s'aguerrir à la pratique de la simulation d'expériences aléatoires, en langage Python en général"

Probabilities --- Lebesgue integral --- Analysis of variance --- Law of large numbers --- Random walks (Mathematics) --- Markov processes --- Probabilités. --- Lebesgue, Intégrale de. --- Analyse de variance. --- Hasard. --- Modèles relationnels probabilistes. --- Théorème de la limite centrale. --- Loi des grands nombres. --- Marches aléatoires (mathématiques) --- Martingales (mathématiques) --- Markov, Processus de.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than knowledge and experience. This textbook approaches the essence of sparse estimation by considering math problems and building R programs. Each chapter introduces the notion of sparsity and provides procedures followed by mathematical derivations and source programs with examples of execution. To maximize readers’ insights into sparsity, mathematical proofs are presented for almost all propositions, and programs are described without depending on any packages. The book is carefully organized to provide the solutions to the exercises in each chapter so that readers can solve the total of 100 exercises by simply following the contents of each chapter. This textbook is suitable for an undergraduate or graduate course consisting of about 15 lectures (90 mins each). Written in an easy-to-follow and self-contained style, this book will also be perfect material for independent learning by data scientists, machine learning engineers, and researchers interested in linear regression, generalized linear lasso, group lasso, fused lasso, graphical models, matrix decomposition, and multivariate analysis.

Artificial intelligence. --- Machine learning. --- Data structures (Computer science). --- Statistics . --- Artificial Intelligence. --- Machine Learning. --- Data Structures and Information Theory. --- Statistics, general. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- Electronic data processing --- File organization (Computer science) --- Abstract data types (Computer science) --- Learning, Machine --- Artificial intelligence --- Machine theory --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Multivariate analysis. --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Mathematical statistics --- Matrices