Semiparametric Regression (Cambridge Series in Statistical and Probabilistic Mathematics)

Empirical Processes in M-Estimation (Cam

📁 Empirical Processes in M-Estimation (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 6)

✍ Sara A. van de Geer 📂 Library 📅 2009 🏛 Cambridge University Press 🌐 English

The theory of empirical processes provides valuable tools for the development of asymptotic theory in (nonparametric) statistical models, and makes it possible to give a unified treatment of various models. This book reveals the relation between the asymptotic behavior of M-estimators and the

Random Graphs and Complex Networks (Camb

📁 Random Graphs and Complex Networks (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 43)

✍ Remco van der Hofstad 📂 Library 📅 2016 🏛 Cambridge University Press 🌐 English

This rigorous introduction to network science presents random graphs as models for real-world networks. Such networks have distinctive empirical properties and a wealth of new models have emerged to capture them. Classroom tested for over ten years, this text places recent advances in a unifie

Random Graphs and Complex Networks: Volu

📁 Random Graphs and Complex Networks: Volume 2 (Cambridge Series in Statistical and Probabilistic Mathematics)

✍ Remco van der Hofstad 📂 Library 📅 2024 🏛 Cambridge University Press 🌐 English

Complex networks are key to describing the connected nature of the society that we live in. This book, the second of two volumes, describes the local structure of random graph models for real-world networks and determines when these models have a giant component and when they are small-, and u

Random Graphs and Complex Networks: Volu

📁 Random Graphs and Complex Networks: Volume 2 (Cambridge Series in Statistical and Probabilistic Mathematics)

✍ Remco van der Hofstad 📂 Library 📅 2024 🏛 Cambridge University Press 🌐 English

Complex networks are key to describing the connected nature of the society that we live in. This book, the second of two volumes, describes the local structure of random graph models for real-world networks and determines when these models have a giant component and when they are small-, and u

Statistical Hypothesis Testing in Contex

📁 Statistical Hypothesis Testing in Context: Volume 52: Reproducibility, Inference, and Science (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 52)

✍ Michael P. Fay, Erica H. Brittain 📂 Library 📅 2022 🏛 Cambridge University Press 🌐 English

Fay and Brittain present statistical hypothesis testing and compatible confidence intervals, focusing on application and proper interpretation. The emphasis is on equipping applied statisticians with enough tools - and advice on choosing among them - to find reasonable methods for almost any p

Bayesian Methods: An Analysis for Statis

📁 Bayesian Methods: An Analysis for Statisticians and Interdisciplinary Researchers (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 5)

✍ Thomas Leonard, John S. J. Hsu 📂 Library 📅 1999 🏛 Cambridge University Press 🌐 English

This exposition of the Bayesian approach to statistics at a level suitable for final year undergraduate and Masters students is unique in presenting its subject with a practical flavor and an emphasis on mainstream statistics. It shows how to infer scientific, medical, and social conclusions from nu