Dieses Buch liefert praxisbezogenes Wissen zur Planung, Durchführung und Interpretation von klinischen Studien und richtet sich an alle Personen, die eine wissenschaftliche Karriere beschreiten wollen oder an Evidence Based Medicine interessiert sind.
The goal of this guide and manual is to provide a practical and brief overview of the theory on computerized adaptive testing (CAT) and multistage testing (MST) and to illustrate the methodologies and applications using R open source language and several data examples.
The goal of this book is to gather in a single work the most relevant concepts related in optimization methods, showing how such theories and methods can be addressed using the open source, multi-platform R tool.
This book examines application and methods to incorporating stochastic parameter variations into the optimization process to decrease expense in corrective measures.
This book treats the notion of morphisms in spatial analysis, paralleling these concepts in spatial statistics (Part I) and spatial econometrics (Part II).
This book provides a self-contained presentation of the statistical tools required for evaluating public programs, as advocated by many governments, the World Bank, the European Union, and the Organization for Economic Cooperation and Development.
This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences.
An Introduction to Probability and Statistical Inference, Third Edition, guides the reader through probability models and statistical methods to develop critical-thinking skills.
This 3rd edition of Modern Mathematical Statistics with Applications tries to strike a balance between mathematical foundations and statistical practice.
This 2nd edition textbook offers a rigorous introduction to measure theoretic probability with particular attention to topics of interest to mathematical statisticians-a textbook for courses in probability for students in mathematical statistics.
This book offers an application-oriented guide to random forests: a statistical learning method extensively used in many fields of application, thanks to its excellent predictive performance, but also to its flexibility, which places few restrictions on the nature of the data used.
This book presents a concise introduction to piecewise deterministic Markov processes (PDMPs), with particular emphasis on their applications to biological models.
This volume gathers selected peer-reviewed papers presented at the XXVI International Joint Conference on Industrial Engineering and Operations Management (IJCIEOM), held on July 8-11, 2020 in Rio de Janeiro, Brazil.
Highlighting the latest advances in nonparametric and semiparametric statistics, this book gathers selected peer-reviewed contributions presented at the 4th Conference of the International Society for Nonparametric Statistics (ISNPS), held in Salerno, Italy, on June 11-15, 2018.
This book analyses the effectiveness of climate finance as political instrument to reduce the effect of anthropogenic activities on climate change and promote the green growth in developing countries.
This updated and revised first-course textbook in applied probability provides a contemporary and lively post-calculus introduction to the subject of probability.
This volume brings together, in a central text, chapters written by leading scholars working at the intersection of modeling, the natural and social sciences, and public participation.
This book addresses the origins, determinants and magnitude of the global problem of sedentary behaviour, along with concise yet in-depth solutions for tackling it.
This textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning.
This monograph uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence.
Bayesian Inference of State Space Models: Kalman Filtering and Beyond offers a comprehensive introduction to Bayesian estimation and forecasting for state space models.
