Statistical Methods for Meta-Analysis

✍ Larry V. Hedges, Ingram Olkin 📂 Library 📅 1985 🏛 Academic Press 🌐 English

The main purpose of this book is to address the statistical issues for integrating independent studies. There exist a number of papers and books that discuss the mechanics of collecting, coding, and preparing data for a meta-analysis , and we do not deal with these. Because this book concerns method

📁 Statistical Methods for Meta-Analysis

✍ Larry V. Hedges, Ingram Olkin 📂 Library 📅 1985 🏛 Academic Press 🌐 English

The main purpose of this book is to address the statistical issues for integrating independent studies. There exist a number of papers and books that discuss the mechanics of collecting, coding, and preparing data for a meta-analysis , and we do not deal with these. Because this book concerns method

Statistical Methods for Meta-Analysis

📁 Statistical Methods for Meta-Analysis

✍ Larry V. Hedges and Ingram Olkin (Auth.) 📂 Library 📅 1985 🏛 Elsevier Inc 🌐 English

The main purpose of this book is to address the statistical issues for integrating independent studies. There exist a number of papers and books that discuss the mechanics of collecting, coding, and preparing data for a meta-analysis , and we do not deal with these. <br>Because this book concerns me

Statistics - Statistical Methods for Dat

📁 Statistics - Statistical Methods for Data Analysis

📂 Library 🌐 English

Bayesian Methods for Statistical Analysi

📁 Bayesian Methods for Statistical Analysis

✍ Borek Puza 📂 Library 📅 2017 🏛 ANU Press 🌐 English

Bayesian methods for statistical analysis is a book on statistical methods for analysing a wide variety of data. The book consists of 12 chapters, starting with basic concepts and covering numerous topics, including Bayesian estimation, decision theory, prediction, hypothesis testing, hierarchical m

Statistical Methods for Annotation Analy

📁 Statistical Methods for Annotation Analysis

✍ Silviu Paun; Ron Artstein; Massimo Poesio 📂 Library 📅 2022 🏛 Morgan & Claypool 🌐 English

<b>Labelling data is one of the most fundamental activities in science, and has underpinned practice, particularly in medicine, for decades, as well as research in corpus linguistics since at least the development of the Brown corpus</b>. With the shift towards Machine Learning in Artificial Intelli