mathematical statistics an introduction to likelihood based inference pdf junr

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==> mathematical statistics an introduction to likelihood based inference pdf <==

"Mathematical Statistics: An Introduction to Likelihood-Based Inference" is a comprehensive text that focuses on the theory and application of likelihood methods in statistical inference. It begins by laying a solid foundation in probability theory, emphasizing the concepts necessary for understanding statistical modeling and inference. The book explores the principles of likelihood, including the likelihood function and its role in parameter estimation and hypothesis testing. Through a detailed examination of various likelihood-based techniques, it covers topics such as maximum likelihood estimation (MLE), likelihood ratio tests, and Bayesian inference, highlighting their mathematical underpinnings and practical applications. The authors provide a range of examples and exercises, illustrating how these methods can be applied to real-world data, thereby bridging the gap between theory and practice. The text is suitable for graduate students and researchers in statistics, providing them with the tools to analyze and interpret data effectively. By emphasizing the likelihood approach, it offers a modern perspective on statistical inference, encouraging readers to engage deeply with the mathematical concepts and their implications in statistical analysis. Overall, this book serves as both an introduction and a reference for those looking to deepen their understanding of statistical inference through the lens of likelihood, equipping them with the skills to tackle complex statistical problems in various fields, from economics to biology.