📊 Asymptotic Statistics

"Asymptotics is the art of replacing the impossible exact finite-sample distributions with the beautiful infinite-sample limits."

📈 Course Metadata

• Offered by: School of Mathematical Sciences & Center for Data Science

• Prerequisites: Mathematical Analysis, Advanced Algebra, Probability Theory, Mathematical Statistics, Functions of Real Variables

• Course Type: 32-hour Graduate Course

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📚 References

The reference textbooks used in this course are as follows:

1. A.W. van der Vaart, Asymptotic Statistics (Cambridge University Press)

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🗺️ Syllabus & Navigation

Below is the directory for this course:

Part I: Foundations of Stochastic Convergence and Limit Theorems

• Chapter 1: Data Distributions and Stochastic Convergence

• Chapter 2: Characteristic Functions

• Chapter 3: Central Limit Theorem (Part I)

• Chapter 4: Central Limit Theorem (Part II)

Part II: Weakly Dependent Data

• Chapter 5: Weakly Dependent Data I

• Chapter 6: Weakly Dependent Data II

Part III: Asymptotic Inference Tools

• Chapter 7: Delta Method

📝 Note on the Contents

Work in Progress

This page is currently under construction. All lengthy theorem proofs in the notes utilize a collapsible box design. If you have any questions or find errors, please feel free to point them out via Issues or contact me directly!

💬 Comments