22 avsnitt

This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathematics in the research and development of efficient statistical methods.

Statistics for Applications MIT

    • Utbildning

This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathematics in the research and development of efficient statistical methods.

    • video
    1. Introduction to Statistics

    1. Introduction to Statistics

    In this lecture, Prof. Rigollet talked about the importance of the mathematical theory behind statistical methods and built a mathematical model to understand the accuracy of the statistical procedure.

    • 1 tim. 18 min
    • video
    2. Introduction to Statistics (cont.)

    2. Introduction to Statistics (cont.)

    This lecture is the second part of the introduction to the mathematical theory behind statistical methods.

    • 1 tim. 17 min
    • video
    3. Parametric Inference

    3. Parametric Inference

    In this lecture, Prof. Rigollet talked about statistical modeling and the rationale behind statistical modeling.

    • 1 tim. 22 min
    • video
    4. Parametric Inference (cont.) and Maximum Likelihood Estimation

    4. Parametric Inference (cont.) and Maximum Likelihood Estimation

    In this lecture, Prof. Rigollet talked about confidence intervals, total variation distance, and Kullback-Leibler divergence.

    • 1 tim. 17 min
    • video
    5. Maximum Likelihood Estimation (cont.)

    5. Maximum Likelihood Estimation (cont.)

    In this lecture, Prof. Rigollet talked about maximizing/minimizing functions, likelihood, discrete cases, continuous cases, and maximum likelihood estimators.

    • 1 tim. 16 min
    • video
    6. Maximum Likelihood Estimation (cont.) and the Method of Moments

    6. Maximum Likelihood Estimation (cont.) and the Method of Moments

    In this lecture, Prof. Rigollet continued on maximum likelihood estimators and talked about Weierstrass Approximation Theorem (WAT), and statistical application of the WAT, etc.

    • 1 tim. 19 min

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