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Lecture
Sampling Theory: Statistics and Inference
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Related lectures (30)
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Entropy and Sampling Theory
Explores entropy, minimally sufficient statistics, exponential families, and Gaussian sampling distributions.
Hypothesis Testing and Confidence Intervals: An Overview
Covers hypothesis testing, confidence intervals, and their applications in statistics.
Parameter Estimation
Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.
Statistical Theory: Inference and Optimality
Explores constructing confidence regions, inverting hypothesis tests, and the pivotal method, emphasizing the importance of likelihood methods in statistical inference.
Statistical Estimation Methods
Covers statistical estimation methods, including maximum likelihood and Bayesian estimation.
Interval Estimation: Method of Moments
Covers the method of moments for estimating parameters and constructing confidence intervals based on empirical moments matching distribution moments.
Sampling Distributions: Estimators and Variance
Covers estimation of parameters, MSE, Fisher information, and the Rao-Blackwell Theorem.
Distribution Theory of Least Squares
Explores the distribution theory of least squares estimators in a Gaussian linear model, focusing on precision and confidence intervals construction.
Approximate Query Processing: BlinkDB
Introduces BlinkDB, a framework for approximate query processing using sampling techniques.
Basics of Linear Regression
Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.
