Statistical Theory: Decision Theory Framework

Related lectures (31)

Page 3 of 4

Explores confidence intervals, hypothesis testing, and decision-making using test statistics and p-values.

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Explores hypothesis testing, emphasizing the state of nature and the importance of choosing the most powerful test.

Covers statistical hypothesis testing, confidence intervals, p-values, and significance levels in hypothesis testing.

Explores sampling in inferential statistics, emphasizing the impact of sample size and randomness on inference accuracy.

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Covers statistical hypothesis testing, likelihood estimation, and confidence intervals construction.

Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

Covers the likelihood ratio test for detection and estimation in statistical analysis.