Survival Analysis: Modeling and Estimation

Related lectures (30)

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Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.

Explores variance, covariance, and practical applications in statistics and probability.

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

Covers t-tests, p-values calculation, and comparison of coefficients.

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Covers binary classification, hypothesis testing, likelihood ratio tests, and decision rules.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.