Bias of an estimator

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Related lectures (32)

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Basic Principles of Point Estimation

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

Linear Regression: Statistical Inference Perspective

Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.

Nonparametric Statistics: Estimation and Optimization

Explores nonparametric statistics, covering estimation methods and the bias-variance tradeoff.

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Confidence Intervals: Gaussian Estimation

Explores confidence intervals, Gaussian estimation, Cramér-Rao inequality, and Maximum Likelihood Estimators.

Spiked Matrix Estimation

Covers the AMP algorithm for spiked matrix estimation and its application to low-rank matrix factorization and GLM models.

Geometric Ergodicity: Convergence Diagnostics

Covers the concept of geometric ergodicity in the context of convergence diagnostics for Markov chains.

Bayes Estimator, Simulated Annealing and EM

Covers Bayes estimator, Simulated Annealing, and EM for parameter estimation.

Estimators: Consistency and Efficiency

Explores the criteria for good estimators, emphasizing consistency and efficiency in estimation.

Statistics for Data Science: Introduction to Statistical Methods

Covers the fundamental concepts of statistics and their application in data science.