Concept
Estimator
Related lectures (207)
Point Estimation in Statistics
Covers the concept of point estimation in statistics, focusing on methods to estimate unknown parameters from a given sample.
Model Selection: Non-Nested Model Selection
Explores model selection, criteria, bias/variance tradeoff, and cross-validation methods.
Estimation and Detection
Explores estimation, detection, linear estimators, and the orthogonality principle in machine learning.
Least-squares Approximation
Explains the concept of least-squares approximation and its computation in specific and general cases.
Mean-Square-Error Inference
Covers the concept of mean-square-error inference and optimal estimators for inference problems using different design criteria.
Statistical Estimation: Gaussian Linear Model
Delves into statistical estimation, highlighting the Gaussian linear model and the limitations of ML estimators.
SVMs and Feature Maps
Explores SVMs, feature maps, and the importance of finding the maximum margin solution for classification problems.
Instrumental Variables: Intuition and Estimation
Covers instrumental variables, addressing endogeneity issues in regression analysis through estimation techniques and practical examples.
Spectral Estimation: Periodogram and Tapering
Explores spectral representations, ACVS estimation, and spectral estimation in time series analysis.
Spectral & Parametric Estimation: Time Series
Covers spectral estimation techniques like tapering and parametric estimation, emphasizing the importance of AR models and Whittle likelihood in time series analysis.