Point estimation

Formal sciences
Statistics
Statistical inference
Statistical hypothesis testing

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Related lectures (32)

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Estimation Methods: Bias-Variance Tradeoff

Explores the MSE quality measure for estimators and the bias-variance tradeoff.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Estimation and Confidence Intervals

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Hypothesis Testing: Wilks' Theorem

Explores hypothesis testing using Wilks' Theorem, likelihood ratio statistics, p-values, interval estimation, and confidence regions.

Hypothesis Testing: A Different Perspective

Delves into a different perspective on hypothesis testing, emphasizing the p-value and significance levels.

Linear Regression: Statistical Inference Perspective

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

Estimation and Analysis of Deviance

Covers estimation of unknown parameters, analyzing model fit, bird prey response, and model diagnostics.

Inference and Mixed Models

Covers point estimation, confidence intervals, and hypothesis testing for smooth functions using mixed models and spline smoothing.

Sparse Regression

Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.

Maximum Likelihood Estimation

Delves into maximum likelihood estimators, their properties, and asymptotic behavior, emphasizing consistency and asymptotic normality.