Estimation Criteria

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Related lectures (32)

Page 3 of 4

Statistics: Hypothesis Testing & Confidence Intervals

Covers hypothesis testing, confidence intervals, data distributions, and statistical significance in data analysis.

Statistical Estimation: Properties and Distributions

Explores statistical parameter estimation, sample accuracy, and Bernoulli variables' properties.

Bias, Variance, Consistency, EMV

Covers bias, variance, mean squared error, consistency, and maximum likelihood estimation in the Poisson model.

Intro to Quantum Sensing: Parameter Estimation and Fisher Information

Introduces Fisher Information for parameter estimation based on collected data.

Confidence Intervals: Margins, Coverage, Pivots

Explains margins of error, coverage, and pivots in constructing confidence intervals for scalar parameters.

Basics of linear regression model

Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.

Optimality in Decision Theory: Unbiased Estimation

Explores optimality in decision theory and unbiased estimation, emphasizing sufficiency, completeness, and lower bounds for risk.

Statistical Measures: Mean, Median, and Dispersion Techniques

Discusses statistical measures of central tendency and dispersion, focusing on mean, median, and their implications in data analysis.

Bias and Variance in Estimation

Discusses bias and variance in statistical estimation, exploring the trade-off between accuracy and variability.

Interval Estimation

Covers the construction of confidence intervals for a normal distribution with unknown mean and variance.