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Related lectures (32)
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Maximum Likelihood Estimation
Covers Maximum Likelihood Estimation in statistical inference, discussing MLE properties, examples, and uniqueness in exponential families.
Sampling Theory: Statistics for Mathematicians
Covers the theory of sampling, focusing on statistics for mathematicians.
Statistical Theory: Cramér-Rao Bound & Hypothesis Testing
Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.
Maximum Likelihood Estimation: Theory
Covers the theory behind Maximum Likelihood Estimation, discussing properties and applications in binary choice and ordered multiresponse models.
Raster - Vector: Spatial Interactions
Explores combining raster and vector layers for spatial analysis in QGIS.
Fisher Information, Cramér-Rao Inequality, MLE
Explains Fisher information, Cramér-Rao inequality, and MLE properties, including invariance and asymptotics.
Maximum Likelihood Estimation: Econometrics
Introduces Maximum Likelihood Estimation in econometrics, covering principles, properties, applications, and specification tests.
Parameter Estimation & Fisher Information
Covers parameter estimation, Fisher information, unbiased estimator, and exponential distributions.
Sampling Distributions: Theory and Applications
Explores sampling distributions, estimators' properties, and statistical measures for data science applications.
Sampling Theory: Sufficiency and Ancillarity
Explores sufficiency and ancillarity in sampling theory, emphasizing the importance of sufficient statistics in compressing data without losing information.
