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Related lectures (31)

Covers maximum likelihood estimation to estimate parameters by maximizing prediction accuracy, demonstrating through a simple example and discussing validity through hypothesis testing.

Sampling Distributions: Estimation

Explores sampling distributions, estimation methods, and consistency in parameter estimation.

Testing: t-tests

Covers t-tests, p-values calculation, and comparison of coefficients.

Hypothesis Testing & Confidence Intervals

Covers hypothesis testing, power, confidence intervals, and small sample considerations.

Law of Large Numbers, Statistics

Covers the Law of Large Numbers in Statistics and methods for deriving estimators and maximum likelihood.

Probability & Stochastic Processes

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Statistical Estimation: Properties and Distributions

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

Nonparametric Statistics: Bayesian Approach

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

Maximum Likelihood Estimation

Covers maximum likelihood estimation, likelihood function, parameter estimation, and hypothesis testing.

Introduction to Behavior Modeling: Simple Example

Introduces behavior modeling through a simple example, focusing on choice modeling components and the analysis of the electric car market.

Maximum Likelihood Theory & Applications

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

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