Maximum Likelihood Estimation
Offered By: statisticsmatt via YouTube
Course Description
Overview
Syllabus
MLE of a Bernoulli Distribution and a Binomial Distribution.
MLE of a Continuous Uniform Distribution.
MLE of a Normal Distribution and a Mixture of Normal Distributions.
Derivative of a Determinant with respect to a Matrix.
Derivative of a Quadratic Form with respect to a Vector.
Derivative of a Trace with respect to a Matrix.
Maximum Likelihood Estimates for a Multivariate Normal Distribution.
The Score Function - Asymptotic Normality.
Kaplan Meier Estimator as an MLE.
MLE for a Wishart Distribution (central).
MLE of a Gumbel Distribution (part 1).
MLE for a Gumbel Distribution (part 2).
MLEs of a Gamma Distribution (part 1).
MLEs of a Gamma Distribution (part 2).
MLE of a Negative Binomial Distribution.
MLEs for a Beta Distribution (part 1).
Method of Moments and MLEs for a Beta Distribution (part 2).
MLE of a Multinomial Distribution.
MLEs of a Double Exponential Distribution.
Using R to Generate Double Exponential Data and Calculate the MLEs.
MLEs of an Inverse Gamma Distribution.
Using R to find the MLEs and Method of Moments estimators for an Inverse Gamma Distribution.
Generating Data and deriving the MLE for a 2 Parameter Lindley Distribution.
Maximum Likelihood Estimators Beta Binomial Distribution.
Using R: Method of Moments and ML estimators for Beta Binomial Distribution.
Taught by
statisticsmatt
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