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1
Generalized Linear Models: Background
2
Generalized Linear Models: Canonical Link Function
3
Generalized Linear Models: Likelihood, Score, and Fisher Information
4
GLM: Iteratively Re-weighted Least Squares for a General Link Function
5
Generalized Linear Models: Probit Regression (part 1)
6
Generalized Linear Models: Probit Regression (part 2)
7
Generalized Linear Models: Logistic "Logit" Regression (part 1)
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Generalized Linear Models: Logistic "Logit" Regression (part 2)
9
Generalized Linear Models: Logistic "Logit" Regression (part 2)
10
Generalized Linear Models: Complementary Log Log Regression (part 1)
11
Generalized Linear Models: Complementary Log Log Regression (part 2)
12
Generalized Linear Models: Complementary Log Log Regression (part 2)
13
Generalized Linear Models: Poisson Regression with Canonical Link (part 1)
14
Generalized Linear Models: Poisson Regression with Canonical Link (part 2)
15
Ordinal Logistic Regression (Proportional Odds Model)
16
Multinomial Logistic Regression
Description:
Dive into a comprehensive 3.5-hour tutorial on Generalized Linear Models (GLMs). Explore the background, canonical link functions, and key concepts like likelihood, score, and Fisher information. Master iteratively re-weighted least squares for general link functions. Delve into specific GLM types including probit, logistic (logit), complementary log-log, and Poisson regression. Gain insights into ordinal logistic regression using the proportional odds model and multinomial logistic regression. Enhance your statistical modeling skills with in-depth explanations and practical applications of these powerful techniques.

Generalized Linear Models

statisticsmatt
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