Все публикации

0:42:34

Versions of spectral theorems; positive operators

0:26:27

Real Spectral Theorem

0:51:37

EM algorithm and missing data part 2

1:08:36

Analysis of Discrete Data Lesson 11: Ordinal and dependent data

1:10:25

4 12 16

1:03:17

Analysis of Discrete Data Lesson 11: Structural and Sampling Zeros part 3

1:06:02

Analysis of Discrete Data Lesson 11: Sampling and Structural Zeros part 2

1:02:42

Analysis of Discrete Data Lesson 11: Sampling and Structural Zeros part 1

0:51:04

Analysis of Discrete Data Lesson 9: Poisson regression and log linear models part 2

1:02:18

Analysis of Discrete Data Lesson 9/10: Poisson regression and log linear models part 3

0:50:04

Analysis of Discrete Data: Model Selection, Akaike and Bayesian information criterion

0:17:00

Analysis of Discrete Data Lesson 9: Poisson Regression and Poisson GLMs part 1

1:09:46

Analysis of Discrete Data Lesson 8: Multinomial Logistic Regression Part 2

0:58:58

Analysis of Discrete Data Lesson 8: Multinomial Logistic Regression Part 1

1:07:24

Analysis of Discrete Data Lesson 7: Logistic regression

1:08:31

Analysis of Discrete Data Lesson 6 Part 2

1:09:16

Analysis of Discrete Data Lesson 6 part 1: generalized linear models (GLMs) and logistic regression

1:04:23

Analysis of Discrete Data Lesson 5 Part 2: Three way tables

1:09:03

Analysis of Discrete Data Lesson 5: Three-way tables, association and independence

1:08:41

Analysis of Discrete Data Lesson 4 Part 2: ordinal data and dependent samples in two by two tables

0:55:12

Lesson 4 Two way tables with ordinal data

0:14:18

Lesson 4 Dependent Samples in two way data

0:53:01

Analysis of Discrete Data Lesson 4 Part 1: prospective, retrospective, I by J tables

1:07:48

Analysis of Discrete Data Lesson 3: Two-way tables, independence, sampling schemes, goodness of fit