01:01:52
Causal Modeling Data Science Talk with Brian Zaharatos
31:27
STAT 4520 Asymptotic Distribution of the MLE
07:29
STAT 4520 Unit #2: Defining moment generating functions
07:59
STAT 5520 Unit #6: Uniformly most powerful tests
06:18
STAT 5520 Unit #6: Neyman Pearson example
08:27
STAT 5520 Unit #6: Neyman Pearson Lemma example
10:14
STAT 5520 Unit #6: The Neyman-Pearson Lemma
10:27
STAT 5520 Unit #6: Comparing hypothesis tests
09:14
STAT 5520 Unit #6: Intro to general hypothesis testing
06:34
STAT 4520 Unit #6: Easy hypothesis test example
14:01
STAT 5520 Unit #5: Intro to Bayesian Inference II
12:25
STAT 5520 Unit #5: Intro to Bayesian Inference I
18:32
STAT 4520 Unit #5: UMVUE for uniform zero theta
11:41
STAT 4520 Unit #5: UMVUE and the exponential family
08:53
STAT 4520 Unit #5: The UMVUE
14:50
STAT 4520 Unit #5: Complete statistics
05:30
STAT 4520 Unit #5: Rao-Blackwell example
06:22
STAT 4520 Unit #5: The Rao-Blackwell theorem and proof
07:11
STAT 4520 Unit #5: Conditional distribution/expectation example
06:55
STAT 4520 Unit #5: Law of total variance
04:49
STAT 4520 Unit #5: Defining conditional distributions
06:24
STAT 4520 Unit #5: Law of total expectation
14:17
STAT 4520 Unit #5: Sufficient statistic factorization theorem
13:06
STAT 4520 Unit #5: Proof of the Cramer-Rao lower bound
08:14
STAT 4520 Unit #5: The Cauchy-Schwarz Inequality
10:02
STAT 4520 Unit #5: Invariance of the MLE example
08:22
STAT 4520 Unit #5: MLE for a uniform sample
05:29
STAT 4520 Unit #5: Method of moments estimator example
12:15
STAT 4520 Unit #4: Simulating confidence intervals
09:20
STAT 4520 Unit #4: Simple confidence interval example
07:41
STAT 4520 Unit #3: Application of the central limit theorem
05:35
STAT 4520 Unit #3: Convergence in distribution does not necessarily imply convergence in probability
10:40
STAT 4520 Unit #3: Convergence of probability implies convergence in distribution (part of proof)
07:09
STAT 4520 Unit #3: Convergence in distribution example
10:20
STAT 4520 Unit #3: Proof of unnamed probability inequality
03:47
STAT 4520 Unit #2: Mean of binomial using the moment generating function
05:49
STAT 4520 Unit #2: Finding a moment generating function
05:35
STAT 4520 Unit #2: The distribution of the max value of a random sample
08:16
STAT 4520 Unit #2: Some properties of the gamma distribution
07:35
STAT 4520 Unit #2: Transformations of random variables (example)
05:13
STAT 4520 Unit #2: Integrating without integrating
05:29
STAT 4520 Indicator functions
08:09
STAT 4520 Unit #1 properties of expectation and variance
05:18
STAT 4520 Unit #1: expectation and variance
11:31
STAT 4520 Unit #1: the probability function
07:23
STAT 4520 Unit #1: Counting Examples
22:55
topic9
11:43
Unit #7 Lesson 8: The basic components of fitting GAMs with smoothing splines
15:17
Unit #7 Lesson 6: Introduction to generalized additive models
04:52
Final Project Clarifications
14:32
Unit #7 Lesson 5: Introduction to smoothing splines
05:53
Unit #7 Lesson 4: Kernel estimation in R
14:53
Unit #7 Lesson 3: Kernel estimation
06:08
Unit #7 Lesson 2: Motivating kernel estimation
12:38
Unit #7 Lesson 1:Introduction to nonparametric regression models
17:25
Unit #6 Lesson 12: Poisson regression in R
04:46
Unit #6 Lesson 11: Poisson regression goodness of fit II
16:29
Unit #6 Lesson 10: Poisson regression goodness of fit I
07:27
Unit #6 Lesson 9: Poisson regression parameter interpretation
07:24
Unit #6 Lesson 8: Poisson regression parameter estimation
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