Statistics for Applications (Fall 2016) by Philippe Rigollet at MIT

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source: MIT OpenCourseWare   2017年10月30日
MIT 18.650 Statistics for Applications, Fall 2016
View the complete course: http://ocw.mit.edu/18-650F16
Instructor: Philippe Rigollet
This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathematics in the research and development of efficient statistical methods.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

1 1:18:03 Introduction to Statistics
2 1:17:09 Introduction to Statistics (cont.)
3 1:22:37 Parametric Inference
4 1:17:57 Parametric Inference (cont.) and Maximum Likelihood Estimation
5 1:16:32 Maximum Likelihood Estimation (cont.)
6 1:19:10 Maximum Likelihood Estimation (cont.) and the Method of Moments
7 1:18:51 Parametric Hypothesis Testing
8 1:18:33 Parametric Hypothesis Testing (cont.)
9 1:21:22 Parametric Hypothesis Testing (cont.)
10 1:22:48 Parametric Hypothesis Testing (cont.) and Testing Goodness of Fit
11 1:21:17 Testing Goodness of Fit (cont.)
12 1:16:02 Regression
13 1:13:59 Regression (cont.)
14 1:15:29 Regression (cont.)
15 1:18:06 Bayesian Statistics
16 1:03:06 Bayesian Statistics (cont.)
17 1:17:12 Principal Component Analysis
18 1:16:53 Principal Component Analysis (cont.)
19 1:15:14 Generalized Linear Models
20 1:17:20 Generalized Linear Models (cont.)
21 1:18:21 Generalized Linear Models (cont.)
22 54:45 Generalized Linear Models (cont.)