The intention of this course is to provide students with decision theory, estimation, confidence intervals, and hypothesis testing. Topics will be covered in this course are statistical models, Bayesian models, decision-theoretic framework, prediction, and sufficiency. Addition to that, it introduces large sample theory, asymptotic efficiency of estimates, and sequential analysis, exponential families I, exponential Families II, methods of estimation I and methods of estimation II.
This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.
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Course Credit: MIT
|Decision Theoretic Framework||00:10:00|
|Exponential Families I||00:10:00|
|Exponential Families II||00:10:00|
|Methods of Estimation I||00:10:00|
|Methods of Estimation II||00:10:00|
|Unbiased Estimation and Risk Inequalities||00:10:00|
|Convergence of Random Variables Probability Inequalities||00:10:00|
|Asymptotics I: Consistency and Delta Method||00:10:00|
|Asymptotics II: Limiting Distributions||00:10:00|
|Asymptotics III: Bayes Inference and Large-Sample Tests||00:15:00|
|Gaussian Linear Models||00:15:00|
|Generalized Linear Models||00:10:00|
|Case Study: Applying Generalized Linear Models||00:15:00|
|Submit Your Assignment||00:00:00|
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