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Statistical Inference I
BST 231

Jointly Offered with: Faculty of Arts & Sciences as BIOSTAT 231

Course Information

Description

A fundamental course in statistical inference. Discusses general principles of data reduction: exponential families, sufficiency, ancillarity and completeness. Describes general methods of point and interval parameter estimation and the small and large sample properties of estimators: method of moments, maximum likelihood, unbiased estimation, Rao-Blackwell and Lehmann-Scheffe theorems, information inequality, asymptotic relative efficiency of estimators. Describes general methods of hypothesis testing and optimality properties of tests: Neyman-Pearson theory, likelihood ratio tests, score and Wald tests, uniformly and locally most powerful tests, asymptotic relative efficiency of tests.

Course Note: Lab or section times to be announced at first meeting; cross-listed: Harvard Chan Students must register for the Harvard Chan course.

Course Prerequisite(s): BST230

School Harvard Chan School
Credits 5
Cross Reg

Available for Harvard Cross Registration

Department Biostatistics
Course Component Lecture
Instruction Mode In Person
Grading Basis HSPH Student Option (Audit, Ordinal, Pass/Fail)
Course Requirements Prerequisite: BST230 (Concurrent Enrollment Allowed)