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BST 231 - Statistical Inference I or return to Course Catalog Search

190048 – Section 1   

SchoolDepartmentFaculty
Harvard T.H. Chan School of Public HealthBiostatisticsJudith Lok
TermDay and TimeLocation
Spring (Full Term) 2018  (show academic calendar)MW   9:45 a.m. - 11:15 a.m.Kresge 202A (HSPH)
Credits
5  (show credit conversion for other schools)
Credit Level
Graduate

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 time to be announced at first meeting; cross-listed: HSPH student must register for HSPH course.Course Prerequisite(s): BST230 (concurrent enrollment allowed)Formerly BIO231

Prerequisite(s)
Prerequisite: BST230 (Concurrent Enrollment Allowed)

 
Cross Registration
Eligible for cross-registration
With permission of instructor/subject to availability

MIT students please cross register from MIT's Add/Drop application.

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