STAT 462 Spring 2015 Modern Nonparametric Statistics (Q)

Many statistical procedures and tools are based on a set of assumptions, such as normality. But, what if some or all of these assumptions are not valid? This question leads to the consideration of distribution-free analysis, an active and fascinating field in modern statistics called nonparametric statistics. In this course we aim to make inference for population characteristics while making as few assumptions as possible. Besides the classical rank or randomization-based tests, this course especially focuses on various modern nonparametric inferential techniques, such as nonparametric density estimation, nonparametric regression, selection of smoothing parameter (cross validation and unbiased risk estimation), bootstrap and jackknife, and Minimax theory. Throughout the semester we will examine these new methodologies and apply them on simulated and real data sets using R.
Class Format: lecture
Requirements/Evaluation: evaluation will be based primarily on homework, exams, and a final presentation and project
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Prerequisites: STAT 201 and STAT 360, or permission of instructor
Enrollment Preference: those who have taken STAT 346
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Divisional Attributes: Division III,Quantitative and Formal Reasoning
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Enrollment Limit: 30
Expected Enrollment: 15
Class Number: 3463
CLASSES ATTR INSTRUCTORS TIMES CLASS NUMBER
STAT 462 - 01 (S) LEC Modern Nonparametric Statistic (Q) Division 3: Science and MathematicsQuantitative and Formal Reasoning Wendy Wang
MWF 11:00 AM-11:50 AM 3463
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