NJIT Academic Integrity CODE: All Students should be aware that the Department of Mathematical Sciences takes the University Code on Academic Integrity at NJIT very seriously and enforces it strictly. This means that there must not be any forms of plagiarism, i.e., copying of homework, class projects, or lab assignments, or any form of cheating in quizzes and exams. Under the University Code on Academic Integrity, students are obligated to report any such activities to the Instructor.
Math 665-102: Statistical Inference
Textbook:
Statistical Inference by Casella and Berger (2nd edition)
Prerequisites:
Math 662 or departmental
approval.
Grading
Policy: Course grade will
be determined on the basis of homework (20%), two midterm exams
(25% each) and a final exam (30%).
Drop Date:
The University Drop Date
March 26, 2013
deadline will be strictly enforced.
Course
Overview: Course will aim
to develop theoretical statistics based on the principles of
probability covered in Math 662. Problem solving will be
emphasized.
Examinations:
There will be two midterm
examinations and a final examination:
Midterm Exam 1: |
February 15, 2013 (tentative) |
Midterm Exam 2: |
April 12, 2013 (tentative) |
Final Exam Week: |
May 9-15, 2013 |
Additional
Policies:
Bring a
scientific calculator to all the lectures and exams. Attendance
at all classes and tests is mandatory. Instructor will maintain
a detailed record of attendance as the administration needs to
know dates the classes were missed. Grading complaints need to
be sorted at the earliest available opportunity with the
instructor. This may mean in-class or immediately after the
class in which the graded homework or exam was handed out.
Makeup Exam
Policy: There will be
NO MAKE-UP EXAMS
during the semester. In the event
the Final Exam is not taken, under rare circumstances where the
student has a legitimate reason for missing the final exam, a
makeup exam will be administered by the math department. In any
case the student must notify the Math Department Office and
the Instructor that the exam will be missed and present
written verifiable proof of the reason for missing the exam,
e.g., a doctor’s note, police report, court notice, etc.,
clearly stating the date AND time of the mitigating problem.
Cellular
Phones: All cellular phones
and beepers must be switched off during all class times.
MATH DEPARTMENT CLASS POLICIES LINK
All DMS students must familiarize themselves with and adhere to the Department of Mathematical Sciences Course Policies, in addition to official university-wide policies. DMS takes these policies very seriously and enforces them strictly. For DMS Course Policies, please click here.
M |
Dr. Martin Luther King, Jr. Day ~ University Closed |
|
Su-Su |
Spring Recess ~ No Classes Scheduled ~ University Open |
|
T |
Last Day to Withdraw from this course |
|
F |
Good Friday ~ University Closed |
|
T |
Classes follow a Friday Schedule, Last Day of Classes |
|
W |
Reading Day |
|
T-W |
Final Exams |
Course Outline:
Tentative Course
Outline |
||
Lecture |
Date |
Topic |
1 |
1/25 |
Distributional Theory background – convergence,
delta method, exponential family of distributions |
2 |
2/1 |
Elementary Statistical Inference I: sampling and
statistics, point and interval estimation |
3 |
2/8 |
Elementary Statistical Inference II: hypothesis
testing |
4 |
2/15 |
Midterm I |
5 |
2/22 |
Sufficiency, completeness, minimal sufficiency,
completeness, ancillary statistics |
6 |
3/1 |
Point estimation – method of moments, maximum
likelihood, Bayes estimators |
7 |
3/8 |
Rao-Cramer lower bound and efficiency,
Rao-Blackwell, minimum variance unbiased estimators |
8 |
3/15 |
Interval Estimation – inverting test statistics,
pivotal statistics, Bayesian and bootstrap
intervals, evaluating interval estimators |
9 |
4/5 |
Optimal Hypothesis Tests I - Neyman-Pearson lemma,
most powerful tests |
10 |
4/12 |
Midterm II |
11 |
4/19 |
Optimal Hypothesis Tests II – Likelihood ratio
tests, uniformly most powerful tests |
12 |
4/26 |
Inference for Normal models I – quadratic forms,
ANOVA |
13 |
5/3 |
Inference for Normal models II – multiple
comparisons, tests for independence, linear
regression |
14 |
5/7 (Tue) |
Review |
15 |
|
|
Prepared By: Prof. Ji Meng Loh
Last revised: January 9, 2013