MATH5905代写、代写Python/c++程序

日期：2023-03-07 10:15

UNSW SCIENCE

School of Maths and Statistics

Course outline

MATH5905

Statistical Inference

Please refer to your Timetable on MyUNSW for your Lecture Tut, Lab enrolment days and times.

Administrative Contacts

Please visit the School of Mathematics and Statistics website for a range of information on School

Policies, Forms and Help for Students.

For information on Courses, please go to “Current Students” and either Undergraduate and/or

Postgraduate”, Course Homepage” for information on all course offerings,

The “Student Notice Board” can be located by going to the “Current Students” page; Notices are

posted regularly for your information here. Please familiarise yourself with the information found

in these locations. The School web page is: https://www.maths.unsw.edu.au

If you cannot find the answer to your queries on the web you are welcome to contact the Student

Services Office directly.

By email Postgraduate pg.mathsstats@unsw.edu.au

By phone: 9385 7053

Should we need to contact you, we will use your official UNSW email address of in the first

instance. It is your responsibility to regularly check your university email account. Please state

your student number in all emails.

Course Information

Assumed knowledge / Pre-Requisite: MATH2801, MATH2901 or MATH5846 and MATH5856, or

admitted to the postgraduate program of the Department of Statistics

Excluded: MATH3811, MATH3911

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Course Aims

The aim of the course is to introduce the main ideas and principles behind the parametric and

nonparametric inference procedures. Both frequentist and Bayesian perspective will be discussed.

Estimation, confidence set construction and hypothesis testing are discussed within decision

theoretic framework. Both finite sample optimality and asymptotic optimality will be defined and

discussed.

Computationally intensive methods such as bootstrap are discussed and are compared to

asymptotic approximations such as Edgeworth expansions and saddle point method. Students will

learn how to determine appropriate inference procedure and to draw inferences using the chosen

procedure. Time permitting, applications in Statistical Financial Engineering will shortly be

discussed.

Course Description

This course presents General interference theory based on maximum likelihood and on Bayes

methods is reviewed. Estimation, confidence set construction and hypothesis testing are

discussed within decision-theoretic framework. Computationally intensive methods such as

bootstrap are discussed and are compared to asymptotic approximations such as saddle point

and empirical likelihood.

Assessment and Deadlines

Assessment Week Weighting

%

Due date if

applicable

CLO’s

Assignment 1 Week 4 10% 12 March CLO1, CLO2

Midterm Test Week 7 20% 30 March CLO3, CLO4

Assignment 2 Week 9 10% 16 April CLO5, CLO6

Final Exam Exam period 60% ALL CLO’s

Late Submission of Assessment Tasks

No late submissions will be accepted. (Where "late" in this context means after any extensions

granted for Special Consideration or Equitable Learning Provisions.)

Course Learning Outcomes (CLO)

1. Show how Statistical Inference arises from the first principles of Probability Theory.

2. Understand the fundamental principles of inference: sufficiency, likelihood, ancillarity,

equivariance.

3. Comprehend the concepts of finite-sample and asymptotic efficiency of

Inference Procedure.

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4. Demonstrate mastery of the parametric and non-parametric delta method,

asymptotic normality, Edgeworth expansions and saddle point method.

5. Estimate key population parameters of interest, to test hypotheses about them and to

construct confidence regions.

6. Use in practice the parametric, nonparametric, Bayes and robust inference.

7. Use computer packages to generate output for the most common Inference Procedures

and for computer-intensive calculations such as bootstrapping and robust estimation.

Course Schedule

The course will include material taken from some of the following topics. This is should only serve

as a guide as it is not an extensive list of the material to be covered and the timings are

approximate. The course content is ultimately defined by the material covered in lectures.

Weeks Topic Reading (if

applicable)

1 Revision. The general inference problem as a decision-theoretic

problem.

Refer to

Moodle

Lecture notes

2 Bayes and minimax decision rules. Principles of data reduction and

inference.

3 Classical estimation theory. Methods for finding estimators.

Information and likelihood.

4 Expected and observed Fisher information matrices. Cramer-Rao

bound. Uniform minimum variance unbiased estimators.

5 Likelihood inference. Estimating statistical functionals. Asymptotic

properties of estimators.

7 Order statistics. Hypothesis testing. Neyman-Pearson theory.

8 Generalized Likelihood Ratio Tests. Higher order asymptotics.

9 Edgeworth expansions. Saddlepoint approximations. Laplace's

method.

10 An introduction to the bootstrap as a computationally intensive

method. Robustness.

Textbooks

Casella, G. and Berger, R. Statistical Inference. Second Edition, Brooks/Cole (2001). This

is the recommended textbook.

Young, G. and Smith, R. Essentials of Statistical Inference. Cambridge University Press

(2005).

A.W. van der Vaart. Asymptotic Statistics. Cambridge University Press (1998).

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Wasserman, L., All of Nonparametric Statistics. Springer (2006).

DasGupta, A. Asymptotic Theory of Statistics and Probability. Springer (2008).

From the textbooks, the recommended text by Casella and Berger (2001) will be most useful. The

remaining texts complement the lecture notes and cover different aspects of the course. Lecture

notes will be provided regularly, before the start of each week.

The books of Young and Smith (2005) and A.W. van der Vaart (1998) are a bit more advanced but

contain important material and will be used for some more specialized topics. The textbook

DasGupta, A. (2008) is an encyclopaedic book covering both classical and modern topics of

inference. It also contains many examples.

Moodle

Log in to Moodle to find announcements, general information, notes, lecture slide, classroom tutorial

and assessments etc.

https://moodle.telt.unsw.edu.au

School and UNSW Policies

The School of Mathematics and Statistics has adopted a number of policies relating to enrolment,

attendance, assessment, plagiarism, cheating, special consideration etc. These are in addition to

the Policies of The University of New South Wales. Individual courses may also adopt other

policies in addition to or replacing some of the School ones. These will be clearly notified in the

Course Initial Handout and on the Course Home Pages on the Maths Stats web site.

Students in courses run by the School of Mathematics and Statistics should be aware of the School

and Course policies by reading the appropriate pages on the Maths Stats web site starting at:

https://www.maths.unsw.edu.au/currentstudents/assessment-policies

The School of Mathematics and Statistics will assume that all its students have read and

understood the School policies on the above pages and any individual course policies on the

Course Initial Handout and Course Home Page. Lack of knowledge about a policy will not be an

excuse for failing to follow the procedure in it.

Academic Integrity and Plagiarism

UNSW has an ongoing commitment to fostering a culture of learning informed by academic

integrity. All UNSW staff and students have a responsibility to adhere to this principle of academic

integrity. Plagiarism undermines academic integrity and is not tolerated at UNSW. Plagiarism at

UNSW is defined as using the words or ideas of others and passing them off as your own.

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The UNSW Student Code provides a framework for the standard of conduct expected of UNSW

students with respect to their academic integrity and behaviour. It outlines the primary

obligations of students and directs staff and students to the Code and related procedures.

In addition, it is important that students understand that it is not permissible to buy

essay/writing services from third parties as the use of such services constitutes plagiarism

because it involves using the words or ideas of others and passing them off as your own. Nor is

it permissible to sell copies of lecture or tutorial notes as students do not own the rights to this

intellectual property.

If a student breaches the Student Code with respect to academic integrity, the University may take

disciplinary action under the Student Misconduct Procedure.

The UNSW Student Code and the Student Misconduct Procedure can be found at:

https://student.unsw.edu.au/plagiarism

An online Module “Working with Academic Integrity” (https://student.unsw.edu.au/aim) is a six-

lesson interactive self-paced Moodle module exploring and explaining all of these terms and

placing them into your learning context. It will be the best one-hour investment you’ve ever made.

Plagiarism

Plagiarism is presenting another person's work or ideas as your own. Plagiarism is a serious

breach of ethics at UNSW and is not taken lightly. So how do you avoid it? A one-minute video for

an overview of how you can avoid plagiarism can be found

https://student.unsw.edu.au/plagiarism.

Additional Support

ELISE (Enabling Library and Information Skills for Everyone)

ELISE is designed to introduce new students to studying at UNSW.

Completing the ELISE tutorial and quiz will enable you to:

analyse topics, plan responses and organise research for academic writing and other

assessment tasks

effectively and efficiently find appropriate information sources and evaluate relevance

to your needs

use and manage information effectively to accomplish a specific purpose

better manage your time

understand your rights and responsibilities as a student at UNSW

be aware of plagiarism, copyright, UNSW Student Code of Conduct and Acceptable Use of

UNSW ICT Resources Policy

be aware of the standards of behaviour expected of everyone in the UNSW community

locate services and information about UNSW and UNSW Library

Some of these areas will be familiar to you, others will be new. Gaining a solid understanding of all

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the related aspects of ELISE will help you make the most of your studies at UNSW.

The ELISE training webpages:

https://subjectguides.library.unsw.edu.au/elise/aboutelise

Equitable Learning Services (ELS)

If you suffer from a chronic or ongoing illness that has, or is likely to, put you at a serious

disadvantage, then you should contact the Equitable Learning Services (previously known as

SEADU) who provide confidential support and advice.

They assist students:

living with disabilities

with long- or short-term health concerns and/or mental health issues

who are primary carers

from low SES backgrounds

of diverse genders, sexes and sexualities

from refugee and refugee-like backgrounds

from rural and remote backgrounds

who are the first in their family to undertake a bachelor-level degree.

Their web site is: https://student.unsw.edu.au/els/services

Equitable Learning Services (ELS) may determine that your condition requires special

arrangements for assessment tasks. Once the School has been notified of these, we will make

every effort to meet the arrangements specified by ELS.

Additionally, if you have suffered significant misadventure that affects your ability to complete the

course, please contact your Lecturer-in-charge in the first instance.

Academic Skills Support and the Learning Centre

The Learning Centre offers academic support programs to all students at UNSW Australia. We

assist students to develop approaches to learning that will enable them to succeed in their

academic study. For further information on these programs please go to:

http://www.lc.unsw.edu.au/services-programs

Applications for Special Consideration for Missed Assessment

Please adhere to the Special Consideration Policy and Procedures provided on the web page below

when applying for special consideration.

https://student.unsw.edu.au/special-consideration

Please note that the application is not considered by the Course Authority, it is considered by a

centralised team of staff at the Nucleus Student Hub.

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The School will contact you (via student email account) after special consideration has been

granted to reschedule your missed assessment, for a lab test or paper-based test only.

For applications for special consideration for assignment extensions, please note that the new

submission date and/or outcome will be communicated through the special consideration web site

only, no communication will be received from the School.

For Dates on Final Term Exams and Supplementary Exams please check the “Key Dates for

Exams” ahead of time to avoid booking holidays or work obligations.

https://student.unsw.edu.au/exam-dates

If you believe your application for Special Consideration has not been processed, you should email

specialconsideration@unsw.edu.au immediately for advice.

Course Evaluation and Development (MyExperience)

Student feedback is very important to continual course improvement. This is demonstrated within

the School of Mathematics and Statistics by the implementation of the UNSW online student

survey myExperience, which allows students to evaluate their learning experiences in an

anonymous way. myExperience survey reports are produced for each survey. They are released to

staff after all student assessment results are finalised and released to students. Course convenor

will use the feedback to make ongoing improvements to the course.