Maximum Likelihood Estimation

Maximum Likelihood Estimation

This course will teach you the derivation of maximum likelihood estimates and their properties.

This course will teach you the derivation of maximum likelihood estimates and their properties.

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Overview

Maximum likelihood is a popular method of estimating population parameters from a sample. This course covers the derivation of maximum likelihood estimates (MLE) and their properties. It’s primary purpose is to provide a useful conceptual foundation for those contemplating taking statistical modeling courses, it is not to provide facility with MLE as a practical tool.

This is a two-week course.

Learning Outcomes

Students who complete this course will study the derivation of maximum likelihood estimates, and their properties. You will understand the role that MLE plays in statistical models, and be able to assess both the advantages and disadvantages of using a maximum likelihood estimate in a particular situation. 

  • Assess the advantages and disadvantages of using MLE in particular cases
  • Describe the role that MLE plays in statistical models
  • Specify the properties of good estimators
  • Describe MLE derivations

Note: The primary purpose of this course is to provide a conceptual understanding of MLE as a building block in statistical modeling. It is not to provide facility with MLE as a practical tool.

Who Should Take This Course

Maximum likelihood estimation is used in many of the methods taught in Statistics.com’s intermediate and advanced courses, such as Survival Analysis, Logistic Regression and Generalized Linear Models, to name a few. Students who need to understand the theory behind those methods should take this course first.

Instructors

mr-kuber-deokar

Mr. Kuber Deokar

Mr. Kuber Deokar holds a Masters degree in Statistics from University of Pune, India, where he also taught undergraduate statistics. Mr. Deokar holds the position of Instructional Operations Supervisor at Statistics.com. He is responsible for coordination of Statistics.com online courses, and ensures seamless interactions between the management team, course instructors, teaching assistants, and students. He also serves as the senior teaching assistant and shares instructional responsibilities for several courses, and handles consultancy assignments, working from our office in Pune, India.
See Instructor Bio

Course Syllabus

Week 1

Basics of Estimation, What is a ML Estimator?

  • Basic definitions: sample, population, and sample mean, sample variance, population mean, population variance etc.
  • Probability distributions: Standard probability distributions, derivations of expected value and variance.
  • Estimation: A quick overview of basics of estimation theory (estimate, estimator etc.).
  • Properties of estimators (or requisites for a good estimator): consistency, unbiasedness (also cover concept of bias and minimum bias), efficiency, sufficiency and minimum variance.
  • Methods of estimation (definitions): method of moments (MOM), method of least squares (OLS) and maximum likelihood estimation (MLE).
  • Why MLE is preferred? MLE vs. other methods of estimation.
  • Pop quiz

Week 2

Properties and Applications of ML Estimators and Bonus Readings

  • MLE: properties
  • MLE: derivations
  • ML estimators don't always exist - examples.
  • In which standard methods are ML estimators used?
  • Use (or not) of ML estimators in linear regression.
  • Use of ML estimators in logistic regression. Should they be used?
  • Tests of hypotheses: tests based on the sampling distribution of the ML estimator
  • Pop quiz
  • Bonus reading material: further readings/references

Week 3

No instruction.

Week 4

No instruction.

Class Dates

2020

Apr 17, 2020 to May 1, 2020

2021

No classes scheduled at this time.

2022

No classes scheduled at this time.

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Prerequisites

Familiarity with Calculus is required.

The courses listed below are prerequisites for enrollment in this course:

Matrix Algebra

This course will teach you the basics of vector and matrix algebra and operations necessary to understand multivariate statistical methods, including the notions of the matrix inverse, generalized inverse and eigenvalues and eigenvectors.
Topic: Statistics, Statistical Modeling | Skill: Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Mar 27, 2020, Aug 14, 2020

What Our Students Say​

This was a very interesting course which almost everyone can benefit from taking.

Kathrin Sunde
Gentian Technology AS

I need to know R to perform my job as I am a product manager for a software company that interacts with R.  I am now able to understand R scripts and hopefully contribute some of my own. The instructor's videos were great. Just hearing his voice made it more personal. This was my first ever web based course and I really enjoyed it although I had to work hard.

Ana Henry
Certara

Frequently Asked Questions

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We offer a “Student Satisfaction Guarantee​” that includes a tuition-back guarantee, so go ahead and take our courses risk free. That’s our commitment to student satisfaction. Students may cancel, transfer, or withdraw from a course under certain conditions. If you’re not satisfied with a course, you may withdraw from the course and receive a tuition refund.

Please see our knowledge center for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

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  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

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Visit our knowledge base and learn more.

FAQs + Knowledge Base

Related Courses

Generalized Linear Models

This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis.
Topic: Statistics, Statistical Modeling | Skill: Intermediate, Advanced | Credit Options: CEU
Class Start Dates: Apr 10, 2020

Logistic Regression

This course will teach you logistic regression ordinary least squares (OLS) methods to model data with binary outcomes rather than directly estimating the value of the outcome, logistic regression allows you to estimate the probability of a success or failure.
Topic: Statistics, Statistical Modeling | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: Jul 17, 2020

Survival Analysis

This course will teach you the various methods used for modeling and evaluating survival data or time-to event data.
Topic: Statistics, Biostatistics, Statistical Modeling | Skill: Intermediate | Credit Options: ACE, CAP, CEU
Class Start Dates: Mar 6, 2020, Sep 11, 2020

Additional Course Information

Organization of Course

This course takes place online at The Institute for 2 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirements

This is a 2-week course requiring 10-15 hours per week of review and study, at times of your choosing.

Homework

Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.

Course Text

Course materials will be provided by the instructor.

Software

This course does not have a software requirement.

Access to one or more of these software packages- SAS, R, S-plus, Stata, Minitab, StatXact, LISREL, will enhance your experience with this course.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

There is no additional information for this course.

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Maximum Likelihood Estimation
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