close menu
Bookswagon-24x7 online bookstore
close menu
My Account
Home > Mathematics and Science Textbooks > Mathematics > Probability and statistics > Em and MM Algorithms for a Class of Left-Truncated Discrete Models: (English)
Em and MM Algorithms for a Class of Left-Truncated Discrete Models: (English)

Em and MM Algorithms for a Class of Left-Truncated Discrete Models: (English)

          
5
4
3
2
1

Out of Stock


Premium quality
Premium quality
Bookswagon upholds the quality by delivering untarnished books. Quality, services and satisfaction are everything for us!
Easy Return
Easy return
Not satisfied with this product! Keep it in original condition and packaging to avail easy return policy.
Certified product
Certified product
First impression is the last impression! Address the book’s certification page, ISBN, publisher’s name, copyright page and print quality.
Secure Checkout
Secure checkout
Security at its finest! Login, browse, purchase and pay, every step is safe and secured.
Money back guarantee
Money-back guarantee:
It’s all about customers! For any kind of bad experience with the product, get your actual amount back after returning the product.
On time delivery
On-time delivery
At your doorstep on time! Get this book delivered without any delay.
Notify me when this book is in stock
Add to Wishlist

About the Book

This dissertation, "EM and MM Algorithms for a Class of Left-truncated Discrete Models" by Xiaolin, Zheng, 郑晓琳, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Left-truncated count data often occur in various fields. Examples include working years of Chartered financial analyst certificate holders, numbers of quinsy of patients who had tonsillectomy, numbers of student enrollments for English course in the University of Windsor, numbers of research papers accepted to an SCI or SSCI journal of a student enrolled in the PhD Program in Applied Statistics in Feng Chia University and so on. A class of left-truncated discrete models such as left-truncated Poisson, left-truncated binomial, left-truncated negative binomial, left-truncated generalized Poisson distributions are proposed in the literature to model such count data. However, the estimates of the parameters in such distributions may be difficult to obtain since the original data set is left-truncated, and hence it is incomplete. For the case of no covariates, in this thesis, I first develop a novel expectation-maximization (EM) algorithm via the stochastic representation method for calculating the maximum likelihood estimates (MLEs) of parameters in the general left-truncated discrete distributions including the general zero-truncated discrete distributions as special cases. An important feature of the proposed EM algorithm is that the latent variables and the observed variables are independent, which is unusual in general EM-type algorithms. Next, I propose a unified minorization-maximization algorithm for obtaining the MLEs of parameters in these left-truncated discrete distributions, since their closed-form solutions are not available in the M-step of the EM algorithm for some distributions. In addition, Bayesian approaches are also developed for the left-truncated Poisson and the left-truncated binomial distributions, respectively. To incorporate the existence of covariates, I furthermore introduce the left-truncated Poisson regression model and the left-truncated binomial regression model, and utilize De Pierro's algorithm to derive the MLEs of the regression coefficients. The performances of all the proposed methods in this thesis are evaluated through simulation studies and three real data sets are analyzed to illustrate the proposed methods. Subjects: Distribution (Probability theory) Multivariate analysis


Best Sellers



Product Details
  • ISBN-13: 9781361041840
  • Publisher: Open Dissertation Press
  • Publisher Imprint: Open Dissertation Press
  • Height: 279 mm
  • No of Pages: 112
  • Spine Width: 6 mm
  • Width: 216 mm
  • ISBN-10: 1361041846
  • Publisher Date: 26 Jan 2017
  • Binding: Paperback
  • Language: English
  • Series Title: English
  • Weight: 277 gr


Similar Products

How would you rate your experience shopping for books on Bookswagon?

Add Photo
Add Photo

Customer Reviews

REVIEWS           
Click Here To Be The First to Review this Product
Em and MM Algorithms for a Class of Left-Truncated Discrete Models: (English)
Open Dissertation Press -
Em and MM Algorithms for a Class of Left-Truncated Discrete Models: (English)
Writing guidlines
We want to publish your review, so please:
  • keep your review on the product. Review's that defame author's character will be rejected.
  • Keep your review focused on the product.
  • Avoid writing about customer service. contact us instead if you have issue requiring immediate attention.
  • Refrain from mentioning competitors or the specific price you paid for the product.
  • Do not include any personally identifiable information, such as full names.

Em and MM Algorithms for a Class of Left-Truncated Discrete Models: (English)

Required fields are marked with *

Review Title*
Review
    Add Photo Add up to 6 photos
    Would you recommend this product to a friend?
    Tag this Book
    Read more
    Does your review contain spoilers?
    What type of reader best describes you?
    I agree to the terms & conditions
    You may receive emails regarding this submission. Any emails will include the ability to opt-out of future communications.

    CUSTOMER RATINGS AND REVIEWS AND QUESTIONS AND ANSWERS TERMS OF USE

    These Terms of Use govern your conduct associated with the Customer Ratings and Reviews and/or Questions and Answers service offered by Bookswagon (the "CRR Service").


    By submitting any content to Bookswagon, you guarantee that:
    • You are the sole author and owner of the intellectual property rights in the content;
    • All "moral rights" that you may have in such content have been voluntarily waived by you;
    • All content that you post is accurate;
    • You are at least 13 years old;
    • Use of the content you supply does not violate these Terms of Use and will not cause injury to any person or entity.
    You further agree that you may not submit any content:
    • That is known by you to be false, inaccurate or misleading;
    • That infringes any third party's copyright, patent, trademark, trade secret or other proprietary rights or rights of publicity or privacy;
    • That violates any law, statute, ordinance or regulation (including, but not limited to, those governing, consumer protection, unfair competition, anti-discrimination or false advertising);
    • That is, or may reasonably be considered to be, defamatory, libelous, hateful, racially or religiously biased or offensive, unlawfully threatening or unlawfully harassing to any individual, partnership or corporation;
    • For which you were compensated or granted any consideration by any unapproved third party;
    • That includes any information that references other websites, addresses, email addresses, contact information or phone numbers;
    • That contains any computer viruses, worms or other potentially damaging computer programs or files.
    You agree to indemnify and hold Bookswagon (and its officers, directors, agents, subsidiaries, joint ventures, employees and third-party service providers, including but not limited to Bazaarvoice, Inc.), harmless from all claims, demands, and damages (actual and consequential) of every kind and nature, known and unknown including reasonable attorneys' fees, arising out of a breach of your representations and warranties set forth above, or your violation of any law or the rights of a third party.


    For any content that you submit, you grant Bookswagon a perpetual, irrevocable, royalty-free, transferable right and license to use, copy, modify, delete in its entirety, adapt, publish, translate, create derivative works from and/or sell, transfer, and/or distribute such content and/or incorporate such content into any form, medium or technology throughout the world without compensation to you. Additionally,  Bookswagon may transfer or share any personal information that you submit with its third-party service providers, including but not limited to Bazaarvoice, Inc. in accordance with  Privacy Policy


    All content that you submit may be used at Bookswagon's sole discretion. Bookswagon reserves the right to change, condense, withhold publication, remove or delete any content on Bookswagon's website that Bookswagon deems, in its sole discretion, to violate the content guidelines or any other provision of these Terms of Use.  Bookswagon does not guarantee that you will have any recourse through Bookswagon to edit or delete any content you have submitted. Ratings and written comments are generally posted within two to four business days. However, Bookswagon reserves the right to remove or to refuse to post any submission to the extent authorized by law. You acknowledge that you, not Bookswagon, are responsible for the contents of your submission. None of the content that you submit shall be subject to any obligation of confidence on the part of Bookswagon, its agents, subsidiaries, affiliates, partners or third party service providers (including but not limited to Bazaarvoice, Inc.)and their respective directors, officers and employees.

    Accept

    New Arrivals



    Inspired by your browsing history


    Your review has been submitted!

    You've already reviewed this product!
    ASK VIDYA