Multiscale statistical classification

PGR-P-248

Coronavirus information for applicants and offer holders

We hope that by the time you’re ready to start your studies with us the situation with COVID-19 will have eased. However, please be aware, we will continue to review our courses and other elements of the student experience in response to COVID-19 and we may need to adapt our provision to ensure students remain safe. For the most up-to-date information on COVID-19, regularly visit our website, which we will continue to update as the situation changes www.leeds.ac.uk/covid19faqs

Key facts

Type of research degree: PhD
Application deadline: Ongoing deadline
Country eligibility: International (open to all nationalities, including the UK)
Funding: Competition funded
Supervisors: Dr Stuart Barber
Schools: School of Mathematics
Research groups/institutes: Statistics

<h2 class="heading hide-accessible">Summary</h2>

Statistical classification of objects into groups can be either supervised (where we know which group some observations fall in to) or unsupervised (where we don't know what groups exist). Examples of supervised classification are discriminant analysis and classification trees; unsupervised classification includes cluster analysis.<br /> <br /> These tools are widespread, especially with the advent of modern 'big data' applications such as genetic databases and market segmentation. Several of these methods are based on estimating the densities of underlying distributions; others use tree-like data structures. Both of these approaches can be thought of in a multiscale way, where we want to look at both local details and the 'big picture' simultaneously but separately. Useful tools for multiscale analysis are wavelets and the more general lifting algorithm.<br /> <br /> This project will investigate the application of wavelets and lifting to statistical classification problems, identifying when and how these approaches can be used and assessing their strengths and weaknesses compared to more traditional methods.

<h2 class="heading">How to apply</h2>

<p>Formal applications for research degree study should be made online through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University's website</a>. Please state clearly in the Planned Course of Study section that yo are applying for <em><strong>PHD Statistics</strong></em> and in the research information section that the research degree you wish to be considered for is <em><strong>Multiscale statistical classification</strong></em> as well as <a href="https://physicalsciences.leeds.ac.uk/staff/5/dr-stuart-barber">Dr Stuart Barber</a> as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).</p> <p> </p>

<h2 class="heading heading--sm">Entry requirements</h2>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

<h2 class="heading heading--sm">English language requirements</h2>

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

<h2 class="heading">Funding on offer</h2>

<p><strong>Self-Funded or externally sponsored students are welcome to apply.</strong></p> <p><strong>UK </strong>– The <a href="https://phd.leeds.ac.uk/funding/209-leeds-doctoral-scholarships-2022">Leeds Doctoral Scholarships</a>, <a href="https://phd.leeds.ac.uk/funding/198-akroyd-and-brown-scholarship-2022">Akroyd & Brown</a>, <a href="https://phd.leeds.ac.uk/funding/199-frank-parkinson-scholarship-2022">Frank Parkinson</a> and <a href="https://phd.leeds.ac.uk/funding/204-boothman-reynolds-and-smithells-scholarship-2022">Boothman, Reynolds & Smithells</a> Scholarships are available to UK applicants.  <a href="https://phd.leeds.ac.uk/funding/60-alumni-bursary">Alumni Bursary</a> is available to graduates of the University of Leeds.</p> <p><strong>Non-UK </strong>– The <a href="https://phd.leeds.ac.uk/funding/48-china-scholarship-council-university-of-leeds-scholarships-2021">China Scholarship Council - University of Leeds Scholarship</a> is available to nationals of China. The <a href="https://phd.leeds.ac.uk/funding/73-leeds-marshall-scholarship">Leeds Marshall Scholarship</a> is available to support US citizens.  <a href="https://phd.leeds.ac.uk/funding/60-alumni-bursary">Alumni Bursary</a> is available to graduates of the University of Leeds.</p>

<h2 class="heading">Contact details</h2>

<p>For further information regarding your application, please contact Doctoral College Admissions by email: <a href="mailto:maps.pgr.admissions@leeds.ac.uk">maps.pgr.admissions@leeds.ac.uk</a>, or by telephone: +44 (0)113 343 5057.</p> <p>For further information regarding the project, please contact Dr Stuart Barber by email:  <a href="mailto:S.barber@leeds.ac.uk">S.barber@leeds.ac.uk</a></p>

<h3 class="heading heading--sm">Linked research areas</h3>