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<CourseUnit xmlns="http://www.manchester.ac.uk/CUICourseUnitDetails" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.manchester.ac.uk/CUICourseUnitDetails.xsd">
  <UnitCode Applicant="Y" Label="Unit code" Student="Y">
    <Code>MATH48201</Code>
  </UnitCode>
  <UnitTitle Applicant="Y" Label="Unit title" Student="Y">
    <Title>Statistical Theory</Title>
  </UnitTitle>
  <MaxUnits Applicant="Y" Label="Credit rating" Student="Y">
    <Units>15</Units>
  </MaxUnits>
  <TeachingPeriods Applicant="Y" Label="Teaching period(s)" Student="Y">
    <Period>Semester 1</Period>
  </TeachingPeriods>
  <AcademicCareer Applicant="Y" Label="Academic career" Student="Y">
    <Value>Undergraduate</Value>
  </AcademicCareer>
  <UnitLevel Applicant="Y" Label="Unit level" Student="Y">
    <Level>Level 4</Level>
  </UnitLevel>
  <StaffList Applicant="Y" Label="Teaching staff" RoleLabel="Course Unit Role" Student="Y">
    <StaffMember>
      <Name>Yang Han</Name>
      <Role>Unit coordinator</Role>
    </StaffMember>
  </StaffList>
  <OfferedBy Applicant="Y" Label="Offered by" Student="Y">
    <OrganisationList>
      <Organisation>
        <OrgName></OrgName>
      </Organisation>
    </OrganisationList>
    <GroupList>
      <Group>
        <GroupName></GroupName>
      </Group>
    </GroupList>
    <FheqLevels>
      <FheqLevel>
        <LevelNumber>1</LevelNumber>
        <LevelName>FHEQ level (Framework for Higher Education Qualifications) ' Masters/Integrated Masters P4 ' </LevelName>
      </FheqLevel>
    </FheqLevels>
    <Ects>
      <MaxUnits>European Credit Transfer &amp; Accumulation System Rating :   7.5</MaxUnits>
    </Ects>
  </OfferedBy>
  <MarketingOverview Applicant="Y" Label="Marketing Course unit overview" Student="">
    <Content>&lt;p&gt;This course aims to introduce students to the principles of estimation and hypothesis testing, and to familiarize them with the effective methods for estimation and constructing test procedures.&lt;/p&gt;</Content>
  </MarketingOverview>
  <UnitOverview Applicant="" Label="Course unit overview" Student="Y">
    <Content>&lt;p&gt;This course aims to introduce students to the principles of estimation and hypothesis testing, and to familiarize them with the effective methods for estimation and constructing test procedures.&amp;nbsp;&lt;/p&gt;</Content>
  </UnitOverview>
  <Aims Applicant="Y" Label="Aims" Student="Y">
    <Content>&lt;p&gt;This course aims to introduce students to the principles of estimation and hypothesis testing, and to familiarize them with the effective methods for estimation and constructing test procedures.&amp;nbsp;&lt;/p&gt;</Content>
  </Aims>
  <LearningOutcomes Applicant="Y" Label="Learning outcomes" Student="Y">
    <Content>&lt;p&gt;On the successful completion of the course, students will be able to: &amp;nbsp;&lt;/p&gt;&lt;ol&gt;&lt;li&gt;Explain the properties of exponential family and apply these concepts to practical examples. &amp;nbsp;&lt;/li&gt;&lt;li&gt;Formulate estimators using the maximum likelihood principle, and analyse their non-asymptotic and asymptotic properties, with application to the exponential family.&lt;/li&gt;&lt;li&gt;Construct likelihood-based confidence intervals for parameters, including their asymptotic forms, and perform hypothesis testing using generalised likelihood ratio tests and related methods.&lt;/li&gt;&lt;li&gt;Explain the key concepts of multiple testing, including FWER and FDR, and apply methods such as the Bonferroni correction and Benjamini-Hochberg procedure to control error rates.&lt;/li&gt;&lt;li&gt;Apply computational techniques to solve statistical inference problems. &amp;nbsp;&lt;/li&gt;&lt;li&gt;Apply advanced methods such as the EM algorithm and Kaplan-Meier estimator to address incomplete and censored data problems.&lt;/li&gt;&lt;/ol&gt;</Content>
  </LearningOutcomes>
  <Knowledge Applicant="Y" Label="Knowledge and understanding" Student="Y">
    <Content></Content>
  </Knowledge>
  <IntellectualSkills Applicant="Y" Label="Intellectual skills" Student="Y">
    <Content></Content>
  </IntellectualSkills>
  <PracticalSkills Applicant="Y" Label="Practical skills" Student="Y">
    <Content></Content>
  </PracticalSkills>
  <TransferableSkills Applicant="Y" Label="Transferable skills and personal qualities" Student="Y">
    <Content></Content>
  </TransferableSkills>
  <EmployabilitySkillsList Applicant="Y" Label="Employability skills" Student="Y">
    <Skill>
      <SkillId></SkillId>
      <SkillDescription></SkillDescription>
    </Skill>
  </EmployabilitySkillsList>
  <Syllabus Applicant="Y" Label="Syllabus" Student="Y">
    <Content>&lt;p&gt;Part A:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;Exponential Family: definition and examples, canonical parameters and statistics, dispersion parameter, cumulant functions&lt;/li&gt;&lt;li&gt;Maximum likelihood estimation (MLE): theoretical properties including asymptotics, restricted MLE, Fisher information, Cramer-Rao inequality, efficiency, most efficient estimators, sufficiency and minimal sufficiency, MLE for exponential family&lt;/li&gt;&lt;li&gt;Inference: likelihood-based confidence intervals, Wald test, (generalised) likelihood ratio test, asymptotic form of the generalised likelihood ratio test&lt;/li&gt;&lt;/ul&gt;&lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;p&gt;Part B:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;Multiple testing and simultaneous inference: it involves the control of various types of error rates, such as Familywise Error Rate (FWER), False Discovery Rate (FDR). Other topics include Bonferroni method, Benjamini-Hochberg procedure etc.&lt;/li&gt;&lt;li&gt;Computational inference: Bootstrap, Monte Carlo and bootstrap tests, Bootstrap confidence intervals, Kernel density estimation (KDE)&lt;/li&gt;&lt;li&gt;Incomplete data: EM algorithm, Kaplan-Meier estimator for censored survival data&amp;nbsp;&lt;/li&gt;&lt;/ul&gt;</Content>
  </Syllabus>
  <TeachingMethods Applicant="Y" Label="Teaching and learning methods" Student="Y">
    <Content>&lt;p&gt;Teaching is composed of two hours of lectures and one tutorial class per week. Teaching materials will be made available online for reference and review.&amp;nbsp;&lt;/p&gt;</Content>
  </TeachingMethods>
  <AssessmentMethods Applicant="Y" Label="Assessment methods" Student="Y">
    <IntroText> </IntroText>
    <Method>
      <MethodId>0</MethodId>
      <MethodName>Other</MethodName>
      <MethodWeight>20%</MethodWeight>
    </Method>
    <Method>
      <MethodId>1</MethodId>
      <MethodName>Written exam</MethodName>
      <MethodWeight>80%</MethodWeight>
    </Method>
    <OtherDescription>&lt;p&gt;Written exam - 80% weighting&lt;/p&gt;&lt;p&gt;Mid-term - 20% weighting&lt;/p&gt;</OtherDescription>
  </AssessmentMethods>
  <FeedbackMethods Applicant="Y" Label="Feedback methods" Student="Y">
    <Content>&lt;p&gt;Generic feedback will be provided after marks are released. &amp;nbsp;&lt;/p&gt;</Content>
  </FeedbackMethods>
  <RequirementsList Applicant="Y" Label="Pre/co-requisites" Student="Y">
    <Requirement>
      <UnitCode>MATH27720</UnitCode>
      <UnitTitle>Probability and Statistics 2</UnitTitle>
      <RequirementType>Pre-Requisite</RequirementType>
      <Description>Compulsory</Description>
    </Requirement>
    <Requirement>
      <UnitCode>MATH20802</UnitCode>
      <UnitTitle>Statistical Methods</UnitTitle>
      <RequirementType>Pre-Requisite</RequirementType>
      <Description>Compulsory</Description>
    </Requirement>
    <AdditionalRequirement>MATH48201 Pre-Requisites: MATH27720</AdditionalRequirement>
  </RequirementsList>
  <AcademicPrograms Applicant="Y" Label="Academic programmes" Student="Y">
    <AcademicProgram>
      <Program></Program>
      <Plan></Plan>
      <Level></Level>
      <Requirement></Requirement>
    </AcademicProgram>
  </AcademicPrograms>
  <FreeChoice Applicant="Y" Label="Available as a free choice unit?" Student="Y">
    <Content>N</Content>
  </FreeChoice>
  <Accreditation Applicant="Y" Label="Accreditation" Student="Y">
    <Content></Content>
  </Accreditation>
  <RecommendedReading Applicant="Y" Label="Recommended reading" Student="Y">
    <Content>&lt;p&gt;Casella, G., &amp;amp; Berger, R. L. (2002). Statistical Inference (2nd ed.). Duxbury Press. &amp;nbsp;&lt;/p&gt;&lt;p&gt;Garthwaite, P. H., Jolliffe, I. T., &amp;amp; Jones, B. (2002). Statistical Inference (2nd ed.). Oxford University Press.&lt;/p&gt;&lt;p&gt;Lauritzen, S. (2023). Fundamentals of mathematical statistics. Taylor Francis.&lt;/p&gt;&lt;p&gt;Abramovich, F. &amp;amp; Ritov, Y. (2023). Statistical theory: a concise introduction (2nd edition). Taylor Francis. &amp;nbsp;&lt;/p&gt;&lt;p&gt;Davison, A. C., &amp;amp; Hinkley, D. V. (1997). Bootstrap Methods and Their Application. Cambridge University Press.&lt;/p&gt;&lt;p&gt;Efron, B., &amp;amp; Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Chapman &amp;amp; Hall.&lt;/p&gt;&lt;p&gt;Hsu, J. (1996). Multiple Comparisons: Theory and Methods. Wiley.&lt;/p&gt;&lt;p&gt;Bretz, F., Hothorn, T., &amp;amp; Westfall, P. (2010). Multiple Comparisons Using R. Springer.&amp;nbsp;&lt;/p&gt;</Content>
  </RecommendedReading>
  <StudyHours Applicant="Y" Label="Study hours" Student="Y">
    <IntroText> </IntroText>
    <ScheduledHours Applicant="Y" Label="Scheduled activity hours" Student="Y">
      <ActivityHours>
        <ActivityType>Lectures</ActivityType>
        <Hours>22</Hours>
      </ActivityHours>
      <ActivityHours>
        <ActivityType>Tutorials</ActivityType>
        <Hours>11</Hours>
      </ActivityHours>
    </ScheduledHours>
    <PlacementHours Applicant="Y" Label="Placement hours" Student="Y">
      <ActivityHours>
        <ActivityType></ActivityType>
        <Hours>0</Hours>
      </ActivityHours>
    </PlacementHours>
    <TotalHours Applicant="Y" Label="Independent study hours" Student="Y">
      <Hours>117</Hours>
    </TotalHours>
  </StudyHours>
  <Notes Applicant="Y" Label="Additional notes" Student="Y">
    <Content></Content>
  </Notes>
</CourseUnit>
