Syllabus

Titel
4567 Y1P3 Statistics II
LV-Leiter/innen
Univ.Prof. Dr. Kurt Hornik
Kontakt
  • LV-Typ
    PI
  • Semesterstunden
    2
  • Unterrichtssprache
    Englisch
Anmeldung
06.02.2019 bis 24.02.2019
Anmeldung über LPIS
Hinweise zur LV
Planpunkt(e) Master
Termine
Wochentag Datum Uhrzeit Raum
Donnerstag 28.02.2019 09:00 - 13:00 D5.1.001
Donnerstag 07.03.2019 09:00 - 13:00 TC.4.03
Donnerstag 14.03.2019 09:00 - 13:00 D5.1.001
Donnerstag 28.03.2019 09:00 - 13:00 TC.4.03
Donnerstag 04.04.2019 09:00 - 13:00 D5.1.001
Donnerstag 11.04.2019 09:30 - 11:30 TC.0.04
Freitag 12.04.2019 12:00 - 16:00 D5.0.002

Inhalte der LV

  • Limit Theorems
  • Estimation of Parameters and Fitting of Probability Distributions
  • Testing Hypotheses and Assessing Goodness of Fit
  • Comparing Two Samples
  • The Analysis of Categorical Data

Lernergebnisse (Learning Outcomes)

After completing this course the student will have the ability to:

  • describe and apply the key methods of statistical inference;
  • solve fundamental statistical inference problems both theoretically and empirically.
    Apart from that, the course will contribute to the ability to:
      • demonstrate effective team skills in order to contribute appropriately to the production of a group output;
      • work, communicate and participate effectively in a team situation and group discussions and to function as a valuable and cooperative team member.

      Moreover, after completing this course the student will have the ability to:

      • adequately communicate the results of fitting statistical models to data;
      • discuss empirical findings in the light of domain knowledge.

      In addition, the student will be able to:

      • use R to perform statistical inference.

      Regelung zur Anwesenheit

      Full attendance is compulsory. This means that students should attend at least 80% of all lectures, at most one lecture can be missed.

      Lehr-/Lerndesign

      The course is taught as a lecture combined with homework assignments and a course project. 

      In combination with the lecture, the homework assignments will help students to consolidate and expand their knowledge and understanding by developing solutions to theoretical and applied problems, and have to be submitted every week via email to the lecturer. Solutions will be discussed in class at the beginning of each unit. 

      For the course projects, teams with up to four members will cooperate in solving statistical inference problems using a mix of analytical and numerical computations, and present their results for one such project.

      Leistung(en) für eine Beurteilung

      • 30% home assignments and group discussions
      • 30% course project
      • 40% written final exam

      The assessment of the homework assignments and course projects will be based on the correctness of results, the clarity and persuasiveness of each bit of work and the recognizable effort made. This implies an ability to work in teams. 

      For the written exam, the assessment will be based on the ability to describe and apply the key concepts discussed throughout the course and to choose the appropriate analytical techniques to obtain the relevant data.

      To avoid the potential free-rider problem related to group work, the final exam will strongly be related to the problems already discussed in homework assignments and course projects.

      Please note that there will be no opportunity to retake the written final exam.

      Students need to get 50% of the overall marks to pass.

      Teilnahmevoraussetzung(en) und Vergabe von Wartelistenplätzen

      Successful completion of Mathematics I and Financial Markets and Instruments

      Literatur

      1 Autor/in: John A. Rice
      Titel: Mathematical Statistics and Data Analysis 

      Verlag: Thomson Brooks/Cole
      Auflage: 3rd edition
      Anmerkungen: ISBN-10: 0495118680, ISBN-13: 9780495118688
      Jahr: 2006
      Prüfungsstoff: Ja
      Empfehlung: Unbedingt notwendige Studienliteratur für alle Studierenden
      2 Autor/in: Felix Abramovich, Ya'acov Ritov
      Titel:

      Statistical Theory: A Concise Introduction

      URL: http://www.crcpress.com/product/isbn/9781439851845


      Verlag: Chapman and Hall/CRC
      Anmerkungen: ISBN 9781439851845
      Jahr: 2013
      Prüfungsstoff: Ja
      Empfehlung: Unbedingt notwendige Studienliteratur für alle Studierenden

      Empfohlene inhaltliche Vorkenntnisse

      • Advanced Business Mathematics (see the class Mathematics I of the QFin program)
      • Advanced Business Probability theory (see the class Probability of the QFin program)
      • Basic Statistical Computing (see the class Statistics I of the QFin program)

      Erreichbarkeit des/der Vortragenden

      kurt.hornik@wu.ac.at

      Detailinformationen zu einzelnen Lehrveranstaltungseinheiten

      Einheit Datum Inhalte
      1

      Limit Theorems; Basic Framework of Statistical Inference

      After attending this session students should recall the basic concepts for and results about convergence of sequences of random variables, and the understand the basic framework of statistical inference.

      Reference: Rice, Chapters 5 and 8.1-8.3.

      2

      Estimation of Parameters and Fitting of Probability Distributions

      After attending this session students should understand the principles of fitting (families of) probability laws to data, in particular for situations where the family depends on a small number of parameters
      which are estimated from the data, as well as the finite-sample and asymptotic properties of these parameter estimates.

      Reference: Rice, Chapter 8.4-8.8.

      3

      Testing Hypotheses and Assessing Goodness of Fit

      After attending this session students should be able to recall the Neyman-Pearson paradigm for statistical hypothesis testing, and the principles of assessing the goodness of fit of probabilistic models to data.

      Reference: Rice, Chapter 9.

      4

      Comparing Two Samples

      After attending this session students should be familiar with methods for comparing samples from (continuous) distributions that may be different, in particular methods for making inferences about how the distributions differ.

      Reference: Rice, Chapter 11.

      5

      Analysis of Categorical Data

      After attending this session students should be able to remember statistical inference techniques for categorical data and contingency tables, including tests for independence, homogeneity, and symmetry.

      Reference: Rice, Chapter 13.

      6

      Presentations and Review

      After attending this session students should recall developing, presenting and discussing the results of using a mix of analytical and numerical computations to solve statistical inference problems.
      They should also assess their efficiency for self and group organization and reflect upon the "big picture" of this course.

      7 Final exam.
      Zuletzt bearbeitet: 07.11.2018



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