040639 UK Exact Tests not only for Experimental Economics (MA) (2013W)
Prüfungsimmanente Lehrveranstaltung
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- Anmeldung von Fr 06.09.2013 09:00 bis Fr 20.09.2013 14:00
- Anmeldung von Mi 25.09.2013 09:00 bis Do 26.09.2013 17:00
- Abmeldung bis Mo 14.10.2013 23:59
Details
max. 50 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Montag
07.10.
12:00 - 14:00
Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag
14.10.
12:00 - 14:00
Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag
21.10.
12:00 - 14:00
Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag
28.10.
12:00 - 14:00
Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag
04.11.
12:00 - 14:00
Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag
11.11.
12:00 - 14:00
Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag
18.11.
12:00 - 14:00
Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag
25.11.
12:00 - 14:00
Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag
02.12.
12:00 - 14:00
Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag
09.12.
12:00 - 14:00
Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag
16.12.
12:00 - 14:00
Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag
13.01.
12:00 - 14:00
Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Montag
20.01.
12:00 - 14:00
Hörsaal 12 Oskar-Morgenstern-Platz 1 2.Stock
Dienstag
28.01.
14:00 - 16:00
Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Stock
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
The grade is made up of a) a midterm, b) a final and c) homeworks that involve finding
data sets, and analyzing data sets. Each of these three parts will be separately graded and
counts equally towards the final grade.
Prerequisites: knowledge of statistics at an undergraduate level.
data sets, and analyzing data sets. Each of these three parts will be separately graded and
counts equally towards the final grade.
Prerequisites: knowledge of statistics at an undergraduate level.
Mindestanforderungen und Beurteilungsmaßstab
In this course we will give an overview and understand of existing and new methods for
testing hypotheses and running regressions that are exact. One goal of this course is to teach
students how to use R in order to analyze data sets. Laptops will be used in class to
demonstrate methods. Students will learn how to analyze data sets and how to read and
understand empirical papers.Who is this course for? Anyone who is curious and
who is genuinely interested in uncovering what is hidden in the data and who is interested
in making mathematically sound claims. Of course many applications cannot be dealt (yet)
with an exact method as often there is too much going on. However this course will
demonstrate that there are lots of relevant areas where one can make exact statements,
including running linear regressions.
testing hypotheses and running regressions that are exact. One goal of this course is to teach
students how to use R in order to analyze data sets. Laptops will be used in class to
demonstrate methods. Students will learn how to analyze data sets and how to read and
understand empirical papers.Who is this course for? Anyone who is curious and
who is genuinely interested in uncovering what is hidden in the data and who is interested
in making mathematically sound claims. Of course many applications cannot be dealt (yet)
with an exact method as often there is too much going on. However this course will
demonstrate that there are lots of relevant areas where one can make exact statements,
including running linear regressions.
Prüfungsstoff
Statistics is a science about how to analyze data. Classical statistical methods often, in fact
most statistical methods typically make claims about data sets that are not in accordance
with the underlying theory and methodology. This is because they make claims about
significance that are based on assuming that the data is infinitely large (they are based on
asymptotic theory). Remember that typically we do not think that the data is normally
distributed, but that is approximately and we will talk about why this sort of approximation
is not what one needs.
most statistical methods typically make claims about data sets that are not in accordance
with the underlying theory and methodology. This is because they make claims about
significance that are based on assuming that the data is infinitely large (they are based on
asymptotic theory). Remember that typically we do not think that the data is normally
distributed, but that is approximately and we will talk about why this sort of approximation
is not what one needs.
Literatur
Overview of Material:
Basics: Concepts: Null and alternative hypothesis, type I and II error, level, size, power, pvalue,
confidence interval. Data types: Single sample, independent samples, matched pairs,
Discussion of the usefulness of normality for large data sets.
Existing tests:
Binomial test with and without assuming identically distributed data, sign test, confidence
interval for the median and other quantiles, confidence interval of distributions,
permutation tests including Wilcoxon Mann Whitney and spearman rank correlation test.
New tests:
Test for the mean of a single sample, for comparing means given two independent samples
and for matched pairs, for the variance of a single sample and for comparing variances and
for analyzing covariance, for investigating tendencies described by a stochastic inequality,
and for running linear and ordinal regressions.
Reading material
Motulsky, Harvey (1995) "Intuitive Biostatistics," Oxford: Oxford Univ. Press.
- a bit vague but precise
Lehmann and Romano (2005) Lehmann, E. L. and Romano, J. P. (2005), Testing Statistical
Hypotheses. New York: Springer.
- very precise but too mathematical for the applied
Schlag (2013): Exact Hypothesis Testing without Assumptions - New and Old Results
not only for Experimental Game Theory
http://homepage.univie.ac.at/karl.schlag/research/statistics/exacthypothesistesting.pdf
and then there are original papers that some lectures will be based on:
Gossner and Schlag (2013): Finite-sample exact tests for linear regressions with bounded
dependent variables, Journal of Econometrics 177, 75-84.
Hoeffding, W. (1956), "On the distribution of the number of successes in independent
trials." The Annals of Mathematical Statistics, 27, 713-721.
Massart, P. (1990), "The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality," The
Annals of Probability 18, 1269-1283.
Basics: Concepts: Null and alternative hypothesis, type I and II error, level, size, power, pvalue,
confidence interval. Data types: Single sample, independent samples, matched pairs,
Discussion of the usefulness of normality for large data sets.
Existing tests:
Binomial test with and without assuming identically distributed data, sign test, confidence
interval for the median and other quantiles, confidence interval of distributions,
permutation tests including Wilcoxon Mann Whitney and spearman rank correlation test.
New tests:
Test for the mean of a single sample, for comparing means given two independent samples
and for matched pairs, for the variance of a single sample and for comparing variances and
for analyzing covariance, for investigating tendencies described by a stochastic inequality,
and for running linear and ordinal regressions.
Reading material
Motulsky, Harvey (1995) "Intuitive Biostatistics," Oxford: Oxford Univ. Press.
- a bit vague but precise
Lehmann and Romano (2005) Lehmann, E. L. and Romano, J. P. (2005), Testing Statistical
Hypotheses. New York: Springer.
- very precise but too mathematical for the applied
Schlag (2013): Exact Hypothesis Testing without Assumptions - New and Old Results
not only for Experimental Game Theory
http://homepage.univie.ac.at/karl.schlag/research/statistics/exacthypothesistesting.pdf
and then there are original papers that some lectures will be based on:
Gossner and Schlag (2013): Finite-sample exact tests for linear regressions with bounded
dependent variables, Journal of Econometrics 177, 75-84.
Hoeffding, W. (1956), "On the distribution of the number of successes in independent
trials." The Annals of Mathematical Statistics, 27, 713-721.
Massart, P. (1990), "The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality," The
Annals of Probability 18, 1269-1283.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Mo 07.09.2020 15:29
theory and understanding which approaches do what they say they do. Exact testing refers
to methods do exactly this, they have properties that can be formally proven. Claims that
are not based on a handful of simulations when the underlying set of possible data
generating processes is so rich that one can never simulate many.