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| STAT0727-2 | Non-parametric statistics
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| Duration : | 30h Th, 10h Pr, 20h Mon. WS |
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| Number of credits : |
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| Lecturer : | Cédric Heuchenne |
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Language(s) of instruction :
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| French language |
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Organisation and examination :
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| Teaching in the second semester |
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Course contents :
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| The first part of the course is devoted to different basic nonparametric tests. Among them, location and scale tests for one or two populations, goodness-of-fit tests to see if a distribution is close to a family of parametric distributions, tests of independence using association measures between two or several random variables will be developed. The second part of the course is devoted to nonparametric estimation of cumulative distribution, density and quantile functions. Especially, interest is focused on kernel methods and their crucial smoothing parameters choice. |
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Learning outcomes of the course :
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| At the end of the course, students will have understood and applied nonparametric inference basic concepts. They will manage some estimation methods as well as basic nonparametric tests. They will also be able to apply those nonparametric procedures in data analysis using some statistical softwares. |
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Prerequisites and co-requisites/ Recommended optional programme components :
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| Basic course in mathematical statistics. |
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Planned learning activities and teaching methods :
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| 30h exercises and sofware applications. |
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Mode of delivery (face-to-face ; distance-learning) :
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| The course is given in an 'ex cathedra' way during the second quadrimester. Courses and workshops are schedulded according to official timetables given to students in the beginning of the year. |
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Recommended or required readings :
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| Course material is made of chapters of books and exercises protected by authors' rights. Required knowledge is seen during the course and corresponding references are provided.
Advised books:
Bosq, D. and Lecoutre, J.P. (1987). Théorie de l'estimation fonctionnelle. Economica, Paris.
Gibbons, J.D. (1971). Nonparametric Statistical Inference. McGraw-Hill, NewYork.
Härdle, W. (1990). Applied Nonparametric Regression. Cambridge University Press, Cambridge.
Hollander, M. and Wolfe, D.A. (1999). Nonparametric Statistical Methods. Second Edition. Wiley, New York.
Maritz, J.S. (1995). Distribution-free Statistical Methods. Second Edition. Chapman and Hall, New York.
Randles, R. and Wolfe, D. (1979). Introduction to the Theory of Nonparametric Statistics. Wiley, New York.
Silverman, B.W. (1986). Density Estimation for Statistics and Data Analysis. Chapman and Hall, London.
Wand, M.P. and Jones, M.C. (1995). An introduction to kernel smoothing. Chapman and Hall, London. |
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Assessment methods and criteria :
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| The evaluation is divided into two parts: a computational work treating a "real life" problem with methods displayed during the theoretical lectures and a exam during May-June first session about the whole course. This exam covers a written part on exercises and an oral part on theory. |
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Work placement(s) :
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Organizational remarks :
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| Teaching language: French |
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Contacts :
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| Cédric HEUCHENNE, HEC-ULg Management School of the University of Liège, N1, local 309, tel: 04/366 27 20, email: C.Heuchenne@ulg.ac.be
Alessandro BERETTA, HEC-ULg Management School of the University of Liège, N1, local 310, email: A.Beretta@ulg.ac.be |
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