Modelos Não Paramétricos

Code:

M4138

Acronym:

M4138

Keywords
Classification	Keyword
CNAEF	Mathematics and statistics

Instance: 2021/2022 - 1S (edição n.º 1)

Active?	Yes
Responsible unit:	Department of Mathematics
Course/CS Responsible:	Computational Statistical Modelling

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
E:MEC	12	PE_Computational Statistical Modelling	1	-	3	21	81

Teaching language

Portuguese

Objectives

Acquire a solid knowledge in inductive statistics and develop capacities and skills in statistical modelling techniques, fundamental to the presentation, analysis and interpretation of data sets.

Learning outcomes and competences

Upon completing this course, the student should: - understand the procedures for the non-parametric estimation of a distribution; - know and know how to apply adjustment tests; - know and know how to apply non-parametric techniques to situations of statistical inference in which the assumption of a parametric model is not adequate. - be able to use the R programming language in the application of non-parametric procedures. This unit complements and develops other Statistics units, providing knowledge and training in identifying and solving various statistical problems where non-parametric approaches are appropriate.

Working method

Presencial

Program

Statistical inference in a non-parametric context. Order statistics. Orders vector. Function of quantiles. Goodness-of-fit tests. Tests based on the vector of orders. Association measures and related hypotheses testing procedures.

Mandatory literature

Jean Dickinson Gibbons; Nonparametric statistical inference
John A. Rice; Mathematical statistics and data analysis. ISBN: 9780495118688
L. Wasserman; All of Nonparametric Statistics, Springer, 2006

Teaching methods and learning activities

TP lectures with exposition of the contents of the program, resolution and discussion of exercises and related problems.

The discussion of the works is open, all students are encouraged to participate.

All resources will be made available to the students.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Exame	40,00
Trabalho prático ou de projeto	60,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	50,00
Frequência das aulas	21,00
Trabalho escrito	10,00
Total:	81,00

Eligibility for exams

Practical assignments/ Project submitted within the fixed  schedules.

Calculation formula of final grade

The evaluation comprehends two components: project (60%) and final exam (40%). A minimum rating of 30% is required in each of the evaluation components.