Università degli Studi di Napoli "Parthenope"

  1. Teaching
  2. QUANTITATIVE METHODS FOR ECONOMIC AND FINANCIAL EVALUATIONS
  3. STATISTICS FOR INSURANCE

Stampa

STATISTICS FOR INSURANCE

Teaching schedule

Academic year: 
2017/2018
Belonging course: 
Course of Master's Degree Programme on QUANTITATIVE METHODS FOR ECONOMIC AND FINANCIAL EVALUATIONS
Disciplinary sector: 
ECONOMIC STATISTICS (SECS-S/03)
Language: 
Italian
Credits: 
9
Year of study: 
2
Teachers: 
Dott.ssa D'AGOSTINO ANTONELLA
Cycle: 
Second semester
Hours of front activity: 
72

Language

Italian

Course description

The module aims to develop abilities for using statistical models for the analysis of insurance data. During the module, the statistical methodology will be complemented by effective applications on real data using the R software. Students attending the course will achieve the required conceptual knowledge in order to implement useful statistical tools for strategic and operational decision-making on insurance data.

Prerequisites

General knowledge and reasoning ability. Descriptive and inferential statistics. Some basic knowledge of simple and multiple linear regression if previously acquired by the student, can facilitate the learning process; this is basically covered by the first-level courses offered by the University “Parthenope” in the economic area. For students coming from different first-level degree programs, an integration including a relevant bibliographic reference will be provided.

Syllabus

Module I:
Introduction to R. Data Entry Issues. Graphic Functions. Random variables. Descriptive and inferential statistics with R. Statistical modelling for insurance data (24 hours)
Module II:
The module introduces statistics methodologies to be used in quantitative research for studying insurance data. The following topics will be discussed. Introduction to insurance data. Discrete and continuous random variables. Exponential family responses and estimation. Categorical responses. Introduction to maximum likelihood estimation and boostrap techniques (48 hours).

Teaching Methods

Lessons are organized into two modules. The first module provides the basic knowledge for using R software. The second module is dedicated to the theoretical discussion of statistical tools useful for analysing insurance data. Slides and R scripts used at lesson are made available through the e-learning online platform Moodle.

Textbooks

P. DE JONG-G. Z. HELLER, Generalized Linear Models for Insurance Data, Cambridge University Press, 2008

J H Maindonald, Using R for Data Analysis and Graphics, Introduction, Code and Commentary J H Maindonald
Centre for Mathematics and Its Applications, Australian National University, 2008. Downlowed from the .

SLIDES PROVIDED BY THE THEACHER.

Learning assessment

The assessment is based on a written examination aiming to evaluate the capacity of student to manage empirical data and an oral examination that establishes the student’s knowledge on statistical methodology. The classwork includes an exercise to be solved using R software. The score is expressed in scale from 0 to 30. Final mark is computed as weighted average between the score of the written (3 CFU) and oral test (6 CFU). The laude can be assigned is the student shows, in his/her answers, a particular ability to deepen the topics mentioned in the examination’s questions. The duration of the written test is 1 hour. During the test, the use of R function notes is allowed.

More information

Expected learning outcomes:
Knowledge and understanding: the student should be able to understand the statistical methodology developed and should also be able to choose the best statistical tools for analyzing insurance data.
Applying knowledge and understanding: the student should be able to apply the acquired theoretical knowledge to empirical data using R software.
Making judgements: the student should be able to use the acquired knowledge also in an autonomous way, by also applying them to specific issues and problems concerning the quantitative analysis of empirical data.
Communication: the student should be able to answer in a clear and thorough way to the questions of oral examination and to solve the proposed exercise of written examination.
Lifelong learning skills: the student should be able to show a good learning ability of statistical methodology and to accomplish autonomous capacity in the study area.